Knowledge

MMLU-Pro

MMLU-Pro — 12K-question challenge MMLU variant across 14 subjects; per-question binary correctness + leaderboard per-category aggregates.

13,542items
48subjects
MITlicense
knowledgedomain
reasoningdomain
textmodality
item-level responses released
Saturation status: No

Response matrix

Fit to width. Hover for subject & item; click a cell for details.

MMLU-Pro response matrix: AI models (rows) against items (columns)
Correct (1)Incorrect (0)Unobserved

Scale: 1 = correct · 0 = incorrect

Subjects

  1. 1gemini-3.1-pro0.8733
  2. 2arx_03140.8297
  3. 3iask_pro0.8119
  4. 4arx_30.7824
  5. 5claude-3-5-sonnet-202410220.7764
  6. 6claude-3.5-sonnet0.7612
  7. 7gpt-4o-2024-08-060.7468
  8. 8sonnet-3.50.7284
  9. 9gpt4o(2024-05-13)0.7207
  10. 10gemini-1.5-pro-0020.7025
  11. 11opus0.6846
  12. 12deepseek-chat-v2_50.6583
  13. 13gemini-1.5-flash-0020.6409
  14. 14DeepSeek-Coder-V20.6363
  15. 15gpt-4o-mini0.6309
  16. 16Meta-Llama-3_1-70B-Instruct0.6282
  17. 17claude-3-5-haiku-202410220.6212
  18. 18flash0.5913
  19. 19Meta-Llama-3-70B-Instruct0.5558
  20. 20sonnet0.5512
  21. 21deepseek0.5248
  22. 22Meta-Llama-3_1-70B0.5247
  23. 23Meta-Llama-3-70B0.5201
  24. 24jamba-1.5-large0.4946
  25. 25Qwen1.5-110B0.492
  26. 26Qwen1.5-72B-Chat0.4715
  27. 27Meta-Llama-3_1-8B-Instruct0.4419
  28. 28Yi-34B0.4208
  29. 29mathstral-7B0.42
  30. 30Mixtral-8x7B-Instruct-v0.10.4189
  31. 31Phi-3-mini-4k-instruct0.4148
  32. 32Meta-Llama-3-8B-Instruct0.4012
  33. 33Mixtral-8x7B-v0.10.3988
  34. 34c4ai-command-r-v010.3659
  35. 35Meta-Llama-3_1-8B0.3649
  36. 36Llama-2-70b-hf0.3629