OptimalThinkingBench: Evaluating Over and Underthinking in LLMs
Authors: Pranjal Aggarwal
Seungone Kim
Jack Lanchantin
Sean Welleck
Jason E Weston
Ilia Kulikov
Swarnadeep Saha
Venue: ICLR 2026 Poster (2026)
Keywords: reasoning llms
overthinking
underthinking
evaluation
benchmark
Primary area: foundation or frontier models, including LLMs
TL;DR: We propose OptimalThinkingBench, which jointly evaluates overthinking and underthinking in LLMs. We propose a unified metric to track progress and extensively evaluate existing models and efficiency methods, finding none achieve optimal thinking.

Abstract: Thinking LLMs solve complex tasks at the expense of increased compute and overthinking on simpler problems, while non-thinking LLMs are faster and cheaper but underthink on harder reasoning problems. This has led to the development of separate thinking and non-thinking LLM variants, leaving the onus of selecting the optimal model for each query on the end user. In this work, we introduce OptimalThinkingBench, a unified benchmark that jointly evaluates overthinking and underthinking in LLMs and also encourages the development of optimally-thinking models that balance performance and efficiency. Our benchmark comprises two sub-benchmarks: OverthinkingBench, featuring simple general queries in 72 domains along with simple math problems, and UnderthinkingBench, containing 11 challenging reasoning tasks along with tough math problems. Using novel thinking-adjusted accuracy metrics, we perform an extensive evaluation of 33 different thinking and non-thinking models and show that no model is able to optimally think on our benchmark. Thinking models often overthink for hundreds of tokens on the simplest user queries without improving performance. In contrast, large non-thinking models ``underthink'', often falling short of much smaller thinking models. We further explore several methods to encourage optimal thinking, but find that these approaches often improve on one sub-benchmark at the expense of the other, highlighting the need for better unified and optimal models in the future.

Published as a conference paper at ICLR 2026
OPTIMALTHINKINGENCCH EVALUATING OVER AND UNDERTHINKING IN LLMs
Pranjal Aggarwal*
Seungone Kim FAIR at Meta & Carnegie Mellon University
FAIR at Meta & Carnegie Mellon University
Jack Lanchantin FAIR Meta
Sean Welleck
Jason Weston FAIR Meta
Ili Kulikov
Camegie Mellon University
FAIR at Meta
Swarnadeep Saha" FAIR at Meta
ABSTRACT
Thinking LLMs solve complex tasks the expense of increased compute and overthinking on simpler problems, while non-thinking LLMs are faster and cheaper but underthink on harder reasoning problems. This has led to the de- velopment of separate thinking and non-thinking LLM variants, leaving the onus selecting the optimal model for each query on the end user. We introduce Optimal meeimmaThhinii Tetaahhiiniiiiiiii Wininkinngench.. thinking and underthinking in LLMs and also encourages the development optimally- thinnknn models that balance performance and efficiency. Our bench- mark comprises two sub-benchharks: Ove Overth weerhhinnke wethiinnnnngian ngBennh, inginnnigBenchh featuring simple math and general queries in 72 domains, and Underthi nk mkinggench, contain- ing 1l challenging reasoning tasks along with harder math problems. Using novel thinking-adjusted accuracy metrics, we extensively evaluate 33 different think- ing and non-thinking models and show that no model is able to optimally think on our benchmark. Thinking models often overthink for hundreds of tokens on the simplest user queries without improving performance. In contrast, large non- thinking models underthink, often falling short of much smaller thinking models. We further explore several methods to encourage optimal thinking, but find that these approaches often improve on one bub-benchmark at the expense of the other, highlighting the need for better unified and optimal models in the future.
unified benchmark that jointly evaluates
INTRODUCTION
Users query LLMs across a spectrum of tasks from factual queries to code and math proofs, so a useful LLM should answer easy questions quickly while spending more time on harder ones for better accuracy. In the past, LLMs have performed well on casy problems but have underthought on complex reasoning problems that required step-by-step thinking (Wei et al., 2022). In contrast, recent "thinking" LLMs (DeepSeek-AI et al., 2025; OpenAl et al., 2024) markedly improved the al., 2025; Aggarwal & Welleck, 2025) but at the cost of overthinking on performance (Cuadron et al., 2025;
latter (Muennighoff simple tasks, harming latency, cost, and in some cases Chen et al., 2025a: Gema et al., 2025; Liu et al., 2025). Consequently, many state-of-the-art LLMS have separate thinking and non-thinking variants, forcing end-users manually decide which model is best an unrealistic requirement for optimal uccuracy-ffiecinns trade-off at scale. To encourage the development of such optimally-thinking models that balance cost and performance,
"Equal contribution. Correspondence: pranjal204 @egmail.cc. 'Correspondence: swa swarneeep@eee. com 'Code is released at: https://gge httpss:///ithul http:///cw...eooooooe////////eee projects/ott /otb. Data released at: https: /huggi otg. think miinkiggeechh

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Published as a conference paper at ICLR 2026
Q: Which layer in Almosphere protects from UV raciation?
Q: Find the shortest path the following maze:
Overthinking Bench
Underthinking Bench
glvnn
Y06x XX02
and Let's perform down right resch final paits So this the fins anower:
right
saking
earn's etmosphere,
The Brewer incorrect, aaouud have asked a thinking model instead!
So ozona layor stops ur ne atton... But walt onn läver is stratecahore However, st shoopphhr as whole cocon't protect from the radiation So disstosphen pree is owert But no.
7.
3
OprimalThinkinggennch
1000 thinking takens later
Dvertinking
Underthinlnng Blench
Oodmmamiining
The frri answer is Stratosphere
(X Incorrect Answer
Thirking Models
X
X
long answer correctly. shoulc have 8akeo B
Nonitnining Model
X
X
non-thinking model inseoad!
Figure I: Opt Tottimaihhnnniggeenccc A unified benchmark to evaluate overthinking and under- thinking in LLMs. Overth nk ingnngenckhhhc ngBench consists of simpler queries where excessive thinking either does not improve or occasionally degrades performance. Underthing ingBench consists of reasoning problems where lack of thinking hurts performance.
we introduce a new benchmark called OptimalThinkingeerr new sub-benchmarks: Overthing nk ngBench and Underthink n that allows to evaluate and develop methods for optimal reasoning across a wide variety of domains.
is a combination of two
To first address the challenge of overthinking by thinking models, a benchmark containing simple queries where non-thinking models achieve high accuracy but thinking models yield similar or even lower scores despite generating hundreds of thinking tokens. We synthetically construct OverthinkingBenne filtering that ensures difficulty control, disambiguation, and answer correctness. It consists of both general and mathematical questions across more than 72 domains with four distinct answer types. wettcchiiknnggg innnchh, consisting of challenging reasoning tasks from different domains (games, algorithms, graphs, arithmetic, geometry, and logic) (Stojanovski et al., 2025), along with 2 competition math benchmarks. for certain questions, no matter how large non-thinking model is, its performance reasoning tasks will be lower than that of a much smaller yer thinking model. Taken together, the synthetic components of both uub-benchmark. allow each to remain dynamic, ensuring that the data generation recipe can be used to prevent benchmark contamination and evolve with increasing model competence. See Figure 1 for example queries from benchmark.
introduce
OverthinkingBent
with automated
We then introduce Underthi nk
is constructed based on the principle that complex
To track progress on Overth nk wwwchiikinngge ingBechh, we first propose (OAA), a metric that computes sample correctness below a certain thinking budget. We use this metric to calculate AUCOAA. an overthinking measure computing the area under the OAA curve to account for range of such thinking budgets. Our final metric for Opt www.aaahiiiiiaammm the Fi score between the overthinking AUCOAA and the underthinking accuracy.
Beerthinkin-AAjjuseee Accuracy
is
We perform comprehensive evaluations with 33 different models show that current thinking models overthink even simple queries without improving performance, leading to a substantial drop in user experience and increasing cost. Non-thinking models, on the other hand, underthink on difficult reasoning problems. Notably, no single model can optimally halance accuracy and efficiency on our benchmark, highlighting the importance of our benchmark. Finally, we explore various training-time as well test-time approaches to optimal thinking that rely reward shaping, routers, or deliberate prompting. We also analyze both qualitatively and quantitatively how models overthink and underthink across different domains and answer types. Our results indicate that while some of these methods prove to be more effective than others, significant gap persists, which motivates the need for better optimally-thinking L.MMs in the future.
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In summary, contributions three-fold: First, we develop
single unified benchmark to simultaneously track the progress of petimally-thinnkin LLMs for both performance and efficiency. Second, through comprchensive evaluations of 33 different thinking and non-thinking L.LMs, we show that state-of-the-art models struggle to optimally balance accuracy and efficieney, leaving large gap for improvement in future work. Third, we explore and compare several methods to encourage optimal thinking. Our results show that, while some approaches are promising, there still exists a significant trade-off between efficient and performant LLMs.
2 OPTIMAL THINKING BENCHMARK
aptimmlll 1 Thin ngBncch consists of two complementary benchmarks designed to evaluate the full spectrum of LLMs' thinking behavior. While wwerthinkiggBenee measures excessive computation on simple queries, Underthr weterrthininngBnchhh quantifies insufficient reasoning on com- plex tasks. Together, they provide unified framework for assessing whether models can adaptively balance computational cost with task complexity while maintaining accuracy.
2.1 OVERTHINKINGBENCH
OverthhinkingBench consists of two subsets: OvT-Math, focusing on simple math problems and OvT-General, consisting of general queries across diverse domains and answer types. In particular, for Ovt-Math, we use Level and 2 problems from the MATH dataset (Hendrycks et al., 2021). For OvT OvT-General, we employ two-stage pipeline consisting of Constrained Dataset Generation followed by Dataset Filtering, as illustrated in Figure 5, We follow a fully synthetic dataset creation recipe to ensure that everthinkingBent difficulty adjusted without human intervention, keeping pace with the rapid progress of LLMs.
can also be easily extended and/or
Constrained Dataset Generation. Creating a benchmark that covers wide set of queries, in line with real-world query distributions, requires diversity. Naively prompting an LLM would primarily produce degenerate questions that may fail to capture the breadth of user queries (Shypula et al., 2025). To address this, we use a constrained question generation setup: given pair of constraints = [D] where represents a specific domain and an answer type, we prompt an LLM L to generate n question-answer pairs: L(C) + { a,) j== where each pair (gi, (g),a;) satisfies the spec- ified constraints. We source 72 domains, D, that span science (e.g., Mechanics, Quantum Physics), general knowledge (e.g., Global Facts) from SuperGPQA (Du al., 2025). Our answer types. T. in- clude four categories that ensure diverse response formats: (a) numeric answers, (b) multiple-choice questions (MCQ), (c) one-word short phrase responses, and (d) open-ended answers.
This approach offers several advantages. First, ensures coverage across domains and answer types. Second, the modular constraints enable systematic ablation studies understand how overthinking varies with specific domains or answer formats. Third, the generation recipe provides defense against benchmark contamination, since new questions be generated while maintaining the same properties. In our analysis, we also vary the number of options in MCQs from 4 to 12, allowing us investigate how distractors affect thinking behavior. The prompt templates are in Appendix C.
