Comparison and analysis of AI models across key performance metrics including quality, price, output speed, latency, context window & others. Click on any model to see detailed metrics. For more details including relating to our methodology, see our FAQs.
Model Comparison Summary
| Model Name | Creator | License | Context Window | Further analysis |
|---|---|---|---|---|
OpenAI | Open | 131k |
Models compared: OpenAI: GPT 4o Audio, GPT 4o Realtime, GPT 4o Speech Pipeline, GPT Realtime, GPT Realtime Mini (Oct '25), GPT-3.5 Turbo, GPT-3.5 Turbo (0125), GPT-3.5 Turbo (0301), GPT-3.5 Turbo (0613), GPT-3.5 Turbo (1106), GPT-3.5 Turbo Instruct, GPT-4, GPT-4 Turbo, GPT-4 Turbo (0125), GPT-4 Turbo (1106), GPT-4 Vision, GPT-4.1, GPT-4.1 mini, GPT-4.1 nano, GPT-4.5 (Preview), GPT-4o (Apr), GPT-4o (Aug), GPT-4o (ChatGPT), GPT-4o (Mar), GPT-4o (May), GPT-4o (Nov), GPT-4o Realtime (Dec), GPT-4o mini, GPT-4o mini Realtime (Dec), GPT-5 (ChatGPT), GPT-5 (high), GPT-5 (low), GPT-5 (medium), GPT-5 (minimal), GPT-5 Codex (high), GPT-5 Pro (high), GPT-5 mini (high), GPT-5 mini (medium), GPT-5 mini (minimal), GPT-5 nano (high), GPT-5 nano (medium), GPT-5 nano (minimal), GPT-5.1, GPT-5.1 (high), GPT-5.1 Codex (high), GPT-5.1 Codex mini (high), GPT-5.2, GPT-5.2 (high), GPT-5.2 (medium), GPT-5.2 (xhigh), gpt-oss-120B (high), gpt-oss-120B (low), gpt-oss-20B (high), gpt-oss-20B (low), o1, o1-mini, o1-preview, o1-pro, o3, o3-mini, o3-mini (high), o3-pro, and o4-mini (high), Meta: Code Llama 70B, Llama 2 Chat 13B, Llama 2 Chat 70B, Llama 2 Chat 7B, Llama 3 70B, Llama 3 8B, Llama 3.1 405B, Llama 3.1 70B, Llama 3.1 8B, Llama 3.2 11B (Vision), Llama 3.2 1B, Llama 3.2 3B, Llama 3.2 90B (Vision), Llama 3.3 70B, Llama 4 Behemoth, Llama 4 Maverick, Llama 4 Scout, and Llama 65B, Google: Gemini 1.0 Pro, Gemini 1.0 Ultra, Gemini 1.5 Flash (May), Gemini 1.5 Flash (Sep), Gemini 1.5 Flash-8B, Gemini 1.5 Pro (May), Gemini 1.5 Pro (Sep), Gemini 2.0 Flash, Gemini 2.0 Flash (exp), Gemini 2.0 Flash Thinking exp. (Dec), Gemini 2.0 Flash Thinking exp. (Jan), Gemini 2.0 Flash-Lite (Feb), Gemini 2.0 Flash-Lite (Preview), Gemini 2.0 Pro Experimental, Gemini 2.5 Flash, Gemini 2.5 Flash Live Preview, Gemini 2.5 Flash Native Audio, Gemini 2.5 Flash Native Audio Dialog, Gemini 2.5 Flash (Sep), Gemini 2.5 Flash-Lite, Gemini 2.5 Flash-Lite (Sep), Gemini 2.5 Pro, Gemini 2.5 Pro (Mar), Gemini 2.5 Pro (May), Gemini 3 Pro Preview (high), Gemini 3 Pro Preview (low), Gemini Experimental (Nov), Gemma 2 27B, Gemma 2 2B, Gemma 2 9B, Gemma 3 12B, Gemma 3 1B, Gemma 3 270M, Gemma 3 27B, Gemma 3 4B, Gemma 3n E2B, Gemma 3n E4B, Gemma 3n E4B (May), Gemma 7B, PALM-2, Whisperwind, fiercefalcon (Non-reasoning), and fiercefalcon (Reasoning), Anthropic: Claude 2.0, Claude 2.1, Claude 3 Haiku, Claude 3 Opus, Claude 3 Sonnet, Claude 3.5 Haiku, Claude 3.5 Sonnet (June), Claude 3.5 Sonnet (Oct), Claude 3.7 Sonnet, Claude 4 Opus, Claude 4 