danielrosehill/Open-Router-API-Pricing-Analysis huggingface dataset:
Model: Qwen: Qwen3 30B A3B
Vendor: Qwen
Hugging Face ID: Qwen/Qwen3-30B-A3B
Description: Qwen3, the latest generation in the Qwen large language model series, features both dense and mixture-of-experts (MoE) architectures to excel in reasoning, multilingual support, and advanced agent tasks. Its unique ability to switch seamlessly between a thinking mode for complex reasoning and a non-thinking mode for efficient dialogue ensures versatile, high-quality performance.

Significantly outperforming prior models like QwQ and Qwen2.5, Qwen3 delivers superior mathematics, coding, commonsense reasoning, creative writing, and interactive dialogue capabilities. The Qwen3-30B-A3B variant includes 30.5 billion parameters (3.3 billion activated), 48 layers, 128 experts (8 activated per task), and supports up to 131K token contexts with YaRN, setting a new standard among open-source models.
Context length: 40960 tokens
Tokenizer: Qwen3
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 40960.0
Prompt price per 1M tokens (USD): 0.06
Completion price per 1M tokens (USD): 0.22
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.14
Output/input cost ratio: 3.67
Supported parameters: frequency_penalty, include_reasoning, logit_bias, max_tokens, min_p, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: Qwen: Qwen3 8B
Vendor: Qwen
Hugging Face ID: Qwen/Qwen3-8B
Description: Qwen3-8B is a dense 8.2B parameter causal language model from the Qwen3 series, designed for both reasoning-heavy tasks and efficient dialogue. It supports seamless switching between "thinking" mode for math, coding, and logical inference, and "non-thinking" mode for general conversation. The model is fine-tuned for instruction-following, agent integration, creative writing, and multilingual use across 100+ languages and dialects. It natively supports a 32K token context window and can extend to 131K tokens with YaRN scaling.
Context length: 128000 tokens
Tokenizer: Qwen3
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 20000.0
Prompt price per 1M tokens (USD): 0.035
Completion price per 1M tokens (USD): 0.1379999999999999
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.0865
Output/input cost ratio: 3.94
Supported parameters: frequency_penalty, include_reasoning, logit_bias, logprobs, max_tokens, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_logprobs, top_p
Model: Qwen: Qwen3 14B (free)
Vendor: Qwen
Hugging Face ID: Qwen/Qwen3-14B
Description: Qwen3-14B is a dense 14.8B parameter causal language model from the Qwen3 series, designed for both complex reasoning and efficient dialogue. It supports seamless switching between a "thinking" mode for tasks like math, programming, and logical inference, and a "non-thinking" mode for general-purpose conversation. The model is fine-tuned for instruction-following, agent tool use, creative writing, and multilingual tasks across 100+ languages and dialects. It natively handles 32K token contexts and can extend to 131K tokens using YaRN-based scaling.
Context length: 40960 tokens
Tokenizer: Qwen3
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.0
Completion price per 1M tokens (USD): 0.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.0
Output/input cost ratio: 0.0
Supported parameters: frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, repetition_penalty, seed, stop, temperature, top_k, top_p
Model: Qwen: Qwen3 14B
Vendor: Qwen
Hugging Face ID: Qwen/Qwen3-14B
Description: Qwen3-14B is a dense 14.8B parameter causal language model from the Qwen3 series, designed for both complex reasoning and efficient dialogue. It supports seamless switching between a "thinking" mode for tasks like math, programming, and logical inference, and a "non-thinking" mode for general-purpose conversation. The model is fine-tuned for instruction-following, agent tool use, creative writing, and multilingual tasks across 100+ languages and dialects. It natively handles 32K token contexts and can extend to 131K tokens using YaRN-based scaling.
Context length: 40960 tokens
Tokenizer: Qwen3
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 40960.0
Prompt price per 1M tokens (USD): 0.0499999999999999
Completion price per 1M tokens (USD): 0.22
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.135
Output/input cost ratio: 4.4
Supported parameters: frequency_penalty, include_reasoning, max_tokens, min_p, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: Qwen: Qwen3 32B
Vendor: Qwen
Hugging Face ID: Qwen/Qwen3-32B
Description: Qwen3-32B is a dense 32.8B parameter causal language model from the Qwen3 series, optimized for both complex reasoning and efficient dialogue. It supports seamless switching between a "thinking" mode for tasks like math, coding, and logical inference, and a "non-thinking" mode for faster, general-purpose conversation. The model demonstrates strong performance in instruction-following, agent tool use, creative writing, and multilingual tasks across 100+ languages and dialects. It natively handles 32K token contexts and can extend to 131K tokens using YaRN-based scaling. 
Context length: 40960 tokens
Tokenizer: Qwen3
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 40960.0
Prompt price per 1M tokens (USD): 0.0499999999999999
Completion price per 1M tokens (USD): 0.1999999999999999
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.1249999999999999
Output/input cost ratio: 4.0
Supported parameters: frequency_penalty, include_reasoning, logprobs, max_tokens, min_p, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_logprobs, top_p
Model: Qwen: Qwen3 235B A22B (free)
Vendor: Qwen
Hugging Face ID: Qwen/Qwen3-235B-A22B
Description: Qwen3-235B-A22B is a 235B parameter mixture-of-experts (MoE) model developed by Qwen, activating 22B parameters per forward pass. It supports seamless switching between a "thinking" mode for complex reasoning, math, and code tasks, and a "non-thinking" mode for general conversational efficiency. The model demonstrates strong reasoning ability, multilingual support (100+ languages and dialects), advanced instruction-following, and agent tool-calling capabilities. It natively handles a 32K token context window and extends up to 131K tokens using YaRN-based scaling.
Context length: 40960 tokens
Tokenizer: Qwen3
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.0
Completion price per 1M tokens (USD): 0.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.0
Output/input cost ratio: 0.0
Supported parameters: frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: Qwen: Qwen3 235B A22B
Vendor: Qwen
Hugging Face ID: Qwen/Qwen3-235B-A22B
Description: Qwen3-235B-A22B is a 235B parameter mixture-of-experts (MoE) model developed by Qwen, activating 22B parameters per forward pass. It supports seamless switching between a "thinking" mode for complex reasoning, math, and code tasks, and a "non-thinking" mode for general conversational efficiency. The model demonstrates strong reasoning ability, multilingual support (100+ languages and dialects), advanced instruction-following, and agent tool-calling capabilities. It natively handles a 32K token context window and extends up to 131K tokens using YaRN-based scaling.
Context length: 40960 tokens
Tokenizer: Qwen3
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 40960.0
Prompt price per 1M tokens (USD): 0.18
Completion price per 1M tokens (USD): 0.54
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.36
Output/input cost ratio: 3.0
Supported parameters: frequency_penalty, include_reasoning, logit_bias, logprobs, max_tokens, min_p, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_logprobs, top_p
Model: TNG: DeepSeek R1T Chimera (free)
Vendor: TNG Technology
Hugging Face ID: tngtech/DeepSeek-R1T-Chimera
Description: DeepSeek-R1T-Chimera is created by merging DeepSeek-R1 and DeepSeek-V3 (0324), combining the reasoning capabilities of R1 with the token efficiency improvements of V3. It is based on a DeepSeek-MoE Transformer architecture and is optimized for general text generation tasks.