Dataset Filtering. Synthetically generated benchmarks require validating answer correctness and ensuring both question clarity and appropriate difficulty. Since an LLM generates both questions and answers, filtering becomes essential. Our filtering method takes advantage of the principle that simple questions should elicit consistently correct responses. Thus, for each generated question yi we sample l: = 8 responses from separate LLM L': C'(4:) {v1, 32. ).
We retain a question-answer pair (g,, a.) if and only if all the sampled answers from the LLM L' match the answer a generated by the LLM L. For answer matching, Cudge that outputs true only if the two answers agrec i.c., data point is accepted Vf E {l...., ......,k. k): Eyudge (qi, a; y/) True. The exact prompt for Ljude is presented in Figure 8.
LLM-as-a-Judge
This recipe ensures three properties: (1) Answer Correctness: The agreement among samples val- idates the reference answer with high likelihood. (2) Question Clarity: Consistent responses in- dicate unambiguous phrasing, since ambiguous questions would lead to divergent interpretations and answers. (3) Appropriate Difficulty: The requirement for 100% agreement ensures questions
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simple enough that they don't require extensive reasoning. Questions that pass this filtering constitute the final OverthinkingBench dataset.
Final Statistics. For OvT CVT-General, after filtering, obtain =1327 high-quality questions, with approximately 330 questions per answer type and about 18 questions per domain. OvT-Math consists of 133 questions from Levels and of the MATH dataset.
we track both aceuracy and the number of thinking tokens generated', ensuring that models produce correct answers without excessive computation. First, for accuracy in OvT-General, we employ the same LLM-as-a- Judge Ljudge used for dataset filtering to determine the correctness of model answer yi, for given question and reference answer a;;
Evaluation Metric. To evaluate models on overthinkinggeeln
Correctness; Ljudee (4i, a, Vi) - {0, 1}
We rely on an LLM for correctness judgment because model responses on Overthink wwwhhhinnnnngnennne have diverse answer formats that preclude exact matching. For OvT MyTh, we use mathematical answer matching using the math-verify tool (Kydlfček). Next, using this correctness criterion, we propose pearhinkiin-Adjuster Accuracy (OAA)) a unified metric to track a model's accuracy when using fewer than & thinking tokens:
OAA: Σ (Correctness; L(ThhinTToeen < t)) Y=1
However, selecting the threshold t presents a challenge, as a small threshold would cause most thinking models score O, while a large threshold would not penalize overthinking. Thus, as an aggre- gated metric, we report the area under the OAA, curve, where the x-axis represents the threshold of thinking tokens i and the y-axis represents the corresponding OAA, score. The metric is calculated as:
Cort
Ovetton
40%
60%
OA4
Nor ri Ouarfiri rg CytnulThanin
30%
430 Thisking Token Throahold IO
800
Amas OAA:
Emax
OAA
AUCoMA =
di
RM
fmax
fmax
Figure 2: Visualization of AUCoAA metric show- ing Overthinking-Addjsteed Accuracy (OAA,) versus thinking token threshold t. We illustrate with three model types: a Non-thinking model (red) achieves constant 70% accuracy from t=0; an Overthinking model (orange) overthinks even on simple problems, decreasing AUCоΛs: and Optimal Thinking model (blue) thinks fast on simple problems while spending more compute on harder problems, achieving better AUCOAA. Shaded areas represent AUCoAA values. The ranking: AUC AUC AUCovertink
t=
(1)
where fmax denotes a pre-defined maxi- mum number of thinking tokens. Our pro- posed metric has several key properties and advantages: (1) Maximum and min- imum values are comparable to accuracy. making it interpretable and easy to mea- sure progress with. (2) Models achieve high scores by simultancously using mini- mal tokens (ideally 0) and answering cor- rectly. (3) Both failure cases, where mod-
els either do not think but generate incorrect answers or generate correct answers but think lot, will obtain low scores. (4) Despite the integral form, the metric is easily computable since token counts
fixed for each response, reducing the equation to a single term rather than integration. Figure provides a visual illustration of AUCOMA-
2.2 UNDERTHINKINGBENCC
Underthino inkk ngBench is constructed based core principle that no matter how large a non- thinking model is, its performance on complex reasoning tasks will be lower than that of much
We count tokens explicitly marked for thinking for each model (c.g., tokens hetween think tags).
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smaller thinking model. In other words, it evaluates how necessary "thinking" (via chain-of of-thought token generation) is to solve a problem.
we again consider different domains of reasoning problems, moving beyond just math. In particular, we start with 100 different reasoning tasks from Reasoning Gym (Stojanovski et al., 2025) and 4 standard math benchmarks. We evaluate the performance of a small thinking model small and a large non- think -thnkk for each task. We retain only tasks where Pinaal Plarye think > A, with threshold À = 0.1. This selection criterion yields: (1) UT UU--U----E Reasoning - a collection of 11 reasoning task types across categories: games, algorithms, graphs, arithmetic, geometry, and logic and (2) UT-Math, consisting of competition-level math benchmarks AIME 25 (Art of Problem Solving, 2025) and HMMT'25 (Balunović et al., 2025). Table in the Appendix presents For each of these tasks, we procedurally generate
Dataset Generation. To operationalize this principle, similar to Overthinkinggertt
thinking model Plagee
the complete list of tasks from UT-Reason questions, allowing us to track progress of underthinking in two model types: (1) Non-thinking models may achieve low accuracy because they cannot generate sufficiently long and correct CoTs. (2) Thinking models may rely on heuristics and underthink on the problems, leading to incorrect answers. The procedural generation enables creation of new questions with increasing complexity to prevent benchmark contamination and to keep up with improving model capabilities.
Final Statistics. We generate 550 questions for UT-Reasoninng with 50 questions for each of the 11 types of reasoning tasks. UT-Math consists 60 questions from AIME'25 and HMMT' exams.
Evaluation Metric. Underthi nk nggench tests the model's ability to generate correct answers to complex reasoning tasks without constraining thinking tokens. We use the task-specific program- matic verifiers provided by Reasoning Gym. In particular, for each sample, we extract the model's final answer from the last \bboxed } in its output and pass it to the task's verifier, which checks correctness against the problem instance via code execution. For example, in the maze shortest-pait -att task, the verifier simulates the proposed path to check for its validity and compares its length to an algorithmically computed optimal solution. For UT-Ma Math we use answer match based on the math- verify tool (Kydlíček). Our final score is the macro average across the reasoning and math subsets.
2.3 EVALUATION METRIC OF OPTIMALTHIKINGBENC
The goal of Opt ima Taaalhhinnn innBenchh to track progress through a single unified metric, since overthinking and underthinking are two sides of the same problem. To standardize evaluation across both benchmarks, we combine AUCOAA from sedderthinknngeent
and accuracy Accur from AUCrAA Overall, model scoring high on Optimalle iettiimalhinn wehiinkkngBBnch must avoid overthinking on simple problems and underthink- ing on complex ones. This metric ensures that models must perform well on both benchmarks simul- taneously to achieve high scores, as F1 tends to be closer to the lower of the two component metrics.
into a single F, score: Fh
AlCosssAcss
3 EXPERIMENTS
3.1 EXPERIMENTAL SETUP
For generating questions (C), filtering (C'), and evaluation (Cjuuge), we use the same LLM: Llama-4- Maverick with different prompts listed in Appendix C. For evaluation, we the maximum number of thinking tokens /max 1000 in Equation 1. We create Underthing ierttinn nk nggBench using Qwen3- 1.7B as the thinking model and Qwen3-235B-AND mance using similar method as described in the Evaluation section. For each task/dataset, we evalu- ate 50 questions on 8 random seeds and take the average. We use À = 0.1 to obtain a representative set of tasks satisfying the principle that "for certain questions, no matter how large a non-thinking model is, its performance on complex reasoning tasks will be lower than that of a much smaller yet thinking model," while also accounting for statistical uncertainty in estimating model performance. We evaluate 33 different open source and proprietary models with varying model sizes and differ- ent families. For hybrid models, we evaluate them in both thinking and non-th non-thinking modes. We compare models on the complete Opt imaal www.iihiiiiiinnnnncceeee using Foth metric. Full details are in Appendix B.
as the non toinkhink model, and compute perfor-
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comparing open/closed thinking/non- thinking models based accuracy, thinking tokens, and our proposed metrics. The main metrics for over, under, and aptimal-thinking are AUCoAA, accuracy. and F respectively. These metrics bolded for the best performing model in each of the four categories. Hybrid models evaluated in either thinking or non-thinking mode. *Only thinking tokens are counted.
Table Main results on
Overthinkinggench
Undertthinkingeenc
Model
Tpeimal/hinnknngBench F1
Accuracy (%)
Tokens
AUCoAA
Accuracy (%)
Tokens
Open Non Thinking Models
Llama-4-Scout
19.1
95.0
95.0
10.6
904
Llama-t-Mavrrick
27.9
95.7
0
95.7
16.3
993
Qwen2.5-7P
9.6
93.6
93.6
1370
Qwen2.5-72B
19.0
96.3
0
96.3
10.5
1174
Qwen3 1.7B!
12.9
89.0
0
88.8
6.9
1943
Qwen3-8BI
24.5
95.9
95.8
14.0
2223
Qwen3-235B-A222
31.7
1501
Closed Shinkingg Models
Sonnet 4
48.3
97.4
97.4
32.1
2229
GPT-4o
17.8
95.3
0
95.3
9.8
694
GPT-4.1
35.4
97.1
1846
Open Thinking Models
Magistral-Smaal Smm6l-2506
11.2
95.7
3303
6.4
42.9
16788
DistiillllLB Llama
20.7
Qwen3-1.70
24.2
93.8
1519
20.6
29.2
13072
Qwen3-8Bt
24.3
98.1
1588
16.3
47.7
13858
RI-0528-Qwen3--8
28.8
96.6
1926
24.2
15610
Swen3-233BBAAe
23.2
1632
14.6
12057
GPT-OSS 20B
57.3
97.1
467
72.7
47.3
8937
GPT-OSS-1200
68.3
4968
Closed Thinking Models
Sonnet-4 03
64.2
99.3
706
71.3
58.3
14035
71.1
97.5
235
78.6
65.0
6273
3.2 MAIN RESULTS WITH THINKING AND NON-THINKING MODELS
In Table 1, we show the performance 20 representative models on Opt iimalmmihiin winhiinnnnnBnnhhh with full results for all 33 models in Table 6 (Appendix D). Our evaluation reveals the following key findings on the state current thinking and non-thinking L.LMs.
Models fail to achieve optimal balance between accuracy and efficiency. Comparing primary F1 metric, we observe that o3 achieves the best performance on Sgttimmmhhinnnnggeeee..... at 71.1%. Among the open-weight models, the best results are obtained by the GPT-OSS-120B model at 68.3%, representing a 3-point gap compared to the best closed- model. Apart from GPT-OSS, all other open-weight weight models score below 50% rent model effectively balances efficiency and reasoning capability because they either do well on Overth nk ngBenrch Underthis nk nngencch but both at the same time. This gap demonstrates substantial room for improvement in developing unified models (particularly with open recipes and weights) that can adaptively adjust their computational effort based on task complexity.
benchmark. Overall,
Most thinking models exhibit severe overthinking on simple queries. OverthinkingBench, all thinking models generate at least 100 thinking tokens for sim- ple queries, with most models generating more than 1300 tokens. This is reflected in the AUCoAA scores that are much lower than the corresponding raw aceuracy numbers. The best perffoeefonnnng open and closed thinking models are GPT-OSS-1208 and o3, generating 154 and 235 tokens respectively. However, other models such as Qwen3 utilize between 1373- 1632 tokens, while Magistral utilizes over 3300 tokens. Since most queries in this benchmark simple questions such as "If steel rod is I meter long, what is its length in centimeters?". the unnccessary computation severely penalizes their AUCoAA scores, highlighting increased cost and reduced utility for users. In contrast, non-thinking models achieve much higher AUCaAa scores, matching their raw accuracies.
