Knowledge & reasoning
MedXpertQA (Text) leaderboard
As of 2026-10-07, the highest measured score on MedXpertQA (Text) is 71.5% by Gemini 3.1 Pro. 78 more models have estimated scores, calibrated from the benchmarks they were measured on.
Measured scores: benchlm.ai.
| # | Model | Score | Source |
|---|---|---|---|
| 1 | Claude Opus 5.5 | 78.7% | estimated ± 8.9 pp, low confidence |
| 2 | GPT-6 Astra | 77.7% | estimated ± 4.5 pp, low confidence |
| 3 | Claude Fable 5.1 | 76.1% | estimated ± 8.9 pp, low confidence |
| 4 | Sakana Fugu | 75.1% | estimated ± 4.5 pp, low confidence |
| 5 | Sakana Fugu-Ultra | 75.1% | estimated ± 4.5 pp, low confidence |
| 6 | Claude Mythos 5 | 74.6% | estimated ± 8.9 pp, low confidence |
| 7 | Claude Sonnet 5.5 | 72.9% | estimated ± 8.9 pp, low confidence |
| 8 | Claude Opus 5 | 72.4% | estimated ± 8.9 pp, low confidence |
| 9 | Gemini 3.1 Pro | 71.5% | measured |
| 10 | GPT-5.6 Sol | 70.7% | estimated ± 4.5 pp, low confidence |
| 11 | Muse Spark 1.1 | 68.9% | estimated ± 8.9 pp, low confidence |
| 12 | Claude Opus 4.7 (Adaptive) | 68.9% | estimated ± 4.5 pp, medium confidence |
| 13 | Claude Opus 4.8 | 66.3% | estimated ± 4.5 pp, medium confidence |
| 14 | GPT-5.5 | 66.3% | estimated ± 4.5 pp, medium confidence |
| 15 | Pareto 26.9 | 66.1% | estimated ± 8.9 pp, low confidence |
| 16 | Kimi K3 | 65.9% | estimated ± 4.5 pp, medium confidence |
| 17 | Step 5 Preview | 65.9% | estimated ± 4.5 pp, medium confidence |
| 18 | GPT-5.6 Terra | 63.5% | estimated ± 4.5 pp, medium confidence |
| 19 | Claude Haiku 5.5 | 63.2% | estimated ± 8.9 pp, low confidence |
| 20 | Ornith-1.5-397B | 63.2% | estimated ± 4.5 pp, medium confidence |
| 21 | Gemini 3.5 Flash | 62.7% | estimated ± 4.5 pp, medium confidence |
| 22 | Qwen3.8 Max | 62.4% | estimated ± 4.5 pp, medium confidence |
| 23 | Pareto 26.10 Preview | 61.7% | estimated ± 4.5 pp, medium confidence |
| 24 | Qwen3.7 Max | 61.7% | estimated ± 4.5 pp, medium confidence |
| 25 | GPT-5.6 Luna | 61.3% | estimated ± 4.5 pp, medium confidence |
| 26 | Hy4 preview | 61.3% | estimated ± 4.5 pp, medium confidence |
| 27 | Claude Sonnet 5 | 60.7% | estimated ± 8.9 pp, low confidence |
| 28 | GPT-5.5 Pro | 60.6% | estimated ± 8.9 pp, low confidence |
| 29 | GPT-5.4 Pro | 60.2% | estimated ± 8.9 pp, low confidence |
| 30 | GPT-5.4 | 59.6% | measured |
| 31 | Qwen3.8-Flash-Next | 59.2% | estimated ± 4.5 pp, medium confidence |
| 32 | GLM-5.2 | 57.5% | estimated ± 4.5 pp, medium confidence |
| 33 | Qwen3.8-Omni-Flash | 56.8% | estimated ± 4.5 pp, medium confidence |
| 34 | DeepSeek V4.1 Flash | 56.5% | estimated ± 4.5 pp, medium confidence |
| 35 | Kimi K2.6 | 55.3% | estimated ± 4.5 pp, medium confidence |
| 36 | Beam | 55.3% | estimated ± 4.5 pp, medium confidence |
| 37 | Qwen3.7 Plus | 54.7% | estimated ± 4.5 pp, medium confidence |