Sonnet, Claude 4.1 Opus, Claude 4.5 Haiku, Claude 4.5 Sonnet, Claude Instant, and Claude Opus 4.5, Mistral: Codestral (Jan), Codestral (May), Codestral-Mamba, Devstral 2, Devstral Medium, Devstral Small, Devstral Small (May), Devstral Small 2, Magistral Medium 1, Magistral Medium 1.1, Magistral Medium 1.2, Magistral Small 1, Magistral Small 1.1, Magistral Small 1.2, Ministral 14B (Dec '25), Ministral 3B, Ministral 3B (Dec '25), Ministral 8B, Ministral 8B (Dec '25), Mistral 7B, Mistral Large (Feb), Mistral Large 2 (Jul), Mistral Large 2 (Nov), Mistral Large 3, Mistral Medium, Mistral Medium 3, Mistral Medium 3.1, Mistral NeMo, Mistral Saba, Mistral Small (Feb), Mistral Small (Sep), Mistral Small 3, Mistral Small 3.1, Mistral Small 3.2, Mixtral 8x22B, Mixtral 8x7B, Pixtral 12B, and Pixtral Large, DeepSeek: DeepSeek Coder V2 Lite, DeepSeek LLM 67B (V1), DeepSeek Prover V2 671B, DeepSeek R1 (FP4), DeepSeek R1 (Jan), DeepSeek R1 0528, DeepSeek R1 0528 Qwen3 8B, DeepSeek R1 Distill Llama 70B, DeepSeek R1 Distill Llama 8B, DeepSeek R1 Distill Qwen 1.5B, DeepSeek R1 Distill Qwen 14B, DeepSeek R1 Distill Qwen 32B, DeepSeek V3 (Dec), DeepSeek V3 0324, DeepSeek V3.1, DeepSeek V3.1 Terminus, DeepSeek V3.2, DeepSeek V3.2 Exp, DeepSeek V3.2 Speciale, DeepSeek-Coder-V2, DeepSeek-OCR, DeepSeek-V2, DeepSeek-V2.5, DeepSeek-V2.5 (Dec), DeepSeek-VL2, and Janus Pro 7B, Perplexity: PPLX-70B Online, PPLX-7B-Online, R1 1776, Sonar, Sonar 3.1 Huge, Sonar 3.1 Large, Sonar 3.1 Small , Sonar Large, Sonar Pro, Sonar Reasoning, Sonar Reasoning Pro, and Sonar Small, xAI: Grok 2, Grok 3, Grok 3 Reasoning Beta, Grok 3 mini, Grok 3 mini Reasoning (low), Grok 3 mini Reasoning (high), Grok 4, Grok 4 Fast, Grok 4 Fast 1111 (Reasoning), Grok 4 mini (0908), Grok 4.1 Fast, Grok 4.1 Fast v4, Grok Beta, Grok Code Fast 1, Grok Voice, Grok-1, and test model, OpenChat: OpenChat 3.5, Amazon: Nova 2.0 Lite, Nova 2.0 Lite (high), Nova 2.0 Lite (low), Nova 2.0 Lite (medium), Nova 2.0 Omni, Nova 2.0 Omni (high), Nova 2.0 Omni (low), Nova 2.0 Omni (medium), Nova 2.0 Pro Preview, Nova 2.0 Pro Preview (high), Nova 2.0 Pro Preview (low), Nova 2.0 Pro Preview (medium), Nova 2.0 Realtime, Nova 2.0 Sonic, Nova Lite, Nova Micro, Nova Premier, and Nova Pro, Microsoft Azure: Phi-3 Medium 14B, Phi-3 Mini, Phi-4, Phi-4 Mini, Phi-4 Multimodal, Phi-4 mini reasoning, Phi-4 reasoning, Phi-4 reasoning plus, Yosemite-1-1, Yosemite-1-1-d36, Yosemite 1.1 d36 Updated, Yosemite-1-1-d64, Yosemite 1.1 d64 Updated, and Yosemite, Liquid AI: LFM 1.3B, LFM 3B, LFM 40B, LFM2 1.2B, LFM2 2.6B, and LFM2 8B A1B, Upstage: Solar Mini, Solar Pro, Solar Pro (Nov), Solar Pro 2, and Solar Pro 2 , Databricks: DBRX, MiniMax: MiniMax M1 40k, MiniMax M1 80k, MiniMax-M2, and MiniMax-Text-01, NVIDIA: Cosmos Nemotron 34B, Llama 3.1 Nemotron 70B, Llama 3.1 Nemotron Nano 4B v1.1, Llama 3.1 Nemotron Nano 8B, Llama 3.3 Nemotron Nano 8B, Llama Nemotron Ultra, Llama 