On
Thinking models, however, show substantial gains complex reasoning. Despite over- thinking on simple queries, thinking models are much better than non-thinking models on Underthi nk ngBench. o3 obtains the highest accuracy Underthi nk ngBench

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Table 2: Results comparing different methods for improving optimal thinking on our benchmark. We evaluate on both werthinkk winkenngBec and Underthink ieeeinngeechh to understand how methods developed to reduce overthinking impact underthinking.
Method
OverthinkingBench
Undertbinkinggench
Accuracy (%6 1
Tokens
AUCoAa
Accuracy (8)
Tokens J
D.istill-Owen-/ 7B
24.5
91.5
1172
25,4
23.6
11763
Veri Thinker (Chen al, 2025b)
27.4 (x2.8)
91.9
689 c4rss
(223))
19.4
1954 (:89)
SB-DS
24.3 (422)
82.2 (9.5)
110.019
73.9 (1433)
14,5 (0.))
3155 (:39)
Ll (Aggareal Welleck, 2025)
20.8
91.1
1037
c9.9
21.8
2853
AdapeThink (Zhong al., 2025a)
38.3 (-118)
(47)
(25
25.4
10224 (178)
Qwen3-SB
21,3
98.1
1588
16.3
47.7
13858
Model Merging (Wa al., 2025)
38.2
97.6
1024
32.4 (1631
46.5
11738
(Aggurwal Welleck, 2025)
(44.2)
867 (-49))
(-300)
(130)
4867 (35%)
65.0%, followed by APT-OSS--20B at 57.9%. Analyzing models that operate in hybrid mode, all Qwen3 models score at or below 20% accuracy in non-thinking mode, with Qwen3-32B achiev- ing only 14.9%. However, when these same models operate in thinking mode, their performance increases significantly. For example. Qwen3-14B's accuracy in thinking mode increases from 14% to 52.4%, representing a 38.4% improvement. This pattern also holds for other hybrid models.
3.3 METHODS FOR IMPROVING OPTIMAL THINKING
Given that all models exhibit trade-off between performance and efficiency, we explore dif- ferent approaches to encourage optimal thinking in models. These include: (1) methods for efficient reasoning that mitigate overthinking, on the question difficulty, and (3) explicitly prompting models to not overthink or underthink.
(2) routing between thinking and non-thinking modes based
Efficient reasoning methods reduce overthinking but also affect performance. Our first ap- proach toward improving optimal thinking is to mitigate overthinking in thinking models using recently proposed methods for efficient reasoning. We test five such methods implemented with two kinds of thinking models, as shown in Table 2.3 They are based on the following concepts 1. Length-based Reward Shaping: L1 and AdaptThink (Aggarwal & Welleck, 2025; Zhang et al., 2025a) primarily modify the reward function during RL training to include an additional length term along with original correctness reward. 2. Model Merging: This method (Wu et al., 2025) merges weights of two different models with different output length distributions, to enable short CoT on simple and long CoT on complex questions. 3. Auxiliary Task Training: VeriThinker (Chen al., 2025b) shows that training for verification task leads to more efficient reasoning.
Generally, these methods reduce token usage in the range of 12% to 91% on Underthinkinggest in accuracy (of up to 13%) in 5 out of these 6 motel- combinations compared to their base
there is a clear decrease versions,. This results in 2 out of these 6 configurations underperforming indicating that efficiency gains come at the cost of reasoning capability. The only exception is AdaptThink that improves on Underthine ri niknkkh intthiiiinnnncc ngBench and significantly reduces thinking tokens on Overt example, -Disstll-Qwen. 7BB achieves 24.5 1yath score, but with LI, this drops 20.8% despite some token reduction (from 1172 to 1037 tokens on eveertinninggennt
OverthinkingBence
However,
the overall Fish score,
although at the expense of a small drop in accuracy (0.7%). For
contains non-math subsets of data and interestingly, we find that even when there are efficiency gains from these methods, they are often less pronounced for non-math tasks (e.g., 37% instead of 82% with AdaptThink). highlighting that training for efficient math reasoning may not always generalize to other domains (Table 7 and Appendix D).
Recall that Opt ima 1 wwinkiingennneeeeeeeee
Question -iffiiindifffcult based routing helps optimality but still has large gap to the oracle router. Our next approach is to leverage router model that uses non-thinking mode for simple questions and thinking mode for complex questions. We evaluate an open-source router (Tran et al., 2025) on Qwen3 models (that support hybrid modes), comparing against both the best individual mode per- formance and an oracle router that always selects the optimal mode. This trained router is a publicly available oftt--of-hheaate art model that is prompted to classify queries as simple complex, using
We directly evaluate these models from HuggingFace, without retraining them.

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Table 3:
Comparison of state-of-the-ar router (that routes between non-thinking and thinking modes based on question difficulty) with an oracle router on Qwen3 to encourage optimal thnnking.
OverthinkingBench
Underthinkinggench
Method
Opt.mma/Thinnkinggenchh
Accuracy (%)
Tokens ! AUCosA
Accuracy (%)
Tokens J
Qwen3 (Avg)
24.3
97.0
1544
17.1
45.9
24074
Qweni-NouThink (Avg)
23.7
94.5
0
94.5
13.7
3545
w/ Trained Router
(120495)
876
55.2
41.7
22238
w/ Oracle Router
61.2
94.5
94.5
45.9
24074
Table 4: Results comparing different prompt variations to encourage optimal thinking.
Overthinkinggench
UnderthinnkingBenh
Method
Opt Sgtimmalhhiniingeenn F1
Accuracy (
Tokens
AUCosA f
Accuracy (%) f
Tokens
Standard
26.3 18.3 (8.)
96.7
1493
19.4
43.1
13207
Step-by-Step
96.4 (0.3)
1638 (-109)
12.0
43.2 (LL))
13580 (138)
Don't Overthink
34.0 (+7.))
96.7 (-0.0)
1147 (-279)
29.5 (+10
42.0 (411))
12424 (69))
which we route non-thinking or thinking mode, respectively. We also evaluate against an oracle router that chooses the non-thinking mode for Overthi nk nggencch and the thinking mode for Underth i nk ngBench. Table 3 shows the results aggregated across different Qwen3 models (full results in Appendix D). While the model-based router improves upon the best individual mode by 20.4%, it still falls significantly short of the result obtained by an oracle router, with gap ranging around 15%. These results suggest that while routing techniques may provide benefits in specific scenarios, developing effective routers for general unified reasoning remains an open challenge.
Explicitly Prompting Models. Next, explore whether models can be explicitly prompted to think optimally. In particular, we use the following prompt suffixes: 1.) Don't Overthink: We explicitly prompt models to overthink. 2.) Let's think step-by-step: This is standard prompt suffix, often used in real-world queries to encourage models to think step-by-step (Kojima al., 2022). Table 4 shows the results of different prompt variations on OverthinkingBenoc macro-averaged across Qwen3 models (with full results in Table 9). First, encouragingly on OverthinkingBent
we find that prompting models to not overthink leads consistent drop in tokens used (on average by 23%). without impacting accuracy. In contrast, prompting models to "Think step-by-step" leads to a small drop in accuracy across all Qwen models, while importantly, increasing thinking length by roughly 10%. This suggests that the colloquial prompt suffix further aggravates overthinking for simple queries with thinking models. Overall, these results point to the fact that the amount of thinking in LLMs can vary noticeably based on the exact prompts being used. We hope that our benchmark generally encourages further explorations of all these training-tine time and inference-time strategies of optimal thinking in future models.
3.4 ANALYSIS OF OVERTHINKING AND UNDERTHINKING
Analysis of Math vs General Domain. Figure 3 compares different metrics OvT-Math and OvT-Genera 1 with 4 representative models. Interestingly, while models achieve higher accuracy on OvT-Math than in OvT-General, the latter also results in much higher overthinking. This result is specifically striking for GPT-OSS-20B which thinks ten times more on Math, even though these questions are relatively simple. This shows that optimizing overthinking for specific domain may not generalize to other domains and hence may not enhance overall optimal thinking in LLMs.
Analysis by Question Domain and Answer Types. We analyze how overthinking varies across different question characteristies for OvT-General (full analysis in Appendix D.2). Figure 4a shows thinking token usage across domains sorted by average thinking length. Models generate significantly more tokens for STEM domains (Engineering, Economics) compared to History, yet this increased thinking shows no correlation with accuracy (Spearman p = -0.46, p > 0.05) or performance improvements over non-thinking counterparts (p = 0.29, p 0.05). demonstrating models cannot adaptively adjust thinking based on domain complexity. Moreover. Figure 4b shows how answer types affect thinking: while models questions, they consume substantially more for numeric questions. Crucially, unlike OvT-Math, these numeric questions often simple facts, yet they trigger extensive overthinking without
comparable tokens for MCQ and open-ended
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(b) Thinking tokens across answer types.
curacy benefits in 4/5 models tested. This is likely because of the emphasis of post-training on mathematical tasks that causes models to conflate numerical tokens with computational complexity. Additionally, Figure 10 demonstrates that adding completely irrelevant MCQ distractors in ques- tions causes near-linear increase in overthinking (42 tokens per option, R² = 0.94). These patterns reveal that current models may be relying superficial cues (domain keywords, numerical tokens, option count) rather than actual task complexity when allocating computational resources.
Qualitative Analysis. We qualitatively examine failure modes in both sub-benchmarks using sta- tistically significant examples obtained by generating 128 responses per model and selecting cases where performance differences are robust (Full details in Appendix D.4). Examples 1, 2, and demonstrate a recurring overthinking pattern: models initially identify correct answers but sub- sequently overthink, introducing conflicting information flawed reasoning that leads to incorrect conclusions. Conversely, Examples and 5 reveal underthinking behavior where non-thinking mod- els rely on heuristics without verification- -lancatiionccammnnggg the first plausible path without systematic exploration or validation. These patterns illustrate how overthinking creates unnecessary confusion while underthinking omits essential verification steps.
algorithms like BFS while actually taking
4 RELATED WORK
Recent works have analyzed overthinking and underthinking in LLMSs across various domains in- cluding adversarial, tool-use, math, and unanswerable queries (Sui et al., 2025; Wang et al., 2025b; Chen et al., 2025a; Kumar et al., 2025: Cuadron et al., 2025; Song & Zheng, 2025; Zhao et al., 2025; Kirichenko et al., 2025; Liu et al., 2025; TSB, 2025). However, these studies treat overthinking and underthinking in isolation on specific benchmarks. Further, existing works on efficient reasoning primarily target overthinking through RL-based length penalties (Aggarwal & Welleck, 2025; Arora & Zanette, 2025: Yi al., 2025; Zhang et al., 2025a), verification training (Chen et al., 2025b), early exit strategies (Yang et al., 2025; Jiang al., 2025), inference-time interventions (Wang

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Published as a conference paper at ICLR 2026
al., 2025a). Others address underthinking by forcing longer generation through decoding time interventions (Muennighoff et al., 2025; Jin al., 2025). However, these typically rely on disparate evaluation setups and use their unique metrics to measure overthinking underthinking, making fair comparison across approaches difficult and hindering systematic progress. In contrast, Optima opttimal mmaal Th nk ngBench provides the first unified benchmark with standardized metrics for both overthinking and underthinking, making evaluation more standardized and enabling fair comparison between these methods. spans several general and reasoning domains (including math) and reveals that optimizing for one of over and underthinking typically degrades the other. We refer readers to Appendix for more detailed related work.
