- MMLU-Pro 85.3
- MMLU-Redux 93.3
- C-Eval 90.2
- SuperGPQA 63.4
- IFEval 91.9
- IFBench 70.2
- MultiChallenge 60
- AA-LCR 58.5
- LongBench v2 59
- GPQA Diamond 84.2
- HMMT Feb 25 89
- HMMT Nov 25 89.2
- SWE-bench Verified 69.2
- Terminal Bench 2 40.5
- LiveCodeBench v6 74.6
- CodeForces 2028
- OJBench 36
- Mean of FullStackBench en 58.1 / FullStackBench zh 55.0 56.5
- BFCL-V4 67.3
- TAU2-Bench 81.2
- VITA-Bench 31.9
- DeepPlanning 22.8
- Browsecomp 61
- Browsecomp-zh 69.5
- WideSearch 57.1
- Seal-0 41.4
- MMMLU 85.2
- MMLU-ProX 81
- NOVA-63 57.1
- INCLUDE 79.7
- Global PIQA 86.6
- PolyMATH 64.4
- WMT24++ 76.3
- MAXIFE 86.6
- MMMU 81.4
- MMMU-Pro 75.1
- MathVision 83.9
- Mathvista(mini) 86.2
- DynaMath 85
- Best of ZEROBench / ZEROBench_sub 34.1 34.1
- VlmsAreBlind 97
- BabyVision 38.4 / 29.6 (with CI / without CI) 38.4
- RealWorldQA 84.1
- MMStar 81.9
- MMBench EN-DEV-v1.1 91.5
- SimpleVQA 58.3
- HallusionBench 67.9
- OmniDocBench1.5 89.3
- CharXiv(RQ) 77.5
- MMLongBench-Doc 59.5
- CC-OCR 80.7
- AI2D_TEST 92.6
- OCRBench 91
- ERQA 64.8
- CountBench 97.8
- RefCOCO(avg) 89.2
- ODInW13 42.6
- EmbSpatialBench 83.1
- RefSpatialBench 63.5
- LingoQA 79.2
- Hypersim 13.1
- SUNRGBD 33.4
- Nuscene 14.6
- Best of VideoMME(w sub.) / VideoMME(w/o sub.) 86.6 86.6
- VideoMMMU 80.4
- MLVU 85.6
- MVBench 74.8
- LVBench 71.4
- MMVU 72.3
- ScreenSpot Pro 68.6
- OSWorld-Verified 54.5
- AndroidWorld 71.1
- TIR-Bench 55.5 / 38.0 (with CI / without CI) 55.5
- V* 92.7 / 89.5 (with CI / without CI) 92.7
- SLAKE 78.7
- PMC-VQA 62
- MedXpertQA-MM 61.4
- HLE w/ CoT 22.4; HLE w/ tool 47.4 47.4
Qwen3.5-35B-A3B
Qwen3.5-35B-A3B places in the top three on 5 benchmarks.
Qwen3.5-35B-A3B
Open weights
- MMMU-Pro 75.1% #40 /90
- IFBench 70.2% #44 /80
- AA-LCR (Artificial Analysis Long Context Reasoning) 58.5% #24 /38
Where it ranks
Its best score on each benchmark, and where that ranks among every model with a score there. We rank each model once, at its best setting.