| 38 | DeepSeek V4 Pro 0813 | 54.1% | estimated ± 4.5 pp, medium confidence |
| 39 | Interfaze Beta | 53.5% | estimated ± 4.5 pp, medium confidence |
| 40 | Muse Spark | 52.6% | measured |
| 41 | Inkling-Small | 52.3% | estimated ± 4.5 pp, medium confidence |
| 42 | Claude Opus 4.6 | 52.1% | measured |
| 43 | Ornith-1.5-35B-A3B | 51.5% | estimated ± 4.5 pp, medium confidence |
| 44 | Qwen3.8-27B | 51.5% | estimated ± 4.5 pp, medium confidence |
| 45 | MiMo-V2.5-Pro | 51.0% | estimated ± 8.9 pp, low confidence |
| 46 | Solar Pro 4 | 50.9% | estimated ± 4.5 pp, medium confidence |
| 47 | Grok 4.20 | 50.2% | measured |
| 48 | DeepSeek V4 Flash 0731 | 48.6% | estimated ± 4.5 pp, low confidence |
| 49 | Inkling | 48.1% | estimated ± 4.5 pp, low confidence |
| 50 | Kimi K2.5 | 47.4% | estimated ± 4.5 pp, low confidence |
| 51 | Hy3 Preview | 46.4% | estimated ± 4.5 pp, low confidence |
| 52 | MiniMax M2.7 | 45.9% | estimated ± 4.5 pp, low confidence |
| 53 | Nemotron 3 Ultra | 45.9% | estimated ± 4.5 pp, low confidence |
| 54 | Ornith-1.5-9B | 44.6% | estimated ± 4.5 pp, low confidence |
| 55 | Solar Open 2 | 44.4% | estimated ± 4.5 pp, low confidence |
| 56 | GPT-5.4 mini | 44.3% | estimated ± 8.9 pp, low confidence |
| 57 | GLM-5.1 | 44.2% | estimated ± 4.5 pp, low confidence |
| 58 | GLM-5 | 43.7% | estimated ± 4.5 pp, low confidence |
| 59 | Ternary Bonsai 2 27B | 43.2% | estimated ± 4.5 pp, low confidence |
| 60 | A.X K2 | 42.9% | estimated ± 4.5 pp, low confidence |
| 61 | Ling 3.0 Flash | 41.6% | estimated ± 4.5 pp, low confidence |
| 62 | MAI-Thinking-1 | 40.1% | estimated ± 4.5 pp, low confidence |
| 63 | Ling 3.0 Flash FP8 | 39.8% | estimated ± 4.5 pp, low confidence |
| 64 | GPT-5.4 nano | 39.3% | estimated ± 8.9 pp, low confidence |
| 65 | K-EXAONE 2.0 | 36.6% | estimated ± 4.5 pp, low confidence |
| 66 | Gemma 4 31B | 32.8% | estimated ± 8.9 pp, low confidence |
| 67 | Mercury 2.5 | 31.9% | estimated ± 4.5 pp, low confidence |
| 68 | Gemma 4 12B | 31.6% | estimated ± 4.5 pp, low confidence |
| 69 | Trinity-Large-Thinking | 28.5% | estimated ± 4.5 pp, low confidence |
| 70 | Nemotron 3.5 Lightning 30B A3B NVFP4 | 27.7% | estimated ± 4.5 pp, low confidence |
| 71 | Nemotron 3 Nano Omni 30B A3B | 24.3% | estimated ± 4.5 pp, low confidence |
| 72 | ZAYA1-8B | 23.2% | estimated ± 4.5 pp, low confidence |
| 73 | MiniCPM5-2B | 22.5% | estimated ± 4.5 pp, low confidence |
| 74 | LongCat-Flash-Lite-Sparse | 21.9% | estimated ± 4.5 pp, low confidence |
| 75 | Trinity-Large-Preview | 17.5% | estimated ± 4.5 pp, low confidence |
| 76 | Gemma 4 26B A4B | 16.1% | estimated ± 8.9 pp, low confidence |
| 77 | Mellum2-12B-A2.5B-Thinking | 14.2% | estimated ± 4.5 pp, low confidence |
| 78 | ZAYA1-74B-Preview | 14.1% | estimated ± 4.5 pp, low confidence |