CONCLUSION
We proposed Opt iimaimmaaa Thi..hhiiigggeeehhh new benchmark jointly measure overthinking and underthinking in LLMs. Our benchmark consists of two sub-benchmarks, spanning math and general-domain questions in 72 domains, with four answer types, and belonging to diverse reason- ing tasks. Through combined efficiency-ajjusted accuracy metric and multiple sub-metrics, we evaluated 33 state-of-the-ar thinking and non non-thinking models and showed that no model is able to benchmark. We also explored different meth-
optimally balance performance and efficiency ods to encourage such optimal thinking which only rarely resulted in improvements, highlighting the need for better unified and optimally-thinkinl designed to evolve with increasing model competence, providing a tunable method to benchmark the optimal thinking performance of models.
LLMs in the future. Opt iotimaliin Think Thinkinggeen mokinnBennh
eech is
REPRODUCIBILITY STATEMENT
We have uploaded Opt ma Thi n TrinkkngBench ngBench part of the supplementary material and will be made public upon acceptance. In Appendix C. we also show our prompts to help reproduce and potentially extend our benchmark in the future. Our evaluation code will also be released to enable benchmarking and measuring progress on Opt wew.imlliiiiiiii Thinlingnniinnnch..
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A DETAILED RELATED WORK
Overthinking and Underthinking in LLMs. Several recent works have analyzed the issues of both overthinking and underthinking in LLMs (Sui et al., 2025; Wang et al., 2025b; Chen et al., 2025a; Saha et al., 2024; Zhang et al., 2025b; Pu et al., 2025). Notably, these analyses span ad- versarial (Kumar et al., 2025), tool-use (Cuadron et al., 2025), math (Song & Zhenng 2025; Zhao et vall,2025, Su et al., 2025; Wang et al., 2025b) and unanswerable (Kirichenko al., 2025) queries. Furthermore, Liu et al. (2025) show that chain-of-thought can hurt performance in tasks where de- liberation hurts performance in humans. Additionally, a very recent concurrent blog post introduces a benchmark and discusses the problem of token efficiency in thinking models (TSB, 2025). Many of these studies have treated overthinking and underthinking in isolation, without unified metrics, often on different and specialized benchmarks, which has hindered the ability to effectively track addresses this issue by provid- ing a unified benchmark and metrics, thereby demonstrating that independently optimizing models for overthinking or underthinking results in improvements in only one of these at the expense of the other.
progress toward optimal thinking in LLMs. OptimalThinkinggeete
Methods for Addressing Overthinking and Underthinking. A large body of prior work has explored reducing overthinking in models with efficient reasoning methods (Arora & Zanette, 2025; Kang et al., 2024; Fang et al., 2025). For instance, Aggarwal & Welleck (2025); Arora & Zanette (2025); Yi al. (2025); Zhang al. (2025a) modify reinforcement learning objectives, VeriThinker (Chen et al., 2025b) trains models on verification tasks, Yang et al. (2025); Jiang al. (2025) develop early exit methods, and Wang et al. (2025a) propose simple inference time intervention. However, these methods have almost universally focused on math and code domains, neglecting the vast proportion of general user queries (Handa et al., 2025). Similarly, past works have improved underthinking by forcefully adding tokens when the model is about to stop generation (Muennighoff et al., 2025; Jin et al., 2025). Furthermore, they typically rely on disparate evaluation setups and use their own unique metrics to measure overthinking thinking, making fair comparison across approaches difficult and hindering systematic progress. Opt imal Think miinnkingnnnhh addresses this gap by providing a unified interface (with benchmarks and metrics) to study both overthinking and underthinking. This makes evaluation more standard- ized and enables fair comparison between these methods. Using this evaluation setup, compare several of these past methods to show that while existing efficient reasoning methods improve over- thinking, they often also degrade underthinking.
under-
B EXPERIMENTAL SETUP
B.1 OPTIMALTHINKINGBBNN CREATION
For generating questions (L), filtering (C'), and evaluation (Cjudge), we the same LLM: Llama- Maverick with different prompts listed in Appendix C. For Overthinkinggeenhh we use 72 different domains, 4 different answer types, and for each (domain, answer type) pair, we generate maximum of 5 questions. For filtering, we sample 8 responses for each question. We use temperature 0.6 and top-p= = 1.0. For evaluation, we set the maximum number of thinking tokens mmx 1000 in Equation 1. In creating wwweerttiininnggeee 1.7B as the thinking model and Swenn-233BB-AD as the non- th--kiiiiiig model.
we set the threshold X = 0.1 and use Qwen3-
B.2 MODEL EVALUATION
with We evaluate 33 different open-source and proprictary models on Opt ipptmaaTThiinnnhe varying model sizes, and different families. For hybrid models, we evaluate them both think- ing and non-thinking modes. We compare models on the complete based on our 1uth metric. addition, for each model, we report the number of thinking to- kens, accuracy, and AUCOAA for OverthinkingBench, and accuracy, complete output to- kens for wodderthinkinngenn inderthinkinggerr
pottimaThiig inknnnhenhh
We report complete output tokens, because answers for are typically only a few tokens, and it is well-studied that even chain- of-thoughh tokens outside of thinking tags contribute to higher performance. All evaluations are performed over 8 seeds, and consistent temperature sampling of 0.6.
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Step 1: Constrained Dataset Generation
Prompt
Output
Problem Constraints
Generate questions (doomann). The be (answer 6ps)
Q 1: Solv for 5
Eg: Type
LLM
generator
A 1:3
Step Dataset Filtering
Answer 1 Answer 2
Keep
Question
Yes
LLM
Answer k
Consistent?
No
Reference Answer
Discard
X
Step 3: Evaluation on Benchmark
Model Output
user aaking
Tokens
Solve for x. 2x + 5 11
double check <thil lo
algebra
LLM
The arewer
Score t
Answer:
LLM Judge
Figure 5: Generation recipe of Overthink nnk ngBench (Step and 2) and evaluation recipe of models on Overthinkinggenee crate and verify the questions and answer correctness. We evaluate model outputs on this benchmark based the number of tokens used (overthinking) and answer correctness, using LLM-as-a- Judge verifier.
(Step 3). We follow generation and filtering pipeline gen-
Step Generation Filtering
Reasoning Tasks
pthink small
Small LLM
Keep
V
Yes
Algonthm: Counting
P small
Artthmetic:: Decimal Caic
P large
Graphs: Shortest Path
No
Discard
X
Pnon- think
12. Games: Maze Sclving
Large LLM
large
Step 2: Evaluation Benchmark
Check correctness solution based on code execution
Selected Reasoning
Accuracy t
Tasks
LLM
Figure 6: Generation recipe Underrthirkingeench (Step 1) and evaluation recipe of models on Underth wetthinnnnnain Bnnchh (Step 2). We follow generation and filtering pipeline first gen- erate and then check for reasoning tasks that particularly benefit from thinking (by leveraging the difference between small thinking model and a large non-thinking model). We evaluate models Underthindn nikknchhhehhhhh using accuracy computed with a code-hased verifier.
C PROMPTS AND ADDITIONAL DETAILS OF OFTIMALTH
This section consists all prompts used throughout OverthinnkingBench for data generation, filtering, and evaluation.
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Main prompt for generating OverthinkingBench questions
suppose have this problem. So basically there are these recent modeds that are called reasoning models, and the idea is that if you increase the inference compute, in the sense that they generate big
longer chain of thoughts, the accuracy increases. However, challenge with them is that they sometimes overthink, spending 1ot of compute even en simple questions, results in reduced utility for user, as it takes a let of time generating long thinking. simple question could be 2+2, and the model is expected to answer immediately.
In order to evaluate this behavior plan to propose
overthinnkiggennhh The idea is simple, this benchmark would contain some very simple questions, where model is expected think for more than 10 to 20 tckens, and sometimes tckens to The accuracy would mostly be 100% because the questions would be simple, and our evaluaticn would be average tokens used, standard deviation, or thinking viclations (how many times thinking was > 20 tokens).
them.
New want you to make prempts for such a dataset. Note that distribution of prompts should be similar to standard benchmarks for Large language models. Simple questions, although of varying difficulty, varying demains, varying types. Your goal is to create 50 such prompts. Diversity along different dimensions is expected.
OUTPUT FORMAT:
output jsen, List (dict), where dict contains two keys: "Question", "Answer category"
Domains: I will give you the following domains, and you are expected to generate some simple (not at all teugh) questions. This is to ensure the benchmark contains real world queries. The straight factual questions, require very basic math questions.
questions
very basic multi-hop reascning,
{question format]
Here are the {len (domains) domains: (domains)
For each domain create
questions. ioss.
Figure 7: Main prompt for constructing the OvT-General subset of Overthinkinggench
C.1 QUESTION GENERATION
The core prompt for generating simple and general questions across diverse domains and answer types is shown in Figure 7. This prompt is designed to elicit questions that should require minimal reasoning tokens while maintaining diversity across domains and answer formats.
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C.2 ANSWER FORMAT SPECIFICATIONS
OverthinkingBent supports four distinct answer types, each with specific constraints to en- sure sound evaluation. The format specifications are provided template substitutions in the main generation prompt.
Prompt specification for numeric answer questions
Additionally answer to every question should be numeric value, that can be matched to gold answer, However, the answer to the question should be clear. Answer in similar json format.
#1 Important: Make sure questions have 1 clear numerical answer with no ambiguity or any potential similar or nearby answer.
For example, bad questicn would affected by diapetes worldwide in millions?" This is bad question, because the number keeps on changing every year, is based on estimate and therefore answers could vary. Do not output such questions.
"Hew many people
Prompt specification for multiple choice questions
Additionally every questicn will be MCQ, with only one correct option and total of fnumLoptions) options, of which clearly is correct without ambiguity. Answer in similar json format.
Prompt specification for short answer questions
Additionalle answer to every question should be a short ansxer such as single word or phrase, that can be matched to gold answer. However, the similar json format.
to the question should be clear. Answer in
Prompt specification for long answer questions
Additionally answer to every question should be long answer such as paragraph, that will be judged by a separate LIM as judge against the reference answer. However, the answer to the question should be clear without ambiguity. Answer in similar json format.
C.3 LLM-AS-A-JUDGE VERIFICATION
Once the model generates the answer, we first extract the answer within the last \ Aboxed its output, using regular expression. The answer is then passed to the LLM-as-a-Judge [boxed is not found, provide the complete output (excluding the reasoning trace) to the LLM-as-a- Judge. The verification prompt for the L-M--s-a-Judge is shown in Figure 8.
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LLM-as-a-Judge prompt for answer verification
User:
### Question: (question)
#4# Ground Truth Answer: (ground truth)
##d Student Answer: (student.answer)
For the above question, please verify if the student/s answer is equivalent to the ground truth answer.