Knowledge and language
| Benchmark | Score | Rank | Source | |
|---|---|---|---|---|
| BOUQuET | 31.4 | #2 of 18 | Official leaderboard | |
| MMLU-Pro | 85.3% | #23 of 167 | Lab model card | |
| IFEval | 91.9% | #21 of 135 | Lab model card | |
| QIMMA | 61.2% | #6 of 25 | Official leaderboard | |
| HallusionBench | 67.9% | #8 of 31 | Lab model card | |
| Judgemark v2.1 | 81.4 | #12 of 42 | Official leaderboard | |
| MMLU-Redux | 93.3% | #13 of 45 | Lab model card | |
| SEA-HELM | 73.5 | #22 of 71 | Official leaderboard | |
| Humanity's Last Exam | 47.4% | #52 of 154 | Lab model card | |
| MMLU-ProX | 81% | #9 of 23 | Lab model card | |
| KINA | 35.4% | #15 of 37 | Official leaderboard | |
| LMArena (Elo) | 1,394 | #138 of 335 | Official leaderboard | |
| C-Eval | 90.2% | #19 of 46 | Lab model card | |
| INCLUDE | 79.7% | #13 of 31 | Lab model card | |
| MMMLU | 85.2% | #34 of 71 | Lab model card | |
| LongBench v2 | 59% | #17 of 35 | Lab model card | |
| SuperGPQA | 63.4% | #17 of 35 | Lab model card | |
| MultiChallenge | 60% | #21 of 42 | Lab model card | |
| IFBench | 70.2% | #44 of 80 | Lab model card | |
| Vectara Hallucination Leaderboard (2025 dataset) | 10.5% | #64 of 104 | Official leaderboard | |
| AA-LCR (Artificial Analysis Long Context Reasoning) | 58.5% | #24 of 38 | Lab model card | |
| MAXIFE | 86.6% | #7 of 11 | Lab model card | |
| NOVA-63 | 57.1% | #7 of 11 | Lab model card | |
| SimpleVQA | 58.3% | #21 of 30 | Lab model card | |
| Global PIQA | 86.6% | #10 of 14 | Lab model card | |
| Judgemark v4 | 40.5 | #30 of 40 | Official leaderboard | |
| Longform Creative Writing (EQ-Bench) | 44.5 | #93 of 119 | Official leaderboard | |
| WideSearch | 57.1% | #19 of 19 | Lab model card | |
| Seal-0 | 41.4% | #7 of 7 | Lab model card |
Math and science
| Benchmark | Score | Rank | Source | |
|---|---|---|---|---|
| GPQA Diamond | 83.5% | #85 of 229 | Epoch AI run | |
| PolyMath | 64.4% | #9 of 24 | Lab model card | |
| HMMT 2025 | 89% | #26 of 54 | Lab model card | |
| DTBench | 80% | #93 of 180 | Official leaderboard | |
| OTIS Mock AIME 2024-2025 | 70% | #111 of 212 | Epoch AI run | |
| MathArena (Apex, IMO, USAMO) | 4.17% | #28 of 47 | Official leaderboard | |
| HMMT Nov 2025 | 89.2% | #17 of 28 | Lab model card | |
| AIME 2026 | 93.3% | #23 of 31 | Official leaderboard | |
| HMMT 2026 | 81.8% | #26 of 31 | Official leaderboard | |
| MathArena Apex Shortlist | 44.7% | #33 of 38 | Official leaderboard |
Coding
| Benchmark | Score | Rank | Source | |
|---|---|---|---|---|
| LiveCodeBench v6 | 74.6% | #45 of 90 | Lab model card | |
| SWE-bench Verified | 69.2% | #85 of 155 | Lab model card | |
| Codeforces rating | 2,028 | #26 of 47 | Lab model card | |
| OJBench | 36% | #9 of 16 | Lab model card | |
| FullStackBench | 56.5% | #7 of 8 | Lab model card | |
| Code Arena | 1,253 | #115 of 128 | Official leaderboard | |
| ALE-Bench | 648 | #124 of 126 | Official leaderboard | |
| SWE-rebench | 17.1% | #13 of 13 | Official leaderboard |
Agents and tools
| Benchmark | Score | Rank | Source | |
|---|---|---|---|---|
| GameWorld | 43.8% | #6 of 22 | Official leaderboard | |
| AndroidWorld | 71.1% | #8 of 23 | Lab model card | |
| tau2-bench | 81.2% | #30 of 79 | Lab model card | |
| BFCL V4 | 67.3% | #12 of 30 | Lab model card | |
| SnakeBench | 25 | #78 of 188 | Official leaderboard | |
| BrowseComp-ZH | 69.5% | #8 of 19 | Lab model card | |
| ScreenSpot-Pro | 68.6% | #16 of 32 | Lab model card | |
| LisanBench | 447.3 | #70 of 133 | Official leaderboard | |