3.3 Nemotron Super 49B, Llama Nemotron Super 49B v1.5, NVIDIA Nemotron 3 Nano, NVIDIA Nemotron Nano 12B v2 VL, NVIDIA Nemotron Nano 9B V2, and Nemotron Nano V3 (30B A3B), StepFun: Step-2, Step-2-Mini, Step3, step-1-128k, step-1-256k, step-1-32k, step-1-8k, step-1-flash, step-2-16k-exp, and step-r1-v-mini, IBM: Granite 3.0 2B, Granite 3.0 8B, Granite 3.3 8B, Granite 4.0 1B, Granite 4.0 350M, Granite 4.0 8B, Granite 4.0 H 1B, Granite 4.0 H 350M, Granite 4.0 H Small, Granite 4.0 Micro, Granite 4.0 Tiny, and Granite Vision 3.3 2B, Inceptionlabs: Mercury, Mercury Coder Mini, Mercury Coder Small, and Mercury Instruct, Reka AI: Reka Core, Reka Edge, Reka Flash (Feb), Reka Flash, Reka Flash 3, and Reka Flash 3.1, LG AI Research: EXAONE 4.0 32B, EXAONE Deep 32B, and Exaone 4.0 1.2B, Xiaomi: MiMo 7B RL and Mimo-v2-flash-1207-sft, Baidu: ERNIE 4.5, ERNIE 4.5 0.3B, ERNIE 4.5 21B A3B, ERNIE 4.5 300B A47B, ERNIE 4.5 VL 28B A3B, ERNIE 4.5 VL 424B A47B, ERNIE 5.0 Thinking Preview, and ERNIE X1, Baichuan: Baichuan 4 and Baichuan M1 (Preview), vercel: v0-1.0-md, Apple: Apple On-Device and FastVLM, Other: LLaVA-v1.5-7B, Tencent: Hunyuan A13B, Hunyuan 80B A13B, Hunyuan T1, and Hunyuan-TurboS, Prime Intellect: INTELLECT-3, Motif Technologies: Motif-2-12.7B, Korea Telecom: midm-250-pro-rsnsft, Z AI: GLM-4 32B, GLM-4 9B, GLM-4-Air, GLM-4 AirX, GLM-4 FlashX, GLM-4-Long, GLM-4-Plus, GLM-4.1V 9B Thinking, GLM-4.5, GLM-4.5-Air, GLM-4.5V, GLM-4.6, GLM-4.6V, GLM-Z1 32B, GLM-Z1 9B, GLM-Z1 Rumination 32B, and GLM-Zero (Preview), Cohere: Aya Expanse 32B, Aya Expanse 8B, Command, Command A, Command Light, Command R7B, Command-R, Command-R (Mar), Command-R+ (Apr), and Command-R+, Bytedance: Duobao 1.5 Pro, Seed-Thinking-v1.5, Skylark Lite, and Skylark Pro, AI21 Labs: Jamba 1.5 Large, Jamba 1.5 Large (Feb), Jamba 1.5 Mini, Jamba 1.5 Mini (Feb), Jamba 1.6 Large, Jamba 1.6 Mini, Jamba 1.7 Large, Jamba 1.7 Mini, Jamba Instruct, and Jamba Reasoning 3B, Snowflake: Arctic and Snowflake Llama 3.3 70B, PaddlePaddle: PaddleOCR-VL-0.9B, Alibaba: QwQ-32B, QwQ 32B-Preview, Qwen Chat 14B, Qwen Chat 72B, Qwen Chat 7B, Qwen1.5 Chat 110B, Qwen1.5 Chat 14B, Qwen1.5 Chat 32B, Qwen1.5 Chat 72B, Qwen1.5 Chat 7B, Qwen2 72B, Qwen2 Instruct 7B, Qwen2 Instruct A14B 57B, Qwen2-VL 72B, Qwen2.5 Coder 32B, Qwen2.5 Coder 7B , Qwen2.5 Instruct 14B, Qwen2.5 Instruct 32B, Qwen2.5 72B, Qwen2.5 Instruct 7B, Qwen2.5 Max, Qwen2.5 Max 01-29, Qwen2.5 Omni 7B, Qwen2.5 Plus, Qwen2.5 Turbo, Qwen2.5 VL 72B, Qwen2.5 VL 7B, Qwen3 0.6B, Qwen3 1.7B, Qwen3 14B, Qwen3 235B, Qwen3 235B A22B 2507, Qwen3 235B 2507, Qwen3 30B, Qwen3 30B A3B 2507, Qwen3 32B, Qwen3 4B, Qwen3 4B 2507, Qwen3 8B, Qwen3 Coder 30B A3B, Qwen3 Coder 480B, Qwen3 Max, Qwen3 Max (Preview), Qwen3 Max Thinking, Qwen3 Next 80B A3B, Qwen3 Omni 30B A3B, Qwen3 VL 235B A22B, Qwen3 VL 30B A3B, Qwen3 VL 32B, Qwen3 VL 4B, and Qwen3 VL 8B, InclusionAI: Ling-1T, Ling-flash-2.0, Ling-mini-2.0, Ring-1T, and Ring-flash-2.0, 01.AI: Yi-Large and Yi-Lightning, and ByteDance Seed: Doubao Seed Code and Seed-OSS-36B-Instruct.
