Who ranks #1 on the Mt Bench leaderboard?
As of August 20, 2026, Qwen2.5 72B Instruct by Qwen ranks #1 on Mt Bench at 93.5%. API pricing is $0.35/M input and $0.40/M output.
Mt Bench
🏆 LeaderboardAs of August 20, 2026, Qwen2.5 72B Instruct is #1 for Mt Bench at 93.5%. Ranked by the Mt Bench score 8 models in this index have a published Mt Bench score. Mt Bench leaderboard: rank models by Mt Bench next to live API token prices.
Updated August 20, 2026282 models33 providers
Showing 1–50 of 282 models
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The index
All Large Language Models
282 models across 33 providers. Search or jump to a lab — every model page stays linked here.
OpenAI
51 modelsGPT-5.6 CyberGPT-5.6 LunaGPT-5.6 SolGPT-5.6 TerraGPT-5.5 InstantGPT-5.5GPT-5.5 ProGPT-5.4 miniGPT-5.4 nanoGPT-5.4GPT-5.3 ChatGPT-5.3 CodexGPT-5.2 CodexGPT-5.2GPT-5.2 ProGPT-5.1 CodexGPT-5.1GPT-5.1 Codex HighGPT-5.1 Codex MiniGPT-5.1 HighGPT-5.1 InstantGPT-5.1 MediumGPT-5.1 ThinkingGPT-5 CodexGPT-5GPT-5 HighGPT-5 MediumGPT-5 miniGPT-5 nanoGPT OSS 120BGPT OSS 120B HighGPT OSS 20BGPT OSS 20B Higho3-proo3o4-miniGPT-4.1GPT-4.1 miniGPT-4.1 nanoo3-minio1o1-proo1-minio1-previewGPT-4oGPT-4o miniChatGPT-4o LatestGPT-4oGPT-4 TurboGPT-4GPT-3.5 Turbo
Anthropic
22 modelsClaude Opus 5Claude Sonnet 5Claude Fable 5Claude Mythos 5Claude Opus 4.8Claude Opus 4.7Claude Sonnet 4.6Claude Opus 4.6Claude Opus 4.5Claude Haiku 4.5Claude Sonnet 4.5Claude Opus 4.1Claude Opus 4Claude Sonnet 4Claude 3.7 SonnetClaude 3.5 HaikuClaude 3.5 SonnetClaude 3.5 SonnetClaude 3 HaikuClaude 3 OpusClaude 3 SonnetClaude Mythos Preview
Gemini 3.7 FlashGemini 3.5 Flash-LiteGemini 3.6 FlashGemini 3.5 FlashGemma 4 26B-A4BGemma 4 31BGemini 3.1 Flash-LiteGemini 3.1 ProGemini 3 FlashGemini 3 ProGemma 3n E4B InstructedGemini 2.5 Flash-LiteGemini 2.5 Pro Preview 06-05Gemini 2.5 FlashGemini 2.5 ProGemma 3 12BGemma 3 27BGemma 3 4BGemini 2.0 Flash-LiteGemini 2.0 FlashGemma 2 27BGemini 1.5 FlashGemini 1.5 ProGemini 1.5 Flash 8BGemini 1.0 Pro
Cursor
2 models
xAI
15 models
DeepSeek
16 models
Qwen
38 modelsQwen3.8-27BQwen3.8-2.4T-A95BQwen3.8 MaxQwen3.7 FlashQwen3.7-PlusQwen3.7 MaxQwen3.6-27BQwen3.6-35B-A3BQwen3.6 PlusQwen3.5-9BQwen3.5-122B-A10BQwen3.5-27BQwen3.5-35B-A3BQwen3.5-397B-A17BQwen3 MaxQwen3 VL 235B A22B InstructQwen3 VL 235B A22B ThinkingQwen3 VL 30B A3B InstructQwen3 VL 30B A3B ThinkingQwen3 VL 32B InstructQwen3 VL 4B InstructQwen3 VL 4B ThinkingQwen3 VL 8B InstructQwen3 VL 8B ThinkingQwen3-Next-80B-A3B-InstructQwen3-Next-80B-A3B-ThinkingQwen3-235B-A22B-Thinking-2507Qwen3-235B-A22B-Instruct-2507Qwen3 235B A22BQwen3 30B A3BQwen3 32BQwen3-Coder 480B A35B InstructQwen2.5 VL 72B InstructQwen3-CoderQwQ-32B-PreviewQwen2.5 72B InstructQwen2.5 7B InstructQwen2.5-Coder 32B Instruct
AI21 Labs
2 models
Amazon
5 models
Baidu
1 models
ByteDance
2 models
Cohere
3 models
IBM
1 models
Inception
1 models
inclusionAI
1 models
LG AI Research
1 models
Meituan
4 models
Meta
12 models
Mistral