De not solve the question by yourself, f, just check if the student's answer is equivalent to the ground truth answer.
the student's answer is correct, output "Final Decision: Yea!r, the student's answer is incorrect, output "Final Decision: No!
Assistant:
Figure 8: LLM-as-a-Judge Answer Verification Prompt.
C.4 DOMAIN COVERAGE
Overthinkinggench spans 72 distinct domains to ensure comprehensive coverage of real-world query distributions. These domains are sourced from SuperGPQA and are shown in Figure 9.
Complete list of 72 domains used OverthinkingBence Creation
Electronic Science and Technology, Philosophy, Traditional Chinese Medicine, Applied Economico cs, Mathematica Physics, Clinical Medicine, Computer Science and Technology, Information and Communication Engineering, Control Science and Engineering, Theoretical Economics, Law, History, Basic Medicine, Education, Materials Science and Engineering, Electrical Engineering, Systems Science, Power Engineering and engineer Thermophysics, Military Science, Biclegy, Business Administration, Language and Literature, Public Health and Preventive Medicine, Political Science, Chemistry, Hydraulic Engineering, Chemical Engineering and Technology, Pharmacy, Geography, Art Studies, Architecture, Forestry Engineering, Public Administration, Oceanography, Journalism and Communication, Nuclear Science and Technology, Weapon Science and Technalogy, Naval Architecture and Ocean kngineering, knvi ronmental Science and Engineering, Transportation Engineering, Geology, Physical Occanography, Musicology, Stomatology, Aquaculture, Mechanical Engineering, Aeronautical and Astronautical Science and Technology, Civil Engineering, Mechanics, Petroleum and Natural Gas Engineering, Seciology, Food Science and Engineering, Agricultural Engincering, Surveying and Mapping Seience and Technol
Technology, ogy, Metallurgical Angineering, library Information and Archival Management, Mining Engineering, Astronemy, Geological Resources and Geological Engineering, Atmospheric Science, Optical Engineering, Animal Husbandry, Geophysics, Crop Science, Management Science and Engineering, Psychology, Forestry, Textile Science and Engineering, Veterinary Medicine, Instrument Science and Technology, Physical Education
Figure 9; List of all domains used in Overthinkinggench.
C.5 ADDITIONAL DETAILS OF UNDERTHINKINGBENCHH
UnderttainkingBench utilizes existing challenging reasoning tasks from the Reasoning Gym framework. Rather than using custom prompts, leverage 11 pre-defined reasoning task types with specific parameter configurations. Table provides an overview of tasks along with their categories and descriptions.
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Table Reasoning tasks and configurations for underthinking benchmark.
Reasoning Task
Category
Description
ab
Algorithmic
Pattern recognition sequences
Letter Counting
Algorithmic
Count specific letters in given text
Bitwise Arithmetic
Arithmetic
Execute bitwise operations on binary numbers Simplify fractions to their lowest terms
Fraction Simplification
Arithmetic
Quantum Locks
Graphs
Find shortest sequence to reach correct value
Maze
Games
Navigate through the maze to reach destination
Knight Swap
Games
Swap all positions of black knights with white knights Use four numbers to make 24 with operations
Puzzle 24
Games
Tsumego
Games
Solve Go game tactical problems
Advanced Geometry
Geometry
Solve advanced geometry problems
Propositional Logic
Logic
Infer correct conclusions from given premises
Each reasoning task generates 50 instances, resulting in a total of 550 challenging problems that require substantial computational effort to solve correctly. The tasks span six domains: games (maze, knight swap, puzzle 24, tsumego), algorithms (ab, letter counting), graphs (quantum locks), arithmetic (bitwise arithmetic, fraction simplification), geometry (advanced geometry) and logic (propositional logic).
D ADDITIONAL RESULTS AND ANALYSES
D.1 METHODS FOR IMPROVING OPTIMAL THINKING
Full results on 33 models are shown in Table 6.
This section contains the full results for the methods mentioned in the main paper.
Results for efficiency-based methods the non-math subsets shown in Table 7. Results for router based methods are in Table 8. Results for prompt based methods are in Table 9.
D.2 OVERTHINKING ANALYSIS
Analysis by Answer Types. In Table 10 we evaluate hybrid models like Qwen3 and compare their accuracy differences between thinking and non-thinking modes across four answer types from our everthinkinggeneee templates. Results where thinking statistically improves performance statistically significant degradations are marked in red (p < 0.05). Overall, the context of our benchmark, we find limited evidence that overthinking significantly harms performance in Qwen3 hybrid models across most answer types. However, thinking definitely reduces user utility due increased latency. When comparing with previous-generation models (Qwen2.5-Instrucc) we clear accuracy drop for non-thinking models in numeric mode despite similar or smaller model sizes, suggesting that adding thinking capabilities to hybrid models might have compromised non-thin -oniniininng mode performance.
Qwen3 allows switching between the two modes through its chat
marked in green, while
Analysis by Question Domains. Figure shows thinking token usage across different domains and model families, sorted by average thinking length with the highest domains at the top. The trends suggest that models generate more thinking tokens for STEM domains such as Science and Engineering, compared to domains like History. Interestingly, this occurs despite models achieving similar accuracy across these domains (Spearman = -0.46 and = 0.1 > 0.05), with little corre- lation between domain type and correctness. Furthermore, when examining accuracy improvements over their non- non-thinking counterparts, we do not find any statistically significant correlation between increased thinking and performance delta (Spearman p 0.29 and p 0.33 > 0.05). These results
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Table 6: Main results on OptimalThinkingBen thinking models. We also show individual results for OverthinkingBen SnweettiinknngBeneee
comparing open/closed thinking/non-
and reporting aceuracy, thinking tokens, and our proposed metrics. The main metrics for over, under, and optimal-thinking are AUCOAA, accuracy, and 1T respectively. These metrics are bolded for the best performing model in each of the four categories. Hybrid models evaluated in either thinking or non non-thinking mode.
Model
opt DggimmaalhhnnnngBanet F7
OverthinkingBanch
Undarcthinkingaanch
Accuracy
Tokens
AUCoss
Accuracy
Tokens
Open Non- Dhinkiinng Models
Mistral 3.mal-3.2-24B--22
1lama-3.1-88
6.5
85.1
85.1
3.5
3811
L1amm-333-70B
92.8
Lamm 4-Scout
19.1
95.0
95.0
10.6
904
M-averik
95.7
Qwen2.5-7B
9.6
93.6
93.6
5.1
1370
Qwen2.5-Mathi
Qwen2.5-728
19.0
96.3
96.3
10,5
1174
Satth2.-Math--2BB
91.8
91.3
8.2
1010
Qwen3-1.775
12.9
89.0
88.8
6.9
1943
Qwen3-8Bf
24.5
95.9
95.8
14.0
2223
Qwen3-14B
96.7
96.6
1585
Qwen3-328
25.8
96.3
96.2
14.9
1423
Qwen3 235B-A22B
31.7
96.9
96.7
18.9
1501
Closed Thinkiin Models
Sonnet-4
48.3
97.4
97.4
32.1
2229
GPT-4o
17.8
95.3
95.3
9.8
694
GPI-4.1
35,4
97.1
97.1
21.7
1846
Open Thinking Models
Magistral-Small-2550
11.2
3303
6.4
16788
R RI-Distill-1155 L5
13.3
80.5
1466
15.2
11.8
13025
DeagScalcR-1.S--Poviiss
18.8
82.7
1022
23.3
8617
RI RI-Distill-BB
24.5
91.5
1172
25.4
23.6
11763
RI-Distill Llama-Sh
20.7
93.2
1307
21.7
11113
Qwen3-1.7B"
24.2
93.8
1519
20.6
29.2
13072
Qwen3-8Bt
98.1
1588
16.3
47.7
13858
11-0528-Qwen3-88
28.8
96.6
1926
24.2
35.7
15610
Qwen3-14B
30,3
98.3
1373
21.3
52.4
12691
Qwen3-32B
Dwen3-235B-A22BB
23.2
98.3
1632
14.6
55.5
12057
Hunyuan-A13B
47.1
96.7
$2.2
42.9
12103
GPT-OSS-20B
57.3
97.1
467
72.7
47.3
8937
Closed Thinking Models
Sommet-4
64.2
99.3
706
71.3
58.3
14035
Table 7: Results comparing differrntt methods for improving optimal thinking our benchmark. We evaluate on both wwwrrtiiiingggee and Underthine wwwetttiinnnnnnnnnnn ingBench to understand how methods developed to reduce overthinking impact underthinking and viseversa.
Method
Dottimm/Thinniigee FI
OverthinkingBench
Underthinnknnglench
Accuracy (5))
Tokens | AUCoAA
Accuracy (16)
Tokens
RI DisDiiil-Qwet::
22.8
85.1
562
44.9
15.2
17967
VeriThinker (Chen 2025h)
15.2
85.9
430 c305)
61.5
:4/0)
2070.
18.7 64.0
83.6 (1.5)
180 (46%)
70.5 (25.8)
(4.)
3598 (90%)
L1 (Aggarwal & Welleck, 2025)
24.3 (4..)
84.8 (0.3)
562 (10))
39.1 (82)
17.6
3494 (413)
Adap(Think (Zhang 2025a)
27.3
85.4
356
61.4
17.5
17176
Qwen3 sn
34.7
96.3
854
30.5
40.3
19505
Model Merging (Wu 3025)
41.6
96.0
553 (359)
50.9 intun
35.1
15560 (00))
(Aggarwal Wellock,
(6.
(348)
(00%)
highlight that models cannot flexibly adjust their thinking based the question domain, resulting in more overthinking in specific domains than others.
Analysis by Answer Types. In Figure 4b, analyze how answer types affect the amount of think- ing. All models show similar behavior: they use comparable token counts for MCQ and open-ended questions while consuming substantially more tokens for numeric questions. Interestingly, unlike
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Table Comparison of state-of-the-arl router model (that routes between non-thinking and think- ing modes based on question difficulty) with an oracle router on Qwen3 family of models to encour- age optimal thinking.
Overthinkinggench
Underchinking@ench
Method
OptiimalTThinnkngBBonc.
1
Accuracy (S)
Tokens
AUCosa
Accuracy (S)
Tokens
Qwen3-1.7B
20.6
26143
Oweea-1..B-Non
12.9
89.0
89.0
6.9
3886
Trained Router
(+10.0))
53.6
26.1
24283
Oracle Router
43.9
89.0
89.0
29.2
26143
Qwen3.8B
24.3
98.1
1587
16.3
47.7
27716
Qwen3 8B 88-NonThink
24.5
95.9
95.9
14.0
4447
w/ Trained Router
49.1 (:24/9))
97.6
900
56.0
43.8
25077
Oraele Router
63.7
95.9
27716
Owen3-328 32B
25.4
97.9
1423
16.9
51.0
25304
(wea3-32B-NonTDhee
25.8
96.3
96.3
14.9
2846
w/ Trained Router
50.5 (+24.9)
97.2
815
55.8
46.
22901
Qwen3-235B-NW 235B-A228
23.2
98.3
1643
14.6
55.5
17133
Qwen 235B-A22B-NoaThhin. w/ Trained Router Oraele Router
53.0 (22139)
97.6
929
55.5
50.8
16691
70.5
96.9
17133
Table 9: Results comparing different prompt variations on Opt imatimalle Th Thinkiggenc to encour- age optimal thinking.