OpenAI | Proprietary | 400k |
OpenAI | Proprietary | 400k |
OpenAI | Proprietary | 400k |
OpenAI | Open | 131k |
OpenAI | Proprietary | 400k |
OpenAI | Open | 131k |
OpenAI | Proprietary | 400k |
OpenAI | Open | 131k |
OpenAI | Proprietary | 400k |
OpenAI | Proprietary | 400k |
OpenAI | Proprietary | 200k |
OpenAI | Proprietary | 400k |
OpenAI | Proprietary | 400k |
OpenAI | Proprietary | 128k |
OpenAI | Proprietary | 400k |
OpenAI | Proprietary | 400k |
OpenAI | Proprietary | 400k |
OpenAI | Proprietary | 400k |
OpenAI | Proprietary | 400k |
OpenAI | Proprietary | 400k |
OpenAI | Proprietary | 200k |
OpenAI | Proprietary | 128k |
OpenAI | Proprietary | 128k |
OpenAI | Proprietary | 128k |
OpenAI | Proprietary | 128k |
OpenAI | Proprietary | 128k |
OpenAI | Proprietary | 128k |
OpenAI | Proprietary | 128k |
OpenAI | Proprietary | 1m |
OpenAI | Proprietary | 128k |
OpenAI | Proprietary | 1m |
OpenAI | Proprietary | 200k |
OpenAI | Proprietary | 1m |
OpenAI | Proprietary | 200k |
OpenAI | Proprietary | 200k |
OpenAI | Proprietary | 200k |
OpenAI | Proprietary | 200k |
OpenAI | Proprietary | 400k |
OpenAI | Proprietary | 128k |
OpenAI | Proprietary | 128k |
OpenAI | Proprietary | 128k |
OpenAI | Proprietary | 128k |
Perplexity | Open | 128k | ||
Perplexity | Proprietary | 200k | ||
Perplexity | Proprietary | 127k | ||
Perplexity | Proprietary | 127k | ||
Perplexity | Proprietary | 127k |
Microsoft Azure | Open | 16k | ||
Microsoft Azure | Open | 128k | ||
Microsoft Azure | Open | 128k | ||
Microsoft Azure | Open | 128k |
Liquid AI | Open | 33k | ||
Liquid AI | Open | 33k | ||
Liquid AI | Open | 33k | ||
Liquid AI | Proprietary | 32k |
MiniMax | Open | 205k | ||
MiniMax | Open | 1m | ||
MiniMax | Open | 1m |
Kimi | Open | 256k | ||
Kimi | Open | 256k | ||
Kimi | Open | 1m | ||
Kimi | Open | 128k |
Reka AI | Open | 128k | ||
Reka AI | Proprietary | 128k |
Baidu | Open | 131k | ||
Baidu | Proprietary | 128k |
Deep Cogito | Open | 128k |
KwaiKAT | Proprietary | 256k |
Motif Technologies | Proprietary | 128k |
Cohere | Open | 256k | ||
Cohere | Open | 128k | ||
Cohere | Open | 128k |
ServiceNow | Open | 128k | ||
ServiceNow | Open | 128k |
InclusionAI | Open | 128k | ||
InclusionAI | Open | 128k | ||
InclusionAI | Open | 128k | ||
InclusionAI | Open | 128k | ||
InclusionAI | Open | 131k |
ByteDance Seed | Proprietary | 256k | ||
ByteDance Seed | Open | 512k |
Databricks | Open | 33k |
Combination metric covering multiple dimensions of intelligence - the simplest way to compare how smart models are. Version 3.0 was released in September 2025 and includes: MMLU-Pro, GPQA Diamond, Humanity's Last Exam, LiveCodeBench, SciCode, AIME 2025, IFBench, AA-LCR, Terminal-Bench Hard, 𝜏²-Bench Telecom. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.