The model merges pretrained weights from both source models to balance performance across reasoning, efficiency, and instruction-following tasks. It is released under the MIT license and intended for research and commercial use.
Context length: 163840 tokens
Tokenizer: DeepSeek
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.0
Completion price per 1M tokens (USD): 0.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.0
Output/input cost ratio: 0.0
Supported parameters: frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, repetition_penalty, seed, stop, temperature, top_k, top_p
Model: TNG: DeepSeek R1T Chimera
Vendor: TNG Technology
Hugging Face ID: tngtech/DeepSeek-R1T-Chimera
Description: DeepSeek-R1T-Chimera is created by merging DeepSeek-R1 and DeepSeek-V3 (0324), combining the reasoning capabilities of R1 with the token efficiency improvements of V3. It is based on a DeepSeek-MoE Transformer architecture and is optimized for general text generation tasks.

The model merges pretrained weights from both source models to balance performance across reasoning, efficiency, and instruction-following tasks. It is released under the MIT license and intended for research and commercial use.
Context length: 163840 tokens
Tokenizer: DeepSeek
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 163840.0
Prompt price per 1M tokens (USD): 0.3
Completion price per 1M tokens (USD): 1.2
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.75
Output/input cost ratio: 4.0
Supported parameters: frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, top_k, top_p
Model: Microsoft: MAI DS R1 (free)
Vendor: Microsoft
Hugging Face ID: microsoft/MAI-DS-R1
Description: MAI-DS-R1 is a post-trained variant of DeepSeek-R1 developed by the Microsoft AI team to improve the model’s responsiveness on previously blocked topics while enhancing its safety profile. Built on top of DeepSeek-R1’s reasoning foundation, it integrates 110k examples from the Tulu-3 SFT dataset and 350k internally curated multilingual safety-alignment samples. The model retains strong reasoning, coding, and problem-solving capabilities, while unblocking a wide range of prompts previously restricted in R1.

MAI-DS-R1 demonstrates improved performance on harm mitigation benchmarks and maintains competitive results across general reasoning tasks. It surpasses R1-1776 in satisfaction metrics for blocked queries and reduces leakage in harmful content categories. The model is based on a transformer MoE architecture and is suitable for general-purpose use cases, excluding high-stakes domains such as legal, medical, or autonomous systems.
Context length: 163840 tokens
Tokenizer: DeepSeek
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.0
Completion price per 1M tokens (USD): 0.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.0
Output/input cost ratio: 0.0
Supported parameters: frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, repetition_penalty, seed, stop, temperature, top_k, top_p
Model: Microsoft: MAI DS R1
Vendor: Microsoft
Hugging Face ID: microsoft/MAI-DS-R1
Description: MAI-DS-R1 is a post-trained variant of DeepSeek-R1 developed by the Microsoft AI team to improve the model’s responsiveness on previously blocked topics while enhancing its safety profile. Built on top of DeepSeek-R1’s reasoning foundation, it integrates 110k examples from the Tulu-3 SFT dataset and 350k internally curated multilingual safety-alignment samples. The model retains strong reasoning, coding, and problem-solving capabilities, while unblocking a wide range of prompts previously restricted in R1.