OverthinkingBench
Underthinkkiggench
Method
Opt ima LeeemmmaThnnnnnnnneee
Accurucy (55)
Tokens
AUCosa
Accuracy (9)
Qwen3-1.7B
Standard
24.2
93.8
1519
20.6
29.2
13072
Step-by-Step
19.5 (447)
93.9 (10.1)
1620 (179)
14.6 (6.)
29.4 (:0.)
13261 (119)
Overthink
28.8 (+4.0)
94.2 (+0.4)
1156 (205)
30.4 (-93)
27.4 613)
12183 (19)
Only Answer
30.8
93.9
1131
32.4
29.3 (40.))
12236
Qwen3-8B
Standard
24.3
98.1
1588
16.3
47.7
13858
Step-by-Step
(8.6
97.4 (0.7)
(-119)
9.4 (om)
47.3 (0.4)
14400 (+48)
Don't Overthink
34.0 (49.))
97.8
1233
26.8
46.4 (4:3)
13030
Only Answer
36.8 (-12.5)
98.1
1247 (229))
29.7 (413.4)
48.4 (407)
13149 (59)
Qwen3-14B
Standard
30.3 19.8 (-10.5)
98.3
1373
21.3
52.4
12691
Step-by-Step Don't D Overthink
97.9
1529 (4119)
12.1
$3.0
13078 (438)
39.2 (+K.))
98.1 (0.2)
1051 (229))
31.4 (+10
52. (0.3)
12058 (58)
Only Answer
42.3 (-11.0)
97.9 (4.46
955 (-05))
35.7 (414
51.8 (406))
11817 (-99)
OvT-Matie numeric questions OYT-General are primarily fact based (See Appendix sub- section D.4 for examples). A potential reason for this difference could be due to the increased computational complexity demanded by the numeric questions. However, as shown in Appendix subsection D.2, evaluate the accuracy of different models and find statistically significant difference in accuracy compared to non-thinking models for the numeric domain in out 5 cases. This finding suggests that mathematical tokens prompts trigger extensive thinking (possibly because of the heavy reliance on mathematical tasks nost-training), regardless of the underlying complexity of the questions.
Analysis by Number of Distractors in MCQs. Finally, in Figure 10, we analyze how over- thinking varies with the number of options for multiple choice questions. The figure shows the average number of thinking tokens versus the number of multiple-choice questions averaged across all 5 Qwen3 models. In particular, we augment the original multiple-choice questions in Overth h nk BeBench by adding completely irrelevant options in the questions. Interestingly, despite being completely irrelevant, we see a clear rise in thinking tokens with an increasing number of options. In particular, we see an almost linear (R² 0.94) increase of 42 tokens per option, indicating how irrelevant distractors can lead to overthinking models.
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Table 10: Delta accuracy between thinking and non-thinking mode for different models and an-
swer types. Values show delta accuracy (%). Dark green indicates statistically significant positive changes, red indicates statistically significant negative changes (p < 0.05).
Model
MCQ
Numeric
Open-ended
Open-ended-long
Average
Qwen3-1.7B
2.2%
4.5%
4.7%
3.5%
3.7%
Qwen3-8B
0.8%
2.3%
1.0%
-0,5%
0.9%
Qwen3- 14B
0.0%
0.9%
0.9%
-0.3%
0.4%
Owen3-32B
-0.2%
1.0%
-0.9%
-0.3%
-0.1%
2250-2225BBA2B
-111% 1%
1.6%
0.1%
0.3%
0.2%
v5 Qwen2.5-7B
Qwen3-8BBnoothink
0.8%
-0.0%
2.6%
1,4%
1.2%
Qwen2.5-72B
Qwen3-32B- -2n-nnonnnnnk
0.2%
2.8%
1.0%
-0.5%
-0.5%
Qwen3-235B-AM A22B-nonthink
0.5%
2.1%
1.0%
-0.6%
-0.3%
Unner (Rtu 9081: Stopeud: tokenstoottion
950
900
Average Thinking Length
800
750
100
Number MCO Options
Figure 10: Results showing how amount of overthinking varies with the number of options for multiple choice questions. Despite most options being distractors, there is almost a linear increase in overthinking with an increasing number of options.
D.3 How SCALING MODEL. SIZE AFFECTS OVERTHINKING?
We examine how overthinking varies with model sizes across the Qwen3 family of models on OvT-General. The results reveal that while there no clear trend, thinking token usage gen- erally increases with model size, despite accuracy remaining relatively constant across models from 8B onwards. Specifically, thinking tokens increase from 750 tokens (1.7B) to 950 tokens (235B), while accuracy fluctuates between 86. 16.1% and 86.8% for models 8B and larger. The increased think- ing does not translate to improved performance, as all models starting from 8B parameters achieve similar accuracies. This suggests that larger models may be exhibiting unnecessary verbosity in their reasoning without corresponding performance benefits, highlighting an area for optimization in model training inference procedures.
D.4 QUALITATIVE ANALYSIS OF OVERTHINKING AND UNDERTHINKING
In this section, qualitatively compare how overthinking could hurt performance OverthinkingBench and how non-thinking models can underthink and rely on heuristics in Underthink BnglerthinkingBenchh. ngBench. However, compare models of similar accuracy, naively selecting
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0.87
0:868
0.867
1000
Accuracy
Thinking Length
0.865
0.861
950
0.86
Thinking Length (tokens)
900
Accuracy
0.85
850
0.84
0.83
778
0
0.82
1.78
8B
148
328
235B
Model
Figure 11: Results for how overthinking and accuracy vary with changing model size for Qwen3 family of models.
questions where model does better than model B is not appropriate due to the stochastic nature of models. For fair comparison, we generate 128 responses for each model and only consider situations where the performance difference is statistically significant. In Examples 1, 2, and 3, we show three instances where overthinking by models leads an incorrect answer. In particular, notice common phenomenon where the model initially comes up with the correct answer but then overthinks either because of conflicting information incorrect reasoning. show cases where non-thinking models underthink. In one such case the model says that it would use BFS; however, declared its first attempt as the correct one, without any eeff-verification or exploration of other solutions.
Examples and we
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Example 1: Overthinking
What is the name of the layer in the atmosphere that protects the Earth from UV radiation?
Question:
Model Thinking Process:
Alright, the question is asking about the layer in the atmosphere that protects the radiation. know that Earth's ' atmosphere has several layers,
Earth
and one of them is responsible for absorbing and blocking harmful UV rays.
First, recall layers the atmosphere from surface outward: 1. Troposphere 2. Stratosphere 3. Mesosphere 4. Thermosphere 5. Exosphere
Now, which one these responsible for protecting Earth from UV radiation? remember that the ozone layer is important for this, but the ozone layer is part of specific atmospheric layer.
I think the ozone layer is located in the stratosphere. But I'm not entirely sure, so I recall that the stratosphere is the second layer from the surface, and it's where the ozone layer resides. The ozone layer absorbs most of the Sun's harmful UV radiation, protecting life on Earth.
But just to be sure, recall that the ozone layer is the lower part stratosphere. So, the stratosphere the layer that contains the ozone layer, which protects Earth from UV radiation.
But wait, is the stratosphere itself the protector, or is specifically the ozone layer within the stratosphere? The question asks for the name of the layer in the atmosphere, not the specific component (ozone). So, the layer is the stratosphere.
[... truncated...... .../
But, recall that sometimes people refer to the ozone layer itself as the pro- tective layer. However, the question specifies "the layer in the atmosphere," and the ozone layer part stratosphere. But common terminology, people might say the ozone layer is the protective layer. But the ozone layer isn't one of the main atmospheric layers (troposphere, stratosphere, etc.), it's region within the stratosphere.
[... trt.nrateed..... .
But, think might be confusing the terminology. The ozone layer is part of the stratosphere, but the stratosphere whole is not solely responsible for protecting from UV radiation-it's the ozone within it. However, the question asks for the layer the atmosphere, not the specifie component. So, strato- sphere is the layer that contains the ozone layer, which protects Earth from UV radiation.
[... pruncated...
After this thinking. I conclude that the correct answer is the stratosphere, because it is the atmospheric layer that contains the ozone layer, which protects Earth from UV radiation.
Final Response:
The layer in the atmosphere that protects the Earth from UV radiation is the stratosphere, as it contains the ozone layer which absorbs and scatters the harmful UV rays.
Answer: Stratosphere
Evaluation: X Incorrect
Expected Answer: Ozone Layer
Analysis: The model demonstrates clear overthinking behavior by repeatedly second- guessing itself and going in circles about whether the answer should be "stratosphere" "ozone layer." The model initially had the correct knowledge that the ozone layer pro- tects from UV radiation, but the excessive deliberation led incorrect final answer due confusion over terminology distinctions.
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Published as a conference paper at ICLR 2026
Example 2: Overthinking with Wrong Conclusion
Question:
Which pop star is known for her hit song "Thank U, Next"?
Model Thinking Process:
Okay, so need to figure out which pop star known for the hit song "Thank U, Next." Let me start by recalling what know about this song. I think it's recent hit. mayhe from the past few years. The title "Thank U, Next" sounds familiar, maybe from music video or some awards show.
First. 1 should remember if there's female artist associated with this song. The name "Thank U, Next" might previous song or artist. Wait, there's a song called "Thank You, Next" by pop artist. Lct me think... recall that Lady Gaga had song called "Thank You, Next," but I'm not sure if that's 's the same. Wait, no, mayhe it's another artist.
bit play on words, maybe referencing
[... truncated....................... /
Wait, remember that Ariana Grande had song called "Thank U, Next." Let me check that. No, actually, I think the song is by different artist. Oh! Maybe it's singer named Megan Thee Stallion? No, think that's more about her "Don't Stop" or something else.
Wait, I'm getting confused. Let me try recall. There's a song that big hit 2018, and the artist is female pop star. song "Thank U, Next" was written by singer who also had hits like "Hello" and "All Want." Wait, that sounds like Adele, but she didn't have song with that title.
Hold on, maybe it by the singer who had song called "Love Me Like You" and "Back to Black." No, that's 's Amy Winehouse.
Wait, think the answer is Lady Gaga. She had song called "Thank You, Next." But I'm not sure. Alternatively, maybe it's by newer artist.
Wait, think I remember that the song "Thank U, Next" was released by pop star named Ariana Grande. Wait, no, Ariana's song called "Thank U, Next." Let check my memory.
Wait, no, I think the correct answer is that the song "Thank U, Next" is by pop star Lady Gaga. But I'm not 100% sure. Alternatively, maybe it's by someone else.
I... tru.cratedd....
Wait, another approach: The song "Thank U, Next" was number one in the US. The artist is pop star who had previous hits. Let me think of the artist who had a song called "Poker Face" and "Bad Romance." That's Lady Gaga. So if she had song called "Thank U, Next," that would make sense.
Wait, but I've heard that the song "Thank U, Next" is actually by different artist. Maybe it's by someone like Ariana Grande?
I think need to make decision here. Based on what I remember, the song "Thank U, Next" is by Lady Gaga. So the answer is Lady Gaga.
Final Response:
The song "Thank U, Next" well-known hit that was released in 2018. It is associated with the pop star Lady Gaga, who is recognized for her powerful vocals, iconic music videos, and a string successful hits such Poker Face, Bad Romance, and Just Dance. The song was signifi- cant success, reaching number one on the Billboard Hot 100 and receiving widespread acclaim for its emotional depth and lyrical honesty.