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Combination metric covering multiple dimensions of intelligence - the simplest way to compare how smart models are. Version 3.0 was released in September 2025 and includes: MMLU-Pro, GPQA Diamond, Humanity's Last Exam, LiveCodeBench, SciCode, AIME 2025, IFBench, AA-LCR, Terminal-Bench Hard, 𝜏²-Bench Telecom. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.
Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if the weights are available but commercial use is limited (typically requires obtaining a paid license).
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While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.
Combination metric covering multiple dimensions of intelligence - the simplest way to compare how smart models are. Version 3.0 was released in September 2025 and includes: MMLU-Pro, GPQA Diamond, Humanity's Last Exam, LiveCodeBench, SciCode, AIME 2025, IFBench, AA-LCR, Terminal-Bench Hard, 𝜏²-Bench Telecom. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.
AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.
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LLM benchmarks dataset. https://artificialanalysis.ai","data":"modelName,omniscienceIndex,detailsUrl,isLabClaimedValue\nGemini 3 Pro Preview (high),12.867,/models/gemini-3-pro/providers,false\nClaude Opus 4.5,10.233,/models/claude-opus-4-5-thinking/providers,false\nClaude 4.1 Opus,4.933,/models/claude-4-1-opus-thinking/providers,false\nGPT-5.1 (high),2.2,/models/gpt-5-1/providers,false\nGrok 4,0.95,/models/grok-4/providers,false\nGemini 3 Pro Preview (low),-1.05,/models/gemini-3-pro-low/providers,false\nClaude 4.5 Sonnet,-2.083,/models/claude-4-5-sonnet-thinking/providers,false\nGPT-5.2 (xhigh),-4.317,/models/gpt-5-2/providers,false\nClaude 4.5 Haiku,-5.667,/models/claude-4-5-haiku-reasoning/providers,false\nClaude Opus 4.5,-6.45,/models/claude-opus-4-5/providers,false\nGrok 3 mini Reasoning (high),-6.9,/models/grok-3-mini-reasoning/providers,false\nClaude 4.5 Haiku,-7.95,/models/claude-4-5-haiku/providers,false\nClaude 4.5 Sonnet,-10.65,/models/claude-4-5-sonnet/providers,false\nGPT-5 (high),-11.1,/models/gpt-5/providers,false\nGPT-5 mini (medium),-12.933,/models/gpt-5-mini-medium/providers,false\nGPT-5 (low),-12.933,/models/gpt-5-low/providers,false\nGPT-5 (medium),-13.733,/models/gpt-5-medium/providers,false\no3,-17.183,/models/o3/providers,false\nGemini 2.5 Pro,-17.95,/models/gemini-2-5-pro/providers,false\nLlama 3.1 405B,-18.167,/models/llama-3-1-instruct-405b/providers,false\nGPT-5.1 Codex mini (high),-18.283,/models/gpt-5-1-codex-mini/providers,false\nDeepSeek V3.2 Speciale,-19.233,/models/deepseek-v3-2-speciale/providers,false\nGPT-5 mini (high),-19.617,/models/gpt-5-mini/providers,false\nDeepSeek V3.2,-23.317,/models/deepseek-v3-2-reasoning/providers,false\nKimi K2 Thinking,-23.417,/models/kimi-k2-thinking/providers,false\nDeepSeek V3.1 Terminus,-26.7,/models/deepseek-v3-1-terminus-reasoning/providers,false\nGPT-5 nano (medium),-27.35,/models/gpt-5-nano-medium/providers,false\nMagistral Medium 1.2,-27.633,/models/magistral-medium-2509/providers,false\nKimi K2 0905,-28.35,/models/kimi-k2-0905/providers,false\nGPT-5 nano (high),-29.65,/models/gpt-5-nano/providers,false\nDeepSeek R1 0528,-29.667,/models/deepseek-r1/providers,false\nGrok 4 Fast,-30.5,/models/grok-4-fast-reasoning/providers,false\nGrok 4.1 Fast,-31.383,/models/grok-4-1-fast-reasoning/providers,false\nDeepSeek V3.2 