MAI-DS-R1 demonstrates improved performance on harm mitigation benchmarks and maintains competitive results across general reasoning tasks. It surpasses R1-1776 in satisfaction metrics for blocked queries and reduces leakage in harmful content categories. The model is based on a transformer MoE architecture and is suitable for general-purpose use cases, excluding high-stakes domains such as legal, medical, or autonomous systems.
Context length: 163840 tokens
Tokenizer: DeepSeek
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 163840.0
Prompt price per 1M tokens (USD): 0.3
Completion price per 1M tokens (USD): 1.2
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.75
Output/input cost ratio: 4.0
Supported parameters: frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, top_k, top_p
Model: OpenAI: o4 Mini High
Vendor: OpenAI
Description: OpenAI o4-mini-high is the same model as [o4-mini](/openai/o4-mini) with reasoning_effort set to high. 

OpenAI o4-mini is a compact reasoning model in the o-series, optimized for fast, cost-efficient performance while retaining strong multimodal and agentic capabilities. It supports tool use and demonstrates competitive reasoning and coding performance across benchmarks like AIME (99.5% with Python) and SWE-bench, outperforming its predecessor o3-mini and even approaching o3 in some domains.

Despite its smaller size, o4-mini exhibits high accuracy in STEM tasks, visual problem solving (e.g., MathVista, MMMU), and code editing. It is especially well-suited for high-throughput scenarios where latency or cost is critical. Thanks to its efficient architecture and refined reinforcement learning training, o4-mini can chain tools, generate structured outputs, and solve multi-step tasks with minimal delay—often in under a minute.
Context length: 200000 tokens
Tokenizer: Other
Modality: text+image->text
Input modalities: image, text, file
Output modalities: text
Max completion tokens (top provider): 100000.0
Prompt price per 1M tokens (USD): 1.1
Completion price per 1M tokens (USD): 4.4
Image price (USD): 0.0008415
Request price (USD): 0.0
Median price per 1M tokens (USD): 2.75
Output/input cost ratio: 4.0
Supported parameters: include_reasoning, max_tokens, reasoning, response_format, seed, structured_outputs, tool_choice, tools
Model: OpenAI: o3
Vendor: OpenAI
Description: o3 is a well-rounded and powerful model across domains. It sets a new standard for math, science, coding, and visual reasoning tasks. It also excels at technical writing and instruction-following. Use it to think through multi-step problems that involve analysis across text, code, and images. 
Context length: 200000 tokens
Tokenizer: GPT
Modality: text+image->text
Input modalities: image, text, file
Output modalities: text
Max completion tokens (top provider): 100000.0
Prompt price per 1M tokens (USD): 2.0
Completion price per 1M tokens (USD): 8.0
Image price (USD): 0.00153
Request price (USD): 0.0
Median price per 1M tokens (USD): 5.0
Output/input cost ratio: 4.0
Supported parameters: include_reasoning, max_tokens, reasoning, response_format, seed, structured_outputs, tool_choice, tools
Model: OpenAI: o4 Mini
Vendor: OpenAI
Description: OpenAI o4-mini is a compact reasoning model in the o-series, optimized for fast, cost-efficient performance while retaining strong multimodal and agentic capabilities. It supports tool use and demonstrates competitive reasoning and coding performance across benchmarks like AIME (99.5% with Python) and SWE-bench, outperforming its predecessor o3-mini and even approaching o3 in some domains.

Despite its smaller size, o4-mini exhibits high accuracy in STEM tasks, visual problem solving (e.g., MathVista, MMMU), and code editing. It is especially well-suited for high-throughput scenarios where latency or cost is critical. Thanks to its efficient architecture and refined reinforcement learning training, o4-mini can chain tools, generate structured outputs, and solve multi-step tasks with minimal delay—often in under a minute.
Context length: 200000 tokens
Tokenizer: GPT
Modality: text+image->text
Input modalities: image, text, file
Output modalities: text
Max completion tokens (top provider): 100000.0
Prompt price per 1M tokens (USD): 1.1
Completion price per 1M tokens (USD): 4.4
Image price (USD): 0.0008415
Request price (USD): 0.0
Median price per 1M tokens (USD): 2.75
Output/input cost ratio: 4.0
Supported parameters: include_reasoning, max_tokens, reasoning, response_format, seed, structured_outputs, tool_choice, tools
Model: Qwen: Qwen2.5 Coder 7B Instruct
Vendor: Qwen
Hugging Face ID: Qwen/Qwen2.5-Coder-7B-Instruct
Description: Qwen2.5-Coder-7B-Instruct is a 7B parameter instruction-tuned language model optimized for code-related tasks such as code generation, reasoning, and bug fixing. Based on the Qwen2.5 architecture, it incorporates enhancements like RoPE, SwiGLU, RMSNorm, and GQA attention with support for up to 128K tokens using YaRN-based extrapolation. It is trained on a large corpus of source code, synthetic data, and text-code grounding, providing robust performance across programming languages and agentic coding workflows.