Answer: Lady Gaga
Evaluation: Incorrect
Expected Answer: Ariana Grande
The model shows severe overthinking behavior and confusion between artists. It correctly identified Ariana Grande initially but then incorrectly attributed Lady Gaga's 's other hits this song. The model's excessive deliberation and solf-doubt led it away from the correct answer despite having the right information early in its reasoning process.
Analysis:
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Example 3: Overthinking with Conflicting Information
Question:
What is the number of time zones in Russia?
Model Thinking Process:
Okay, so need to figure out how many time zones Russia has. Let me start by recalling what I know. Russia is huge country, spanning lot of east-west distance. I remember that time zones are generally based on longitudinal di- visions, with each time corresponds one hour.
roughly covering 15 degrees of longitude, which
First, think Russia is in northern hemisphere, and it's located between Europe and Asia. know that the country is so large that crosses multiple time zones. But how many exactly?
Wait, think I've heard that Russia has 11 time zones. But I'm not entirely sure. Let me try to remember or think through the details. The main time zone in Russia Moscow Time, which is UTC+3. But then there's also other zones. For example, there's Yekaterinburg Time (UTC+5), Omsk Time (UTC+6), Krasnoyarsk Time (UTC+7), Irkutsk Time (UTC+8), Yakutsk Time (UTC+9), Vladivostok Time (UTC+10), and Magadan Time (UTC+11), That's like 7 time zones. But wait, I thought there were 11. Maybe there are more? [... truncated...... ...
That's 9. But thought there were 11. Wait, maybe there are more splits. Let me check. Oh, think there's s also the time zone in the Far East, but maybe some of these zones split further? Or maybe I'm mixing up with other countries. Alternatively, perhaps the answer is 11, but need to confirm. Let me think about the history. Russia used to have more time zones, but some For example, in 2014, they reduced Wait, but in 2014, they actually changed the time zones, reducing the number. So maybe the current number is 9, But I need to be careful here. [... truncated.... ...]
merged. number time zones from to
But some sources say 11. Maybe the confasion comes from the fact that before 2014, there were more. Let me check my memory. In 2014, Russia reduced the number of time zones from 11 to by merging some. So the current number is 9. However, there's possibility that some sources still refer the old number. But according to recent information, it's But need to be sure.
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Example 3: Overthinking with Conflicting Information (continued)
Model Thinking Process:
Wait, another way to think: the country spans from western part (UTC+3) to the eastern part (UTC+12), which is a difference of hours. Since each time zone is hour. that would be 9 time zones. But wait, the difference between UTC+3 and UTC+12 is 9 hours, that would mean time zones. But earlier I thought there were 11.
I... truncctted...................... ...]
Wait, this is getting confusing. Alternatively, perhaps the answer is that Russia currently has 11 time zones, but some sources might say 10. However, accord- ing to recent information, after the 2014 adjustments, Russia 9 time zones. I think the commonly accepted answer is that Russia has 11 time zones, but due to the 2014 changes, some sources say or 10. However, the correct answer 11, as the country spans from UTC+2 to UTC+12, which is 10 time zones, but including Kaliningrad as UTC+2, it's 11.
Therefore, despite the 2014 changes, the number of time zones in Russia is 11. However, some sources might say 9 or 10 due to the adjustments. But the correct answer 11.
Final Response:
Russia spans a vast territory across multiple longitudinal regions, leading to significant number of time zones. Historically, the country had 11 time zones, which stretched from UTC+2 (Kaliningrad, an exclave the west) to UTC+12 (Kamchatka, in the far east).
However, in 2014, Russia implemented major reform reduce the num- ber of time zones from 11 to 9. This involved merging some of the previ- ously distinct time zones.
Despite this reduction, the total number of time zones currently recognized in Russia is 9.
Answer: 9
Evaluation: x Incorrect
Expected Answer: 11
The model demonstrates extensive deliberation and conflicting information pro- cessing. While it correctly mentions the 2014 time zone reforms, it gets confused between historical and current numbers. The model shows classic overthinking by going back and forth between and 11, ultimately providing an inconsistent conclusion (claiming 11 is cor- rect but answering 9). The correct answer is 11 time zones as of current Russian law.
Analysis:
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Example 4: Underthinking in Pathfinding
Find the shortest path from to the destination in the grid (output as sequence of directions):
Question:
X
X
0
X
X
0
X
0
0
0
X
0
0
X
0
X
0
0 X
where H represents the destination point
Model Thinking Process:
We are given grid and need to find the shortest path from the start point (*) to the
destination point (H), moving only through open cells (O), and passing through
blocked cells (X). Movement is allowed four directions: up, down, left, right.
Let locate the start (*) and destination (H) points in the grid. Start (*)
position (0, 6) Destination (HI) is position (2, 1)
need to use BFS to find the shortest path. Let me just trace path quickly.
BFS proceeds follows: From (0,6) (1,6) (down) From (1,6) (2,6)
(down) From (2,6) - (2,5) (left) From (2,5) - (2,4) (left) From (2,4)
(2,3) (left)- From (2,3) - (2,2) (left) From (2,2) + (2,1) (left)
So the direction sequence is: down down left left left left left
Final Response:
We are given a grid and need to find the shortest path from the start point (*) the destination point (H), moving only through open cells (O). Step Find Start and Destination Start (*) position (0, 6) Destina-
tion (H) is at position (2, 1)
Step 2: BFS Find the Shortest Path BFS proceeds follows: FFrom
(0,6) + (1,6) (down) From (1,6) - (2,6) (down) From (2,6) - (2,5)
(left) -rom (2.5) - (2,4) (left) From (2,4) (2,3) (left) From (2.3)
(2,2) (left) From (2,2) - (2,1) (left)
Answer: down down left left left left left
Evaluation: x Incorrect
Expected Answer: down down left left down down left left left up up
Analysis: The model shows classic underthinking behavior by claiming never actually implementing or verifying it. It assumes a path exists without checking cell validity and follows the first viable route without exploring alternatives. The model skips systematic exploration and doesn't verify the optimality of its solution.
BFS but
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Example 5: Underthinking in Arithmetic
Question: Solve: ((0xe293 Oxcd5d) (0xd7b7 0x2442))
Model Thinking Process:
I need to solve ((0xe293 0xed5d) + (0xd7b7 + 0x2442)). P'll just convert to decimal, do the arithmetic, and convert back.
Step 1: Compute 0xe293 0xcd5d Convert both values to decimal: 0xe293 in hex = 57987 in decimal 0xed5d in hex 52573 in decimal
Now compute the product: 57987 x 52573 3050347251
Convert back to hexadecimal: 3050347251 in hex 0x74blf51f
Step Compute 0xd7b7 0x2442 Convert both values decimal: 0xd7b7
54695 0x2444 = 9282
Add them: 54695 9282 63977
0x19d1 Step Add the results Now add: 0x74b1f51f + 0xf9d1 0x74b2eef0
Convert back to hexadecimal: 63977
Final Response:
To solve the expression: ((Oxe293 * Oxcd5d) (0xd7b7 0x2442))
We will compute each part step by step using hexadecimal arithmetic.
Step 1: Compute Oxe293 Oxed5d Convert both values to decimal:
0xe293 in hex 57987 in decimal 0xed5d hex = 52573 in decimal
Now compute the product: 57987 x 52573 = 3050347251 Convert back to hexadecimal: 3050347251 in hex 0x74b1f51f
Step 2: Compute Oxd7b7 + 0x2442 Convert both values decimal:
0xd7b7 = 54695 =
Add them: 54695 + 9282 63977 Convert back to hexadecimal: 63977 in
hex = 0xf9d1
Step 3: Add the two results Now add: 0x74b1151f+ 0x19d1 = 0x74b2eef0
Answer: 0x74b2ee,f0
Evaluation: x Incorrect
Expected Answer: 0xb5c30260
Analysis: The model demonstrates insufficient verification of its calculations. It doesn't double-check the decimal conversions, leading cascade of errors. The initial conver- sions for '0xe293' (which is '58003') and '0xd7b7' (which is '55223') are incorrect. This reliance on unverified intermediate steps leads to an incorrect final result. A careful recalcu- lation would have shown the correct answer should be '0xb5c30260'.
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D.5 AUTOMATED ANALYSIS OF UNDERTHINKING PATTERNS
To further quantify underthinking behaviors, we conduct an automated analysis of reasoning chains on Underth weteertiiiingghee innningench using two-step prompting approach with o3. Given an incorrect chain of thought from model, we first prompt o3 to segment the reasoning into coherent, non- overlapping steps based on subgoal switches, method changes, or conclusion points. In the second step, provide o3 with both the segmented incorrect chain and correct chain of thought as reference, prompting it to classify each segment for underthinking patterns. We define underthinking instances where models fail to earry out necessary reasoning, verification, ixploration-distt distinct from simple numerical or symbolic errors.
We categorize underthinking into several subtypes: (1) verification-fallieee verify interme- diate results or the final answer; (2) premature conclusion-stopping reasoning early or accepting assumptions without justification; (3) missing case analysis- skipping required case analysis not exploring all relevant possibilities; (4) promised method not executed to use method or algorithm but actually executing and (5) ignored conrrddction-iinoniii clear contradictions introduced by earlier steps. Each identified underthinking segment is assigned criticalness score (0 9) indicating its contribution the final incorrect answer.
Quantitative Results. Table 11 presents the results of our automated analysis across both non- thinking and thinking models. We observe several key patterns. First, non-thinking models exhibit underthinking more frequently, with 87.2% of samples containing at least one underthinking seg- ment on average, compared to 70.5% for thinking models. Second, while thinking models have lower rate of samples with underthinking, they are not immune to this problem-ever large thinking models like Qwen3-235B show underthinking in 62.4% of cases. Third, distilled reasoning models (R1-Distii variants) show surprisingly high underthinking rates (84-91% suggesting that distilla- tion may not fully transfer robust reasoning verification behaviors. Note that the average number of underthinking steps is higher for thinking models because their longer reasoning chains lead to more segments; thus, Fraction UT provides a more reliable comparison across model types.
Error Type Analysis. Table 12 shows the distribution of underthinking subtypes across model cate- gories. The most striking difference verification failures: non-thinking models fail to verify their reasoning in 44.6% of underthinking cases, compared 34.6% for thinking models. Conversely, thinking models more frequently exhibit premature conclusions (21.9% vs 17.6%), suggesting they sometimes abandon complex reasoning prematurely rather than failing initiate verification. Both model types show similar rates of missing case analysis (~14%) and promised-bus nut-not-executed methods (~10-11 1)). These patterns suggest that non-thinking models primarily underthink by skipping verification steps, while thinking models are more prone to giving up complex reason- ing paths.
Example Analysis Outputs. Below we show two example outputs from automated analysis, illustrating how underthinking manifests in model reasoning:
Example 1: No Verification (GPT-40)
Segment ID: 2
Segment Snippet:
Step 1: Look for neighboring tokens that can
be rewritten... 2. ++B #B** + no rewrite (no #' facing each
other)
Underthinking: True Error Subtype: no.verification
Criticalness:
Error Snippet: 'Bf #2 - no rewrite (no '#' facing each other)
Reasoning: The model claims that the pair "Hl #h* has no facing hashes, but by its own stated rules a right-hash (BF) followed by left-hash (FB) must rewritten to nothing, Failing to notice this shows did verify the facing-hash condition it described, leading to incorrect state that corrupts all further steps.