Exp,-31.9,/models/deepseek-v3-2-reasoning-0925/providers,false\nDevstral Medium,-32.8,/models/devstral-medium/providers,false\nGLM-4.6,-33.25,/models/glm-4-6/providers,false\nHermes 4 405B,-35.067,/models/hermes-4-llama-3-1-405b/providers,false\nKAT-Coder-Pro V1,-35.533,/models/kat-coder-pro-v1/providers,false\nDoubao Seed Code,-35.933,/models/doubao-seed-code/providers,false\nGPT-5.1,-36.583,/models/gpt-5-1-non-reasoning/providers,false\nGPT-5 (minimal),-36.667,/models/gpt-5-minimal/providers,false\nERNIE 4.5 300B A47B,-36.833,/models/ernie-4-5-300b-a47b/providers,false\nHermes 4 405B,-37.367,/models/hermes-4-llama-3-1-405b-reasoning/providers,false\nGemini 2.5 Flash (Sep),-37.5,/models/gemini-2-5-flash-preview-09-2025-reasoning/providers,false\nGrok Code Fast 1,-38.033,/models/grok-code-fast-1/providers,false\nNova Premier,-38.317,/models/nova-premier/providers,false\nQwen3 Max Thinking,-39.783,/models/qwen3-max-thinking/providers,false\nMistral Large 3,-40.983,/models/mistral-large-3/providers,false\nGemini 2.5 Flash (Sep),-41.317,/models/gemini-2-5-flash-preview-09-2025/providers,false\nGPT-4.1,-42.133,/models/gpt-4-1/providers,false\nERNIE 5.0 Thinking Preview,-42.367,/models/ernie-5-0-thinking-preview/providers,false\nNVIDIA Nemotron Nano 9B V2,-43.217,/models/nvidia-nemotron-nano-9b-v2-reasoning/providers,false\nLlama 4 Maverick,-43.467,/models/llama-4-maverick/providers,false\nGemini 2.5 Flash-Lite (Sep),-43.717,/models/gemini-2-5-flash-lite-preview-09-2025/providers,false\nGLM-4.6,-43.883,/models/glm-4-6-reasoning/providers,false\nDeepSeek V3.1 Terminus,-44.583,/models/deepseek-v3-1-terminus/providers,false\nQwen3 Max,-44.9,/models/qwen3-max/providers,false\nQwen3 235B 2507,-45.383,/models/qwen3-235b-a22b-instruct-2507/providers,false\nLlama Nemotron Ultra,-46.2,/models/llama-3-1-nemotron-ultra-253b-v1-reasoning/providers,false\nGLM-4.5V,-46.417,/models/glm-4-5v-reasoning/providers,false\nQwen3 VL 235B A22B,-46.567,/models/qwen3-vl-235b-a22b-reasoning/providers,false\nDeepSeek R1 Distill Llama 70B,-47.433,/models/deepseek-r1-distill-llama-70b/providers,false\nLlama Nemotron Super 49B v1.5,-47.467,/models/llama-nemotron-super-49b-v1-5-reasoning/providers,false\nNova 2.0 Pro Preview (low),-47.5,/models/nova-2-0-pro-reasoning-low/providers,false\nQwen3 235B A22B 2507,-47.7,/models/qwen3-235b-a22b-instruct-2507-reasoning/providers,false\nMistral Medium 3.1,-47.9,/models/mistral-medium-3-1/providers,false\nDevstral 2,-47.917,/models/devstral-2/providers,false\nDeepSeek V3.2,-48.683,/models/deepseek-v3-2/providers,false\nMiniMax-M2,-49.533,/models/minimax-m2/providers,false\nNova 2.0 Pro Preview (medium),-50.3,/models/nova-2-0-pro-reasoning-medium/providers,false\nNova 2.0 Pro Preview,-50.367,/models/nova-2-0-pro/providers,false\nCommand A,-50.4,/models/command-a/providers,false\nHermes 4 70B,-50.717,/models/hermes-4-llama-3-1-70b-reasoning/providers,false\nMistral Small 3.2,-51.3,/models/mistral-small-3-2/providers,false\nNova 2.0 Omni (low),-51.4,/models/nova-2-0-omni-reasoning-low/providers,false\nQwen3 Coder 30B A3B,-51.7,/models/qwen3-coder-30b-a3b-instruct/providers,false\ngpt-oss-120B (high),-51.933,/models/gpt-oss-120b/providers,false\nDevstral Small,-51.967,/models/devstral-small/providers,false\nGrok 4.1 Fast,-52.317,/models/grok-4-1-fast/providers,false\nNVIDIA Nemotron 3 Nano,-52.383,/models/nvidia-nemotron-3-nano-30b-a3b-reasoning/providers,false\nQwen3 Next 80B A3B,-52.783,/models/qwen3-next-80b-a3b-reasoning/providers,false\nLlama 4 