This model is part of the Qwen2.5-Coder family and offers strong compatibility with tools like vLLM for efficient deployment. Released under the Apache 2.0 license.
Context length: 32768 tokens
Tokenizer: Qwen
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.03
Completion price per 1M tokens (USD): 0.09
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.06
Output/input cost ratio: 3.0
Supported parameters: frequency_penalty, max_tokens, presence_penalty, repetition_penalty, response_format, structured_outputs, temperature, top_k, top_p
Model: OpenAI: GPT-4.1
Vendor: OpenAI
Description: GPT-4.1 is a flagship large language model optimized for advanced instruction following, real-world software engineering, and long-context reasoning. It supports a 1 million token context window and outperforms GPT-4o and GPT-4.5 across coding (54.6% SWE-bench Verified), instruction compliance (87.4% IFEval), and multimodal understanding benchmarks. It is tuned for precise code diffs, agent reliability, and high recall in large document contexts, making it ideal for agents, IDE tooling, and enterprise knowledge retrieval.
Context length: 1047576 tokens
Tokenizer: GPT
Modality: text+image->text
Input modalities: image, text, file
Output modalities: text
Max completion tokens (top provider): 32768.0
Prompt price per 1M tokens (USD): 2.0
Completion price per 1M tokens (USD): 8.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 5.0
Output/input cost ratio: 4.0
Supported parameters: max_tokens, response_format, seed, structured_outputs, tool_choice, tools
Model: OpenAI: GPT-4.1 Mini
Vendor: OpenAI
Description: GPT-4.1 Mini is a mid-sized model delivering performance competitive with GPT-4o at substantially lower latency and cost. It retains a 1 million token context window and scores 45.1% on hard instruction evals, 35.8% on MultiChallenge, and 84.1% on IFEval. Mini also shows strong coding ability (e.g., 31.6% on Aider’s polyglot diff benchmark) and vision understanding, making it suitable for interactive applications with tight performance constraints.
Context length: 1047576 tokens
Tokenizer: GPT
Modality: text+image->text
Input modalities: image, text, file
Output modalities: text
Max completion tokens (top provider): 32768.0
Prompt price per 1M tokens (USD): 0.3999999999999999
Completion price per 1M tokens (USD): 1.6
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 1.0
Output/input cost ratio: 4.0
Supported parameters: max_tokens, response_format, seed, structured_outputs, tool_choice, tools
Model: OpenAI: GPT-4.1 Nano
Vendor: OpenAI
Description: For tasks that demand low latency, GPT‑4.1 nano is the fastest and cheapest model in the GPT-4.1 series. It delivers exceptional performance at a small size with its 1 million token context window, and scores 80.1% on MMLU, 50.3% on GPQA, and 9.8% on Aider polyglot coding – even higher than GPT‑4o mini. It’s ideal for tasks like classification or autocompletion.
Context length: 1047576 tokens
Tokenizer: GPT
Modality: text+image->text
Input modalities: image, text, file
Output modalities: text
Max completion tokens (top provider): 32768.0
Prompt price per 1M tokens (USD): 0.0999999999999999
Completion price per 1M tokens (USD): 0.3999999999999999
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.2499999999999999
Output/input cost ratio: 4.0
Supported parameters: max_tokens, response_format, seed, structured_outputs, tool_choice, tools
Model: EleutherAI: Llemma 7b
Vendor: EleutherAI
Hugging Face ID: EleutherAI/llemma_7b
Description: Llemma 7B is a language model for mathematics. It was initialized with Code Llama 7B weights, and trained on the Proof-Pile-2 for 200B tokens. Llemma models are particularly strong at chain-of-thought mathematical reasoning and using computational tools for mathematics, such as Python and formal theorem provers.
Context length: 4096 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 4096.0
Prompt price per 1M tokens (USD): 0.7999999999999999
Completion price per 1M tokens (USD): 1.2
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 1.0
Output/input cost ratio: 1.5
Supported parameters: frequency_penalty, max_tokens, min_p, presence_penalty, repetition_penalty, seed, stop, temperature, top_k, top_p
Model: AlfredPros: CodeLLaMa 7B Instruct Solidity
Vendor: AlfredPros
Hugging Face ID: AlfredPros/CodeLlama-7b-Instruct-Solidity
Description: A finetuned 7 billion parameters Code LLaMA - Instruct model to generate Solidity smart contract using 4-bit QLoRA finetuning provided by PEFT library.
Context length: 4096 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 4096.0
Prompt price per 1M tokens (USD): 0.7999999999999999
Completion price per 1M tokens (USD): 1.2
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 1.0
Output/input cost ratio: 1.5
Supported parameters: frequency_penalty, max_tokens, min_p, presence_penalty, repetition_penalty, seed, stop, temperature, top_k, top_p
Model: ArliAI: QwQ 32B RpR v1 (free)
Vendor: Arli AI
Hugging Face ID: ArliAI/QwQ-32B-ArliAI-RpR-v1
Description: QwQ-32B-ArliAI-RpR-v1 is a 32B parameter model fine-tuned from Qwen/QwQ-32B using a curated creative writing and roleplay dataset originally developed for the RPMax series. It is designed to maintain coherence and reasoning across long multi-turn conversations by introducing explicit reasoning steps per dialogue turn, generated and refined using the base model itself.