| 79 | LLaDA2.2-mini | 8.9% | estimated ± 4.5 pp, low confidence |
| 80 | Soofi S 30B-A3B | 8.5% | estimated ± 4.5 pp, low confidence |
| 81 | Mellum2-12B-A2.5B-Instruct | 7.8% | estimated ± 4.5 pp, low confidence |
| 82 | MiniCPM5-1B | 4.1% | estimated ± 4.5 pp, low confidence |
| 83 | LFM2.5-230M | 4.0% | estimated ± 4.5 pp, low confidence |
All leaderboards
Agentic · terminal
- AA Terminal-Bench 2.1
- AA Terminal-Bench 4.0
- Terminal-Bench 2.0
- Terminal-Bench 3.0
- Terminal-Bench 4.0
- Terminal-Bench-Science 0.1
- Terminal-Bench 2.1
- Terminal-Bench 2.1 (Vals AI)
Agentic · tools
- AA Agentic Index
- AA-AnalystAgent
- AA AutomationBench
- AA EnterpriseOps-Gym
- GDP.pdf
- AA Harvey LAB
- AA ITBench
- AA Tau3 Banking
- Agents' Last Exam
- APEX-Agents
- APEX-Agents-AA
- ApprenticeBench
- AutomationBench
- BFCL v4
- BrowseComp
- Claw-Eval
- CWE-bench v1
- CyberGym
- DeepPlanning
- DeepSearchQA
- DRACO
- ExploitGym
- GDPval-AA
- Gert Labs
- HLE w/ tools
- JobBench
- MCP Atlas
- MCP-Tasks
- OSWorld-Verified
- OSWorld 2.0
- QwenClawBench
- ResearchClawBench
- SkillsBench
- τ²-bench results
- τ³-bench results
- Toolathlon
- Toolathlon-Verified
- VITA-Bench
- WideResearch
Coding
- AA Coding Index
- AA-SciCode
- CursorBench 3.1
- CursorBench 3.2
- CursorBench 4.0
- DeepSWE
- FrontierCode 1.1 Main
- FrontierCode 1.1 Extended
- FrontierSWE v2
- LiveCodeBench
- LiveCodeBench Pro
- LiveCodeBench v6
- NL2Repo
- OpenHarmony Bench
- PostTrainBench v1.1
- ProgramBench
- React Native Evals
- SciCode
- SWE-bench Pro
- SWE-bench Verified
- SWE-Rebench
- Vals LiveCodeBench
- Vals SWE-bench
- Vibe Code Bench
- VulcanBench v3
Math
- AIME 2025
- AIME26
- FrontierMath (legacy)
- FrontierMath v2 (Tier 4)
- FrontierMath v2 (Tiers 1-3)
- HMMT Feb 2025
- HMMT Feb 2026
- HMMT Nov 2025
- IMOAnswerBench
- MATH-500
- MMAnswerBench
Knowledge & reasoning
- AA-GPQA Diamond
- AA-HLE
- ARC-AGI-2
- ARC-AGI-1
- ARC-AGI-3
- Artificial Analysis Intelligence Index
- BioMysteryBench (human-difficult)
- BioMysteryBench (human-solvable)
- C-Eval
- CritPt
- GPQA
- GPQA Diamond
- HealthBench (raw)
- HealthBench Hard
- HealthBench (length-adjusted)
- HealthBench Professional
- HealthBench Professional (raw)
- HLE
- HLE w/o tools
- HLE-Verified
- JevBench 1.4
- LABBench2
- MedXpertQA (Text)
- MMLU
- MMLU-Pro
- MMLU-Pro (Arcee)
- MMLU-Redux
- MMMLU
- AA-Omniscience Accuracy
- SuperGPQA
- Vals GPQA Diamond
- Vals MMLU-Pro
Instruction following
Multilingual
Vision & documents
- AA-MMMU-Pro
- BabyVision
- CharXiv
- CharXiv w/o tools
- ERQA
- MathVision
- MathVision w/ Python
- MedXpertQA (MM)
- MMMU
- MMMU-Pro
- MMMU-Pro w/ Python
- MMVU
- OfficeQA Pro
- OmniDocBench 1.5
- RealWorldQA
- RefCOCO (avg)
- ScreenSpot Pro
- SimpleVQA
- Video-MME (with subtitle)
- VideoMMMU
- V*
- ZeroBench