Scout,-53.05,/models/llama-4-scout/providers,false\nQwen3 VL 32B,-53.233,/models/qwen3-vl-32b-reasoning/providers,false\nSeed-OSS-36B-Instruct,-53.533,/models/seed-oss-36b-instruct/providers,false\nQwen3 VL 8B,-53.8,/models/qwen3-vl-8b-instruct/providers,false\nQwen3 VL 235B A22B,-53.867,/models/qwen3-vl-235b-a22b-instruct/providers,false\nQwen3 VL 8B,-54.317,/models/qwen3-vl-8b-reasoning/providers,false\nGemini 2.5 Flash-Lite (Sep),-54.633,/models/gemini-2-5-flash-lite-preview-09-2025-reasoning/providers,false\nNova 2.0 Lite (low),-54.95,/models/nova-2-0-lite-reasoning-low/providers,false\nApriel-v1.5-15B-Thinker,-55.85,/models/apriel-v1-5-15b-thinker/providers,false\ngpt-oss-120B (low),-55.933,/models/gpt-oss-120b-low/providers,false\nQwen3 30B A3B 2507,-57.433,/models/qwen3-30b-a3b-2507-reasoning/providers,false\nSolar Pro 2,-57.533,/models/solar-pro-2-reasoning/providers,false\nNova 2.0 Lite (medium),-57.633,/models/nova-2-0-lite-reasoning-medium/providers,false\nDevstral Small 2,-58.883,/models/devstral-small-2/providers,false\nQwen3 VL 30B A3B,-59.133,/models/qwen3-vl-30b-a3b-reasoning/providers,false\nNova 2.0 Omni (medium),-59.7,/models/nova-2-0-omni-reasoning-medium/providers,false\nApriel-v1.6-15B-Thinker,-59.833,/models/apriel-v1-6-15b-thinker/providers,false\nNova 2.0 Lite,-60.483,/models/nova-2-0-lite/providers,false\nQwen3 Next 80B A3B,-60.483,/models/qwen3-next-80b-a3b-instruct/providers,false\ngpt-oss-20B (low),-60.6,/models/gpt-oss-20b-low/providers,false\nEXAONE 4.0 32B,-61.417,/models/exaone-4-0-32b-reasoning/providers,false\nGranite 4.0 H Small,-62.067,/models/granite-4-0-h-small/providers,false\nMotif-2-12.7B,-62.233,/models/motif-2-12-7b/providers,false\nQwen3 Omni 30B A3B,-62.417,/models/qwen3-omni-30b-a3b-reasoning/providers,false\nSolar Pro 2,-63.117,/models/solar-pro-2/providers,false\nGLM-4.5-Air,-63.15,/models/glm-4-5-air/providers,false\nQwen3 VL 32B,-63.9,/models/qwen3-vl-32b-instruct/providers,false\nMinistral 3B (Dec '25),-63.967,/models/ministral-3b/providers,false\nQwen3 VL 30B A3B,-64.033,/models/qwen3-vl-30b-a3b-instruct/providers,false\nDeepSeek R1 0528 Qwen3 8B,-64.617,/models/deepseek-r1-qwen3-8b/providers,false\ngpt-oss-20B (high),-64.9,/models/gpt-oss-20b/providers,false\nQwen3 8B,-66.117,/models/qwen3-8b-instruct-reasoning/providers,false\nEXAONE 4.0 32B,-66.133,/models/exaone-4-0-32b/providers,false\nNVIDIA Nemotron Nano 12B v2 VL,-66.35,/models/nvidia-nemotron-nano-12b-v2-vl-reasoning/providers,false\nGPT-5 nano (minimal),-66.367,/models/gpt-5-nano-minimal/providers,false\nMagistral Small 1.2,-66.383,/models/magistral-small-2509/providers,false\nQwen3 30B A3B 2507,-66.8,/models/qwen3-30b-a3b-2507/providers,false\nMinistral 14B (Dec '25),-67.383,/models/ministral-14b/providers,false\nLing-flash-2.0,-67.45,/models/ling-flash-2-0/providers,false\nQwen3 Omni 30B A3B,-69.75,/models/qwen3-omni-30b-a3b-instruct/providers,false\nMinistral 8B (Dec '25),-69.983,/models/ministral-8b/providers,false\nOLMo 3 7B Think,-73.967,/models/olmo-3-7b-think/providers,false\nQwen3 8B,-75.4,/models/qwen3-8b-instruct/providers,false"}
While higher intelligence models are typically more expensive, they do not all follow the same price-quality curve.
Combination metric covering multiple dimensions of intelligence - the simplest way to compare how smart models are. Version 3.0 was released in September 2025 and includes: MMLU-Pro, GPQA Diamond, Humanity's Last Exam, LiveCodeBench, SciCode, AIME 2025, IFBench, AA-LCR, Terminal-Bench Hard, 𝜏²-Bench Telecom. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.