The model was trained using RS-QLORA+ on 8K sequence lengths and supports up to 128K context windows (with practical performance around 32K). It is optimized for creative roleplay and dialogue generation, with an emphasis on minimizing cross-context repetition while preserving stylistic diversity.
Context length: 32768 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.0
Completion price per 1M tokens (USD): 0.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.0
Output/input cost ratio: 0.0
Supported parameters: frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, repetition_penalty, seed, stop, temperature, top_k, top_p
Model: ArliAI: QwQ 32B RpR v1
Vendor: Arli AI
Hugging Face ID: ArliAI/QwQ-32B-ArliAI-RpR-v1
Description: QwQ-32B-ArliAI-RpR-v1 is a 32B parameter model fine-tuned from Qwen/QwQ-32B using a curated creative writing and roleplay dataset originally developed for the RPMax series. It is designed to maintain coherence and reasoning across long multi-turn conversations by introducing explicit reasoning steps per dialogue turn, generated and refined using the base model itself.

The model was trained using RS-QLORA+ on 8K sequence lengths and supports up to 128K context windows (with practical performance around 32K). It is optimized for creative roleplay and dialogue generation, with an emphasis on minimizing cross-context repetition while preserving stylistic diversity.
Context length: 32768 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 32768.0
Prompt price per 1M tokens (USD): 0.03
Completion price per 1M tokens (USD): 0.11
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.07
Output/input cost ratio: 3.67
Supported parameters: frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, top_k, top_p
Model: Agentica: Deepcoder 14B Preview (free)
Vendor: Agentica
Hugging Face ID: agentica-org/DeepCoder-14B-Preview
Description: DeepCoder-14B-Preview is a 14B parameter code generation model fine-tuned from DeepSeek-R1-Distill-Qwen-14B using reinforcement learning with GRPO+ and iterative context lengthening. It is optimized for long-context program synthesis and achieves strong performance across coding benchmarks, including 60.6% on LiveCodeBench v5, competitive with models like o3-Mini
Context length: 96000 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.0
Completion price per 1M tokens (USD): 0.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.0
Output/input cost ratio: 0.0
Supported parameters: frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, repetition_penalty, seed, stop, temperature, top_k, top_p
Model: Agentica: Deepcoder 14B Preview
Vendor: Agentica
Hugging Face ID: agentica-org/DeepCoder-14B-Preview
Description: DeepCoder-14B-Preview is a 14B parameter code generation model fine-tuned from DeepSeek-R1-Distill-Qwen-14B using reinforcement learning with GRPO+ and iterative context lengthening. It is optimized for long-context program synthesis and achieves strong performance across coding benchmarks, including 60.6% on LiveCodeBench v5, competitive with models like o3-Mini
Context length: 96000 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.015
Completion price per 1M tokens (USD): 0.015
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.015
Output/input cost ratio: 1.0
Supported parameters: frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, repetition_penalty, seed, stop, temperature, top_k, top_p
Model: xAI: Grok 3 Mini Beta
Vendor: xAI
Description: Grok 3 Mini is a lightweight, smaller thinking model. Unlike traditional models that generate answers immediately, Grok 3 Mini thinks before responding. It’s ideal for reasoning-heavy tasks that don’t demand extensive domain knowledge, and shines in math-specific and quantitative use cases, such as solving challenging puzzles or math problems.

Transparent "thinking" traces accessible. Defaults to low reasoning, can boost with setting `reasoning: { effort: "high" }`

Note: That there are two xAI endpoints for this model. By default when using this model we will always route you to the base endpoint. If you want the fast endpoint you can add `provider: { sort: throughput}`, to sort by throughput instead. 

Context length: 131072 tokens
Tokenizer: Grok
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.3
Completion price per 1M tokens (USD): 0.5
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.4
Output/input cost ratio: 1.67
Supported parameters: include_reasoning, logprobs, max_tokens, reasoning, response_format, seed, stop, temperature, tool_choice, tools, top_logprobs, top_p
Model: xAI: Grok 3 Beta
Vendor: xAI
Description: Grok 3 is the latest model from xAI. It's their flagship model that excels at enterprise use cases like data extraction, coding, and text summarization. Possesses deep domain knowledge in finance, healthcare, law, and science.

Excels in structured tasks and benchmarks like GPQA, LCB, and MMLU-Pro where it outperforms Grok 3 Mini even on high thinking. 

Note: That there are two xAI endpoints for this model. By default when using this model we will always route you to the base endpoint. If you want the fast endpoint you can add `provider: { sort: throughput}`, to sort by throughput instead. 

