Which model should I use?

Pick the job, set what you're willing to pay, and see what the measured data says. Capability comes from human preference votes on that specific kind of work; price is live API pricing. Nothing here is a blended "intelligence score" we invented.

Required by every agent framework

41 models measured · Arena text / coding

Rows on the frontier are Pareto-optimal: nothing we track is both stronger and cheaper. "Strength vs leader" rescales the board so 100% = parity with the best model on it.

#ModelProviderScoreStrength$/1MContextToolsFrontier
1 Claude Opus 5.5
anthropic/claude-opus-5.5
Anthropic 1547 100% $8.001M tools frontier
2 Claude Opus 5
anthropic/claude-opus-5
Anthropic 1534 96% $10.001M tools
3 Claude Fable 5.1
anthropic/claude-fable-5.1
Anthropic 1528 94% $20.001M tools
4 Claude Fable 5
anthropic/claude-fable-5
Anthropic 1519 92% $20.001M tools
5 Claude Sonnet 5.5
anthropic/claude-sonnet-5.5
Anthropic 1513 90% $4.001M tools frontier
6 Gemini 3.8 Flash
google/gemini-3.8-flash
Google 1510 89% $1.501.048576M tools frontier
7 Kimi K3
moonshotai/kimi-k3
Moonshot AI 1508 89% $3.051.048576M tools
8 GLM 5.3 Flash
z-ai/glm-5.3-flash
Z.ai (Zhipu) 1508 89% $0.241.048576M tools frontier
9 DeepSeek V4.1 Flash
deepseek/deepseek-v4.1-flash
DeepSeek 1506 88% $0.531.048576M tools
10 Qwen3.8 Max (0902)
qwen/qwen3.8-max-0902
Qwen (Alibaba) 1502 87% $3.001M tools
11 Gemini 3.7 Flash
google/gemini-3.7-flash
Google 1497 86% $1.501.048576M tools
12 GLM 5.3
z-ai/glm-5.3
Z.ai (Zhipu) 1496 85% $1.261.048576M tools
13 Gemini 3.6 Flash
google/gemini-3.6-flash
Google 1491 84% $1.501.048576M tools
14 Kimi K2.6
moonshotai/kimi-k2.6
Moonshot AI 1488 83% $1.34262K tools
15 GPT-6 Astra
openai/gpt-6-astra
OpenAI 1487 83% $20.001.05M tools
16 Claude Opus 4.8
anthropic/claude-opus-4.8
Anthropic 1487 83% $10.001M tools
17 GPT-6.1 Sol
openai/gpt-6.1-sol
OpenAI 1487 83% $4.001.05M tools
18 GLM 5.1
z-ai/glm-5.1
Z.ai (Zhipu) 1485 82% $1.49205K tools
19 GLM 5.2
z-ai/glm-5.2
Z.ai (Zhipu) 1485 82% $1.791.048576M tools
20 Kimi K2.5
moonshotai/kimi-k2.5
Moonshot AI 1483 82% $1.00262K tools
21 Claude Sonnet 5
anthropic/claude-sonnet-5
Anthropic 1483 82% $4.001M tools
22 Qwen3.8 27B
qwen/qwen3.8-27b
Qwen (Alibaba) 1482 82% $0.601M tools
23 Grok 4.5
x-ai/grok-4.5
xAI 1474 79% $3.00500K tools
24 Qwen3.7 Plus
qwen/qwen3.7-plus
Qwen (Alibaba) 1473 79% $0.561M tools
25 MiniMax M3
minimax/minimax-m3
MiniMax 1469 78% $0.531.048576M tools
26 DeepSeek V4 Pro 0813
deepseek/deepseek-v4-pro-0813
DeepSeek 1469 78% $0.991.048576M tools
27 DeepSeek V4 Pro 0423
deepseek/deepseek-v4-pro
DeepSeek 1469 78% $1.201.048576M tools
28 GLM 5V Turbo
z-ai/glm-5v-turbo
Z.ai (Zhipu) 1466 77% $1.90203K tools
29 Grok 4.6
x-ai/grok-4.6
xAI 1465 77% $3.00500K tools
30 Mistral Medium 3.5
mistralai/mistral-medium-3-5
Mistral 1461 76% $3.00262K tools
31 DeepSeek V4 Flash 0731
deepseek/deepseek-v4-flash-0731
DeepSeek 1457 75% $0.171.048576M tools frontier
32 DeepSeek V4 Flash 0423
deepseek/deepseek-v4-flash
DeepSeek 1457 75% $0.331.048576M tools
33 MiniMax M2.7
minimax/minimax-m2.7
MiniMax 1454 74% $0.37205K tools
34 Gemini 3.5 Flash Lite
google/gemini-3.5-flash-lite
Google 1453 74% $0.851.048576M tools
35 DeepSeek V3.2
deepseek/deepseek-v3.2
DeepSeek 1449 72% $0.40164K tools
36 GPT-6 Sol
openai/gpt-6-sol
OpenAI 1447 72% $4.001.05M tools
37 GPT-6 Luna
openai/gpt-6-luna
OpenAI 1439 70% $0.201.05M tools
38 Grok 4.7
x-ai/grok-4.7
xAI 1427 67% $3.00500K tools
39 MiniMax M2.1
minimax/minimax-m2.1
MiniMax 1421 65% $0.53205K tools
40 Grok 4.3
x-ai/grok-4.3
xAI 1415 64% $1.561M tools
41 MiniMax M2.5
minimax/minimax-m2.5
MiniMax 1381 55% $0.47205K tools

Capability: Arena leaderboard dataset (CC-BY-4.0), published 2026-10-08. Pricing: OpenRouter, retrieved 2026-10-12. Blended price = (input×3 + output×1) ÷ 4.

For agents: this whole decision table is one fetch — GET /api/v1/route/{task}.json, or /api/v1/route/all.json for every task at once. No key, open CORS. See API & Bots.

How to read this honestly

  • Capability here is human preference, not correctness. Arena scores say which answer people preferred in head-to-head votes on that category. A model can be preferred and still be wrong.
  • Elo boards and agent boards use different scales. On the Elo boards a dead tie with the leader is a 0.50 win probability; on the Bradley-Terry agent boards parity is 1.0. We rescale both to "% of leader strength" for display and publish the raw numbers in the API.
  • Models with no score on a board are left out of that task rather than given an estimated one.
  • Price is OpenRouter's listed price at retrieval time; going direct to a provider can be cheaper or dearer.