These examples illustrate the two dominant underthinking patterns: non-thin thinkinnkig models often skip verification of their stated rules (Example 1), while thinking models sometimes prematurely abandon
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Automated analysis of underthinking patterns in model reasoning chains on Underthi nik nngBench. We use two-step prompting approach with 03 to segment incorrect chains of thought and classify each segment for underthinking. "At Least One UT" indicates the percentage of samples with at least one underthinking segment. "Avg. UT Steps" shows the average number underthinking segments per sample. "Fraction UT is the average fraction of segments classified underthinking.
Table 11:
Model
At Least One UT (%) 4
Avg. UT Steps |
Fraction UT (%) /
Non-Thinen Mon-Thinking Models
GPT-4o
87.6
1.67
46.4
GPT-4.1
89.5
1.60
69.3
Sonnet-4 (non-think)
67.0
1.72
32.5
Misttal-Smalll33222441
90.5
2.99
51.0
1lama-3.1-9B--nstruuc
96.1
2,25
64.1
Llama-3.3-70B-Instteee
93.5
3.02
55.8
Llama-4-Scou--77
87.9
3,52
47.8
Llave---MaverickIII1
80.9
2.87
40.9
Qwen2.5-7BBInstruuc
94.4
2.01
70.1
Swen2.5-Matt-77B-Inss
87.5
2.27
57.5
Swen2.5-72B-Instree
87.4
2.00
58.4
Instruct
83.7
2.27
46.3
Qwen3-1 1.7BB (non-think)
96.8
2.18
49.3
Qwen3-8B 8B (non-think)
91.5
2.06
47.0
Qwen3- BwB (non-think)
88.7
1.78
46.8
Qwen3-32B (non-think)
84.8
1.88
42.5
Qwen3-235B-A225 (non-think)
74.3
1.81
32,9
Average Non-Thinking)
87.2
2.23
50.5
Thinking Models
Sonnet-4
53.2
1.21
24.3
Magistral-Small-25500 Small-2506
68.0
2.37
36.1
RI R1-Distill-1.5B
91.2
3.68
64.7
R R1-Distill-7B
84.0
3,25
53.1
RI-Distil--Llama-B
90.1
3,33
56.6
DeepScaleR-1.5B
85.9
3.24
58.9
R R1-0528-Qwen3-88
63.6
2.09
31.3
Qwen3-1.7B
72.2
2.58
43.0
Qwen3-8B
63.4
2.14
33.4
Qwen3-14B
58.6
1.95
30.6
Qwen3-32B
57.9
2.02
30.8
Boon3-235B-A222
62,4
2.23
32.0
Hunyuan-A138 A13B
65.9
2.41
36.9
Average (Thinking)
70.5
2.50
40.9
complex computations (Example 2). Both patterns contribute to incorrect final answers and highlight areas for improvement future model development.
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Table 12: Distribution of underthinking enror types (%) across model categories. "Verification" indicates failure to verify intermediate results. "Premature" refers to prematurely stopping reasoning or accepting assumptions. "Missing" denotes skipped case analysis. "Promised" means claiming to use a method but not executing it. "Ignore" refers to ignoring contradictions.
Model
Verif.
Premat.
Missing
Promised
Ignore
Other
Non-Thinking Models
GPT-4o
54.6
17.5
10.6
9.5
1.8
6.0
Maama-33-77B-Insttme
40.3
16.8
19.0
13.9
3.6
6.4
Qwen3-223 (non-think)
48.8
18.8
12.1
12.7
3.4
4.2
Arg. Non-Thinking
44.6
17.6
14.2
10.3
4.5
8.8
Thinking Models
Qwen3-8B
35.4
24.5
14.4
13.0
6.4
6.3
Qwen3-235B
33.5
27.2
13.4
15.0
5.0
6.0
Sonnet-4
37.7
22.7
15.8
15.2
2.5
5.9
RI-Distill-7B
36.7
19.7
12.8
9.7
8.5
12.6
Arg. Thinking
34.6
21.9
14.3
11.2
7.5
10.5
Example 2: Premature Abandonment (Qwen3-235B)
Segment ID:
Segment Snippet: 'Let's process the leftmest first: 1-2... This is getting very cime-consuiing...
Underthinking:
Truc
Error Subtype: promature.abandoomoe
Criticalness:
Error Snippet: 'This is getting very time-consuming."
The model starts another manual reduction, quickly becones confused, and explicitly gives up because it is 'getting very cime-consuming' instead of completing or validating the computation. Abandening the necessary step leads directly to an incorrect conclusion.
Reasoning:
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Reviews:
[{"review_id": "6U3JYNfJYt", "reviewer": "Reviewer_Vrui", "summary": "The paper proposes a benchmark unifying the overthinking and underthinking for LLMs and corresponding metrics to evaluate the LLM performance on accuracy and efficiency.", "questions": "1. What is the advantage of using this unified benchmark compared to combining two existing benchmarks that separately target overthinking and underthinking?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The benchmark jointly evaluates LLM performance on both simple and challenging tasks. This assessment reveals whether LLMs can adaptively allocate reasoning effort based on task difficulty, thus achieving an optimal balance between accuracy and efficiency.\n2. The paper proposes the overthinking-adjusted accuracy, which presents the token-efficiency of reasoning LLM more precisely\n3. The paper conducts experiments on a wide range of LLMs, which provide a comprehensive and solid evaluation of the current model performance.", "weaknesses": "1. The underthinking dataset is constructed based on performance differences between small reasoning models and large non-reasoning models. However, the evaluation method and the reason for the selected performance threshold are not clearly explained. Additionally, the construction process does not account for the absolute performance of each model. If the reasoning model also performs poorly on a given task, there could be factors beyond reasoning capability, such as context misunderstanding or knowledge gaps, causing the failures. Performance on such tasks may not precisely reflect reasoning capabilities.\n2. The information provided by precision, recall, and F1 scores is limited. Overthinking is a complex behavior that can arise from different underlying causes. The current metrics only assess response accuracy with and without token length constraints, but cannot reveal how responses evolve over time. For instance, on simple questions where LLMs can derive answers in a few tokens, it remains unclear how the model behaves after generating the correct answer. Understanding this evolution is crucial for improving LLMs' optimal thinking capabilities.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ulvdKwkLa6", "reviewer": "Reviewer_UJ2U", "summary": "This paper evaluates the critical problems of \"overthinking\" and \"underthinking\" in long reasoning models. It introduces OptimalThinkingBench, a unified benchmark designed to evaluate the thinking efficiency of these models. The authors conduct a comprehensive evaluation of 33 LLMs on this benchmark, compare the performance of several efficiency optimization methods, and provide a detailed analysis of specific cases. The paper's key finding—that no current model achieves an optimal balance of thinking efficiency—is insightful to this field.", "questions": "1. There appears to be a discrepancy in the paper. Line 209 states the filtering threshold $\\lambda=0.1$, while line 744 states $\\lambda=0.3$. Could the authors clarify which value was actually used for the dataset construction?\n2. What's the reasoning for selecting $t_{max} = 1000$ as the maximum thinking length for the $AUC_{OAA}$ calculation? The experimental results (e.g., in Table 1) show that several models generate far more than 1000 tokens on average for simple queries.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. Comprehensive Workload and Evaluation: The paper demonstrates a substantial amount of work. The benchmark is constructed through automatic synthesis and filtering existing datasets. The experimental section is thorough, covering a broad spectrum of LLMs and testing the effectiveness of multiple optimization methods. The findings and analysis are also detailed and insightful.\n2. Sound Metric Design: The proposed metrics avoid subjective semantic judgments of what constitutes \"overthinking\" and \"underthinking\". Instead, they rely on objective, quantifiable, and reproducible measures: thinking token count and final answer accuracy. This approach provides a robust and practical standard for evaluation.\n3. Scalable and Diverse Benchmark Construction: The benchmark's construction relies on automated synthesis and filtering, ensures it is extensible for future models and helps prevent dataset contamination. Furthermore, the benchmark's broad domain coverage facilitates a more holistic evaluation of model performance across diverse tasks.", "weaknesses": "1. Lack of formal definitions: The paper relies heavily on an intuitive understanding of \"overthinking\" and \"underthinking\" rather than providing precise, formal definitions. This ambiguity can hinder a clear grasp of the exact problem being solved and makes it difficult to establish firm boundaries for what qualifies as each behavior.\n\n2. Potential for dataset bias in UnderthinkingBench: The construction of the UnderthinkingBench is filtered based on the performance gap between two specific models (Qwen3-1.7B and Qwen3-235B-A22B) . This reliance on a specific pair of models may introduce significant dataset bias, potentially making the benchmark overly tailored to the strengths and weaknesses of these models. Furthermore, the \"performance-only\" evaluation approach may amplify this bias, the developer can not capture the true nature of the cognitive failure and is less conducive to developing genuine solutions.\n\n3. Limited analysis of root causes: While the paper provides robust metrics for what is happening (evaluation), it offers limited analysis into why models overthink or underthink. The evaluation neither reveal the root causes of these inefficiencies, nor provide clear, actionable directions for model optimization. Given that \"overthinking\" and \"underthinking\" are widely discussed problems, a paper introducing a benchmark would be stronger if it also provided deeper diagnostic insights, moving beyond scoring to explain the underlying mechanisms.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zIiYxrm2s8", "reviewer": "Reviewer_gas8", "summary": "This paper introduces a unified benchmark that jointly evaluates overthinking and underthinking in LLMs and also encourages the development of optimally-thinking models that balance performance and efficiency. This benchmark comprises two sub-benchmarks: OverthinkingBench, featuring simple math and general queries in 72 domains, and UnderthinkingBench, containing 11 challenging reasoning tasks along with harder math problems. The author evaluate 33 different thinking and non-thinking models and show that no model is able to optimally think on our benchmark. Thinking models often overthink for hundreds of tokens on the simplest user queries without improving performance. In contrast, large nonthinking models underthink, often falling short of much smaller thinking models.", "questions": "Just as the statements on weakness.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "This paper introduces a unified benchmark that jointly evaluates overthinking and underthinking in LLMs and also encourages the development of optimally-thinking models that balance performance and efficiency. Different thinking and non-thinking models and show that no model is able to optimally think on our benchmark.", "weaknesses": "1. The paper's central claim is that \"no model is able to optimally think.\" However, this conclusion is drawn from a pool of models that may not represent the current state-of-the-art in reasoning and instruction-following. Top-tier commercial models like OpenAI's GPT-4/GPT-4o, Anthropic's Claude 3 family, and Google's Gemini family are specifically engineered to handle a wide distribution of task difficulties. More commercial models should be considered. \n2. It seams that many other simple benchmarks can work as the no-thinking parts(many instance like MATH500), and many hard benchmarks can work as the thinking parts(like HLE math parts). The authors should highlight more unique features of this benchmark, as it currently appears highly replaceable. \n3. Limited exploration on \"optimal thinking\". The explored methods are not detailed in the summary, but they might be simple approaches like static routing or basic prompting. More advanced techniques exist, such as dynamic router models that predict the required reasoning depth, or using the LLM itself to decide if it needs to \"think\" (i.e., use a COT prompt). Without comparing against these more sophisticated strategies, the conclusion that the trade-off is unavoidable seems premature.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)