Context length: 131072 tokens
Tokenizer: Grok
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 3.0
Completion price per 1M tokens (USD): 15.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 9.0
Output/input cost ratio: 5.0
Supported parameters: frequency_penalty, logprobs, max_tokens, presence_penalty, response_format, seed, stop, temperature, tool_choice, tools, top_logprobs, top_p
Model: NVIDIA: Llama 3.1 Nemotron Ultra 253B v1
Vendor: NVIDIA
Hugging Face ID: nvidia/Llama-3_1-Nemotron-Ultra-253B-v1
Description: Llama-3.1-Nemotron-Ultra-253B-v1 is a large language model (LLM) optimized for advanced reasoning, human-interactive chat, retrieval-augmented generation (RAG), and tool-calling tasks. Derived from Meta’s Llama-3.1-405B-Instruct, it has been significantly customized using Neural Architecture Search (NAS), resulting in enhanced efficiency, reduced memory usage, and improved inference latency. The model supports a context length of up to 128K tokens and can operate efficiently on an 8x NVIDIA H100 node.

Note: you must include `detailed thinking on` in the system prompt to enable reasoning. Please see [Usage Recommendations](https://huggingface.co/nvidia/Llama-3_1-Nemotron-Ultra-253B-v1#quick-start-and-usage-recommendations) for more.
Context length: 131072 tokens
Tokenizer: Llama3
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.6
Completion price per 1M tokens (USD): 1.7999999999999998
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 1.2
Output/input cost ratio: 3.0
Supported parameters: frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, repetition_penalty, response_format, structured_outputs, temperature, top_k, top_p
Model: Meta: Llama 4 Maverick (free)
Vendor: Meta
Hugging Face ID: meta-llama/Llama-4-Maverick-17B-128E-Instruct
Description: Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active parameters per forward pass (400B total). It supports multilingual text and image input, and produces multilingual text and code output across 12 supported languages. Optimized for vision-language tasks, Maverick is instruction-tuned for assistant-like behavior, image reasoning, and general-purpose multimodal interaction.

Maverick features early fusion for native multimodality and a 1 million token context window. It was trained on a curated mixture of public, licensed, and Meta-platform data, covering ~22 trillion tokens, with a knowledge cutoff in August 2024. Released on April 5, 2025 under the Llama 4 Community License, Maverick is suited for research and commercial applications requiring advanced multimodal understanding and high model throughput.
Context length: 128000 tokens
Tokenizer: Llama4
Modality: text+image->text
Input modalities: text, image
Output modalities: text
Max completion tokens (top provider): 4028.0
Prompt price per 1M tokens (USD): 0.0
Completion price per 1M tokens (USD): 0.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.0
Output/input cost ratio: 0.0
Supported parameters: max_tokens, repetition_penalty, response_format, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: Meta: Llama 4 Maverick
Vendor: Meta
Hugging Face ID: meta-llama/Llama-4-Maverick-17B-128E-Instruct
Description: Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active parameters per forward pass (400B total). It supports multilingual text and image input, and produces multilingual text and code output across 12 supported languages. Optimized for vision-language tasks, Maverick is instruction-tuned for assistant-like behavior, image reasoning, and general-purpose multimodal interaction.

Maverick features early fusion for native multimodality and a 1 million token context window. It was trained on a curated mixture of public, licensed, and Meta-platform data, covering ~22 trillion tokens, with a knowledge cutoff in August 2024. Released on April 5, 2025 under the Llama 4 Community License, Maverick is suited for research and commercial applications requiring advanced multimodal understanding and high model throughput.
Context length: 1048576 tokens
Tokenizer: Llama4
Modality: text+image->text
Input modalities: text, image
Output modalities: text
Max completion tokens (top provider): 16384.0
Prompt price per 1M tokens (USD): 0.15
Completion price per 1M tokens (USD): 0.6
Image price (USD): 0.0006684
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.375
Output/input cost ratio: 4.0
Supported parameters: frequency_penalty, logit_bias, max_tokens, min_p, presence_penalty, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: Meta: Llama 4 Scout (free)
Vendor: Meta
Hugging Face ID: meta-llama/Llama-4-Scout-17B-16E-Instruct
Description: Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input (text and image) and multilingual output (text and code) across 12 supported languages. Designed for assistant-style interaction and visual reasoning, Scout uses 16 experts per forward pass and features a context length of 10 million tokens, with a training corpus of ~40 trillion tokens.

Built for high efficiency and local or commercial deployment, Llama 4 Scout incorporates early fusion for seamless modality integration. It is instruction-tuned for use in multilingual chat, captioning, and image understanding tasks. Released under the Llama 4 Community License, it was last trained on data up to August 2024 and launched publicly on April 5, 2025.
Context length: 128000 tokens
Tokenizer: Llama4
Modality: text+image->text
Input modalities: text, image
Output modalities: text
Max completion tokens (top provider): 4028.0
Prompt price per 1M tokens (USD): 0.0
Completion price per 1M tokens (USD): 0.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.0
Output/input cost ratio: 0.0
Supported parameters: max_tokens, repetition_penalty, response_format, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: Meta: Llama 4 Scout
Vendor: Meta
Hugging Face ID: meta-llama/Llama-4-Scout-17B-16E-Instruct
Description: Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input (text and image) and multilingual output (text and code) across 12 supported languages. Designed for assistant-style interaction and visual reasoning, Scout uses 16 experts per forward pass and features a context length of 10 million tokens, with a training corpus of ~40 trillion tokens.

