hoololi/llm-next-prime

LLM Next Prime Dataset

This dataset contains factual observations from experiments recording how different large language models answer next-prime questions in three execution modes.

Each question has the form:

What is the smallest prime number that is strictly greater than n?

One dataset row corresponds to:

one input number × one model × one mode

The goal is not to provide a definitive benchmark ranking. The dataset is intended as raw experimental material for analyzing model behavior, tool use, latency, cost, and failure modes on a simple but objectively checkable numerical task.

Quick start

from datasets import load_dataset

df = load_dataset("hoololi/llm-next-prime", split="train").to_pandas()
df.head()

Or with pandas:

import pandas as pd

df = pd.read_parquet("next_prime_20260722.parquet")

Experiment setup

Models were queried through OpenRouter. For each input number and each model, the experiment was run in three modes:

1. raw: the model answers the question directly.
2. prompted: the model receives a more explicit prime-number-oriented system prompt.
3. tool: the model may call a deterministic local next_prime(n) tool, then produce a final answer.

The correct result was computed locally with Python prime-checking utilities and was not included in the prompt.

Dataset composition

The published file contains:

100 input cases × 22 models × 3 modes = 6600 rows

The 100 input numbers were generated with a fixed seed and balanced across magnitude ranges:

• 1–99
• 100–999
• 1,000–9,999
• 10,000–99,999
• 100,000–999,999

There are 20 cases per range.

Important fields

Core task fields:

• task_type: experiment type, here next_prime.
• operation_id: case identifier.
• operation: question sent to the model.
• correct_result: smallest prime strictly greater than the input number.
• input_number: value of n.
• magnitude_group: order-of-magnitude group, such as 10^3.
• distance_to_next_prime: correct_result - input_number.
• input_is_prime: whether the input number itself is prime.

Model and mode fields:

• model_family
• model
• mode
• model_provider
• model_label

Answer/evaluation fields:

• answer_text: final model response text.
• extracted_answer: numeric answer extracted from answer_text.
• final_result_correct: whether the final extracted answer equals correct_result.
• run_success: whether the model call produced a usable final response.
• run_error: captured error message, if any.
• latency_seconds
• cost
• input_tokens, output_tokens, total_tokens

Tool fields:

• tool_called
• tool_call_count
• tool_expression: tool input, represented as text for compatibility with the arithmetic dataset.
• tool_result
• tool_result_correct
• tool_error
• tool_error_type
• tool_calls: nested details of tool calls when available.

Notes on interpretation

final_result_correct is strict: it evaluates the final model answer after numeric extraction. In tool mode, this can differ from tool_result_correct.

For example, a model may successfully call the tool and receive the correct result, but then fail to produce a final answer or produce a final answer that is not cleanly extractable. These cases are intentionally preserved because they reveal end-to-end agent reliability issues.

Useful derived metrics include:

• final-answer accuracy: mean of final_result_correct;
• tool-output accuracy: mean of tool_result_correct for tool-mode rows;
• recoverable tool accuracy: whether either the final answer or the tool output was correct.

Error message redaction

Provider error messages were redacted before publication to remove account-specific identifiers such as OpenRouter user IDs. Provider names, error codes, rate-limit messages, and endpoint errors are otherwise preserved when available, because they are useful for analyzing run failures.

Suggested analysis questions

This dataset can be used to investigate:

• accuracy by input magnitude and mode;
• end-to-end accuracy versus accuracy among successful responses only;
• whether tool use removes the magnitude-dependent accuracy drop;
• latency and cost by model family and mode;
• run failure rates by model and provider;
• tool-call success versus final-answer success;
• wrong-answer distance from the correct next prime;
• cost/latency/accuracy trade-offs.

Limitations

• Results are raw observations from a specific provider setup, model list, prompt design, tool interface, and run date.
• Model/provider versions and routing can change over time.
• Cost and token metadata depend on provider reporting and may be missing for some rows.
• The dataset is not intended as a definitive benchmark leaderboard.
• Tool mode evaluates an agentic pipeline, not just the mathematical correctness of the local tool.

License

Released under CC BY 4.0.