23 modelsMistral Medium 3.5Mistral Small 4Min istral 3 (3B Reasoning 2512)MiniStral 3 (14B Instruct 2512)Ministral 3 (14B Reasoning 2512)Ministral 3 (3B Instruct 2512)Ministral 3 (8B Instruct 2512)Ministral 3 (8B Reasoning 2512)Mistral Large 3 (675B Instruct 2512)Mistral Large 3Devstral Small 1.1Devstral MediumMistral Small 3.2 24B InstructMagistral MediumMistral Small 3.1 24B BaseMistral Small 3.1 24B InstructMistral Small 3 24B InstructPixtral LargeMinistral 8B InstructMistral SmallPixtral-12BMistral Large 2Mistral NeMo Instruct
Moonshot AI
9 models
Nous Research
1 models
NVIDIA
5 models
Poolside
2 models
Sakana AI
1 models
StepFun
1 models
Tencent
1 models
Thinking Machines
1 models
Unisound
1 models
Upstage
1 models
Xiaomi
3 modelsWhich model leads Mt Bench right now?
As of August 20, 2026, Qwen2.5 72B Instruct by Qwen is #1 for Mt Bench at 93.5%. Ranked by the Mt Bench score This board also tracks Mt Bench. Next on the same board: DeepSeek-V2.5 and Hermes 3 70B. This mt bench leaderboard ranks models by Mt Bench. Scores come from public evals. Prices are the live API rates in the table above.
Sources: OpenAI API pricing (https://developers.openai.com/api/docs/pricing); Anthropic Claude API pricing (https://platform.claude.com/docs/en/about-claude/pricing); Gemini API pricing (https://ai.google.dev/gemini-api/docs/pricing)
| Rank | Model | Mt Bench | Input /M | Output /M |
|---|---|---|---|---|
| 1 | Qwen2.5 72B Instruct | 93.5% | $0.35 | $0.40 |
| 2 | DeepSeek-V2.5 | 90.2% | $0.14 | $0.28 |
| 3 | Hermes 3 70B | 89.9% | $0.35 | $1.40 |
| 4 | Qwen2.5 7B Instruct | 87.5% | $0.30 | $0.30 |
| 5 | Mistral Large 2 | 86.3% | $2.00 | $6.00 |
| 6 | Mistral Small 3 24B Instruct | 83.5% | $0.07 | $0.14 |
| 7 | Ministral 8B Instruct | 83% | $0.10 | $0.10 |
| 8 | Pixtral-12B | 76.8% | $0.15 | $0.15 |
Related LLM leaderboards
Individual benchmark pages
Rank one eval at a time. All LLM benchmarks.
Mt Bench FAQ
What are the top models on Mt Bench?
The current Mt Bench ranking as of August 20, 2026 is 1. Qwen2.5 72B Instruct at 93.5%; 2. DeepSeek-V2.5 at 90.2%; 3. Hermes 3 70B at 89.9%.
Which mt bench model is the cheapest?
Ministral 8B Instruct is the cheapest scored model on this mt bench leaderboard at $0.10/M input and $0.10/M output ($0.20 blended). Qwen2.5 72B Instruct still leads Mt Bench at 93.5%.
Should I always pick the #1 Mt Bench model?
Not automatically. Qwen2.5 72B Instruct leads Mt Bench, but a cheaper scored model can be the better production choice if the quality gap is small. Use the table to weigh Mt Bench against input/output price, context window, and related evals.
How often is the Mt Bench leaderboard updated?
Scores and API prices on this page are refreshed from published evals and provider rates. The snapshot is labeled August 20, 2026. Treat it as a current index, not a one-off blog post.
What is Mt Bench?
Mt Bench is a public LLM eval (the Mt Bench score). This page ranks models that have published a score, next to live API prices.
Where is the Mt Bench leaderboard?
This page is the Mt Bench leaderboard. Models are sorted by Mt Bench, with input and output token prices on the same row so you can weigh score against cost. Official boards often omit price; that comparison is the point of this index.
How is this Mt Bench ranking different from the official board?
Official eval pages own the methodology. This page keeps the published Mt Bench score next to live API $/M so you can pick a production SKU, not only a trophy number.