Built for high efficiency and local or commercial deployment, Llama 4 Scout incorporates early fusion for seamless modality integration. It is instruction-tuned for use in multilingual chat, captioning, and image understanding tasks. Released under the Llama 4 Community License, it was last trained on data up to August 2024 and launched publicly on April 5, 2025.
Context length: 327680 tokens
Tokenizer: Llama4
Modality: text+image->text
Input modalities: text, image
Output modalities: text
Max completion tokens (top provider): 16384.0
Prompt price per 1M tokens (USD): 0.08
Completion price per 1M tokens (USD): 0.3
Image price (USD): 0.0003342
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.19
Output/input cost ratio: 3.75
Supported parameters: frequency_penalty, logit_bias, max_tokens, min_p, presence_penalty, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: Qwen: Qwen2.5 VL 32B Instruct (free)
Vendor: Qwen
Hugging Face ID: Qwen/Qwen2.5-VL-32B-Instruct
Description: Qwen2.5-VL-32B is a multimodal vision-language model fine-tuned through reinforcement learning for enhanced mathematical reasoning, structured outputs, and visual problem-solving capabilities. It excels at visual analysis tasks, including object recognition, textual interpretation within images, and precise event localization in extended videos. Qwen2.5-VL-32B demonstrates state-of-the-art performance across multimodal benchmarks such as MMMU, MathVista, and VideoMME, while maintaining strong reasoning and clarity in text-based tasks like MMLU, mathematical problem-solving, and code generation.
Context length: 16384 tokens
Tokenizer: Qwen
Modality: text+image->text
Input modalities: text, image
Output modalities: text
Prompt price per 1M tokens (USD): 0.0
Completion price per 1M tokens (USD): 0.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.0
Output/input cost ratio: 0.0
Supported parameters: frequency_penalty, max_tokens, presence_penalty, repetition_penalty, seed, stop, temperature, top_k, top_p
Model: Qwen: Qwen2.5 VL 32B Instruct
Vendor: Qwen
Hugging Face ID: Qwen/Qwen2.5-VL-32B-Instruct
Description: Qwen2.5-VL-32B is a multimodal vision-language model fine-tuned through reinforcement learning for enhanced mathematical reasoning, structured outputs, and visual problem-solving capabilities. It excels at visual analysis tasks, including object recognition, textual interpretation within images, and precise event localization in extended videos. Qwen2.5-VL-32B demonstrates state-of-the-art performance across multimodal benchmarks such as MMMU, MathVista, and VideoMME, while maintaining strong reasoning and clarity in text-based tasks like MMLU, mathematical problem-solving, and code generation.
Context length: 16384 tokens
Tokenizer: Qwen
Modality: text+image->text
Input modalities: text, image
Output modalities: text
Max completion tokens (top provider): 16384.0
Prompt price per 1M tokens (USD): 0.0499999999999999
Completion price per 1M tokens (USD): 0.22
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.135
Output/input cost ratio: 4.4
Supported parameters: frequency_penalty, logit_bias, logprobs, max_tokens, min_p, presence_penalty, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, top_k, top_logprobs, top_p
Model: DeepSeek: DeepSeek V3 0324 (free)
Vendor: DeepSeek
Hugging Face ID: deepseek-ai/DeepSeek-V3-0324
Description: DeepSeek V3, a 685B-parameter, mixture-of-experts model, is the latest iteration of the flagship chat model family from the DeepSeek team.

It succeeds the [DeepSeek V3](/deepseek/deepseek-chat-v3) model and performs really well on a variety of tasks.
Context length: 163840 tokens
Tokenizer: DeepSeek
Modality: text->text
Input modalities: text
Output modalities: text
Prompt price per 1M tokens (USD): 0.0
Completion price per 1M tokens (USD): 0.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.0
Output/input cost ratio: 0.0
Supported parameters: frequency_penalty, max_tokens, presence_penalty, repetition_penalty, seed, stop, temperature, tool_choice, tools, top_k, top_p
Model: DeepSeek: DeepSeek V3 0324
Vendor: DeepSeek
Hugging Face ID: deepseek-ai/DeepSeek-V3-0324
Description: DeepSeek V3, a 685B-parameter, mixture-of-experts model, is the latest iteration of the flagship chat model family from the DeepSeek team.