Price per token, represented as USD per million Tokens. Price is a blend of Input & Output token prices (3:1 ratio).
Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).
The number of tokens required to run all evaluations in the Artificial Analysis Intelligence Index (excluding repeats).
The cost to run the evaluations in the Artificial Analysis Intelligence Index, calculated using the model's input and output token pricing and the number of tokens used across evaluations (excluding repeats).
Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data.
Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).
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Price per token included in the request/message sent to the API, represented as USD per million Tokens.
Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).
While higher intelligence models are typically more expensive, they do not all follow the same price-quality curve.
Combination metric covering multiple dimensions of intelligence - the simplest way to compare how smart models are. Version 3.0 was released in September 2025 and includes: MMLU-Pro, GPQA Diamond, Humanity's Last Exam, LiveCodeBench, SciCode, AIME 2025, IFBench, AA-LCR, Terminal-Bench Hard, 𝜏²-Bench Telecom. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.
Price per token, represented as USD per million Tokens. Price is a blend of Input & Output token prices (3:1 ratio).
Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).
Measured by Output Speed (tokens per second)
Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).
Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).
{"@context":"https://schema.org","@type":"Dataset","name":"Output Speed","creator":{"@type":"Organization","name":"Artificial Analysis","url":"https://artificialanalysis.ai"},"description":"Output Tokens per Second; Higher is better","measurementTechnique":"Independent test run by Artificial Analysis on dedicated hardware.","spatialCoverage":"Worldwide","keywords":["analytics","llm","AI","benchmark","model","gpt","claude"],"license":"https://creativecommons.org/licenses/by/4.0/","isAccessibleForFree":true,"citation":"Artificial Analysis (2025). LLM benchmarks dataset. https://artificialanalysis.ai","data":""}
Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).
Price per token, represented as USD per million Tokens. Price is a blend of Input & Output token prices (3:1 ratio).
Measured by Time (seconds) to First Token
Time to first answer token received, in seconds, after API request sent. For reasoning models, this includes the 'thinking' time of the model before providing an answer. For models which do not support streaming, this represents time to receive the completion.
Seconds to output 500 Tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speed
Seconds to receive a 500 token response. Key components:
Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).
The total number of trainable weights and biases in the model, expressed in billions. These parameters are learned during training and determine the model's ability to process and generate responses.
The number of parameters actually executed during each inference forward pass, expressed in billions. For Mixture of Experts (MoE) models, a routing mechanism selects a subset of experts per token, resulting in fewer active than total parameters. Dense models use all parameters, so active equals total.