It succeeds the [DeepSeek V3](/deepseek/deepseek-chat-v3) model and performs really well on a variety of tasks.
Context length: 163840 tokens
Tokenizer: DeepSeek
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 163840.0
Prompt price per 1M tokens (USD): 0.24
Completion price per 1M tokens (USD): 0.84
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.54
Output/input cost ratio: 3.5
Supported parameters: frequency_penalty, logit_bias, logprobs, max_tokens, min_p, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_logprobs, top_p
Model: OpenAI: o1-pro
Vendor: OpenAI
Description: The o1 series of models are trained with reinforcement learning to think before they answer and perform complex reasoning. The o1-pro model uses more compute to think harder and provide consistently better answers.
Context length: 200000 tokens
Tokenizer: GPT
Modality: text+image->text
Input modalities: text, image, file
Output modalities: text
Max completion tokens (top provider): 100000.0
Prompt price per 1M tokens (USD): 150.0
Completion price per 1M tokens (USD): 600.0
Image price (USD): 0.21675
Request price (USD): 0.0
Median price per 1M tokens (USD): 375.0
Output/input cost ratio: 4.0
Supported parameters: include_reasoning, max_tokens, reasoning, response_format, seed, structured_outputs
Model: Mistral: Mistral Small 3.1 24B (free)
Vendor: Mistral AI
Hugging Face ID: mistralai/Mistral-Small-3.1-24B-Instruct-2503
Description: Mistral Small 3.1 24B Instruct is an upgraded variant of Mistral Small 3 (2501), featuring 24 billion parameters with advanced multimodal capabilities. It provides state-of-the-art performance in text-based reasoning and vision tasks, including image analysis, programming, mathematical reasoning, and multilingual support across dozens of languages. Equipped with an extensive 128k token context window and optimized for efficient local inference, it supports use cases such as conversational agents, function calling, long-document comprehension, and privacy-sensitive deployments. The updated version is [Mistral Small 3.2](mistralai/mistral-small-3.2-24b-instruct)
Context length: 96000 tokens
Tokenizer: Mistral
Modality: text+image->text
Input modalities: text, image
Output modalities: text
Max completion tokens (top provider): 96000.0
Prompt price per 1M tokens (USD): 0.0
Completion price per 1M tokens (USD): 0.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.0
Output/input cost ratio: 0.0
Supported parameters: frequency_penalty, max_tokens, presence_penalty, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: Mistral: Mistral Small 3.1 24B
Vendor: Mistral AI
Hugging Face ID: mistralai/Mistral-Small-3.1-24B-Instruct-2503
Description: Mistral Small 3.1 24B Instruct is an upgraded variant of Mistral Small 3 (2501), featuring 24 billion parameters with advanced multimodal capabilities. It provides state-of-the-art performance in text-based reasoning and vision tasks, including image analysis, programming, mathematical reasoning, and multilingual support across dozens of languages. Equipped with an extensive 128k token context window and optimized for efficient local inference, it supports use cases such as conversational agents, function calling, long-document comprehension, and privacy-sensitive deployments. The updated version is [Mistral Small 3.2](mistralai/mistral-small-3.2-24b-instruct)
Context length: 131072 tokens
Tokenizer: Mistral
Modality: text+image->text
Input modalities: text, image
Output modalities: text
Max completion tokens (top provider): 131072.0
Prompt price per 1M tokens (USD): 0.0499999999999999
Completion price per 1M tokens (USD): 0.22
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.135
Output/input cost ratio: 4.4
Supported parameters: frequency_penalty, max_tokens, presence_penalty, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p
Model: AllenAI: Olmo 2 32B Instruct
Vendor: Allen Institute for AI
Hugging Face ID: allenai/OLMo-2-0325-32B-Instruct
Description: OLMo-2 32B Instruct is a supervised instruction-finetuned variant of the OLMo-2 32B March 2025 base model. It excels in complex reasoning and instruction-following tasks across diverse benchmarks such as GSM8K, MATH, IFEval, and general NLP evaluation. Developed by AI2, OLMo-2 32B is part of an open, research-oriented initiative, trained primarily on English-language datasets to advance the understanding and development of open-source language models.
Context length: 4096 tokens
Tokenizer: Other
Modality: text->text
Input modalities: text
Output modalities: text
Max completion tokens (top provider): 4096.0
Prompt price per 1M tokens (USD): 0.1999999999999999
Completion price per 1M tokens (USD): 0.35
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.2749999999999999
Output/input cost ratio: 1.75
Supported parameters: frequency_penalty, logit_bias, max_tokens, min_p, presence_penalty, repetition_penalty, seed, stop, temperature, top_k, top_p
Model: Google: Gemma 3 4B (free)
Vendor: Google
Hugging Face ID: google/gemma-3-4b-it
Description: Gemma 3 introduces multimodality, supporting vision-language input and text outputs. It handles context windows up to 128k tokens, understands over 140 languages, and offers improved math, reasoning, and chat capabilities, including structured outputs and function calling.
Context length: 32768 tokens
Tokenizer: Gemini
Modality: text+image->text
Input modalities: text, image
Output modalities: text
Max completion tokens (top provider): 8192.0
Prompt price per 1M tokens (USD): 0.0
Completion price per 1M tokens (USD): 0.0
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.0
Output/input cost ratio: 0.0
Supported parameters: max_tokens, response_format, seed, structured_outputs, temperature, top_p
Model: Google: Gemma 3 4B
Vendor: Google
Hugging Face ID: google/gemma-3-4b-it
Description: Gemma 3 introduces multimodality, supporting vision-language input and text outputs. It handles context windows up to 128k tokens, understands over 140 languages, and offers improved math, reasoning, and chat capabilities, including structured outputs and function calling.
Context length: 96000 tokens
Tokenizer: Gemini
Modality: text+image->text
Input modalities: text, image
Output modalities: text
Prompt price per 1M tokens (USD): 0.01703012
Completion price per 1M tokens (USD): 0.0681536
Image price (USD): 0.0
Request price (USD): 0.0
Median price per 1M tokens (USD): 0.0425918599999999
Output/input cost ratio: 4.0
Supported parameters: frequency_penalty, max_tokens, min_p, presence_penalty, repetition_penalty, response_format, seed, stop, temperature, top_k, top_p