| LLM Chess | -26 | #66 of 123 | Official leaderboard | |
| Toolathlon | 28.7% | #32 of 50 | Official leaderboard | |
| VITA-Bench | 31.9% | #11 of 16 | Lab model card | |
| DeepPlanning | 22.8% | #7 of 10 | Lab model card | |
| BrowseComp | 61% | #57 of 81 | Lab model card | |
| Terminal-Bench 2.0 | 40.5% | #51 of 63 | Lab model card | |
| OSWorld-Verified | 54.5% | #34 of 41 | Lab model card | |
| MyPCBench | 7.60% | #8 of 9 | Official leaderboard | |
| RuneBench | 0.75 | #76 of 76 | Official leaderboard | |
| AutoResearchBench | 1.94% | #13 of 13 | Official leaderboard | |
| iOSWorld | 12.8% | #6 of 6 | Official leaderboard |
Professional work
| Benchmark | Score | Rank | Source | |
|---|---|---|---|---|
| MedXpertQA-MM | 61.4% | #8 of 21 | Lab model card | |
| LMBuild | 47% | #9 of 18 | Official leaderboard | |
| PMC-VQA | 62% | #6 of 10 | Lab model card | |
| SLAKE | 78.7% | #7 of 10 | Lab model card | |
| Vending-Bench 2 | $463 | #57 of 67 | Official leaderboard | |
| LEXam-hard | 23.9% | #13 of 15 | Official leaderboard |
Vision, audio and robotics
| Benchmark | Score | Rank | Source | |
|---|---|---|---|---|
| CountBench | 97.8% | #2 of 28 | Lab model card | |
| VLMs Are Blind | 97% | #2 of 14 | Lab model card | |
| MathVista | 86.2% | #11 of 70 | Lab model card | |
| MMMU | 81.4% | #17 of 103 | Lab model card | |
| MMStar | 81.9% | #6 of 36 | Lab model card | |
| OCRBench | 91% | #6 of 34 | Lab model card | |
| RefAdv-S | 66.7% | #4 of 19 | Official leaderboard | |
| Hypersim | 13.1 | #2 of 9 | Lab model card | |
| DynaMath | 85% | #6 of 23 | Lab model card | |
| RealWorldQA | 84.1% | #11 of 38 | Lab model card | |
| AI2D | 92.6% | #16 of 52 | Lab model card | |
| Video-MME | 86.6% | #16 of 51 | Lab model card | |
| MMLongBench-Doc | 59.5% | #6 of 19 | Lab model card | |
| MMBench-EN v1.1 | 91.5% | #8 of 23 | Lab model card | |
| ZeroBench | 34.1% | #11 of 31 | Lab model card | |
| OCRBench v2 | 65.3% | #5 of 14 | Official leaderboard | |
| MATH-Vision (MathVision) | 83.9% | #19 of 51 | Lab model card | |
| ERQA | 64.8% | #14 of 36 | Lab model card | |
| MMVU | 72.3% | #11 of 26 | Lab model card | |
| MVBench | 74.8% | #10 of 23 | Lab model card | |
| MMMU-Pro | 75.1% | #40 of 90 | Lab model card | |
| VideoMME v2 (with subtitles) | 25.8% | #14 of 31 | Official leaderboard | |
| MLVU | 85.6% | #10 of 22 | Lab model card | |
| V* | 92.7% | #8 of 17 | Lab model card | |
| RefSpatial-Bench | 63.5% | #8 of 16 | Lab model card | |
| GeoGuesser environment (FineEnvs) | 0.33 | #7 of 14 | Official leaderboard | |
| Nuscene | 14.6 | #4 of 8 | Lab model card | |
| TIR-Bench | 55.5% | #3 of 6 | Lab model card | |
| LVBench | 71.4% | #17 of 31 | Lab model card | |
| RefCOCO / RefCOCO+ / RefCOCOg | 89.2% | #16 of 29 | Lab model card | |
| CharXiv | 77.5% | #44 of 75 | Lab model card | |
| Video-MMMU | 80.4% | #22 of 36 | Lab model card | |
| EmbSpatialBench | 83.1% | #10 of 16 | Lab model card | |
| CC-OCR | 80.7% | #9 of 14 | Lab model card | |
| SUNRGBD | 33.4 | #6 of 9 | Lab model card | |
| ODinW-13 | 42.6% | #9 of 13 | Lab model card | |
| LingoQA | 79.2% | #7 of 10 | Lab model card | |
| BabyVision | 38.4% | #21 of 26 | Lab model card | |
| AgentWorldBench | 47.7 | #12 of 13 | Official leaderboard | |
| CiteVQA | 10.7% | #15 of 16 | Official leaderboard |
Safety and security
| Benchmark | Score | Rank | Source | |
|---|---|---|---|---|
| SusVibes | 18.3% | #4 of 10 | Official leaderboard | |
| nocot-bench (NCRI) | 60.1 | #137 of 185 | Official leaderboard |
Other
| Benchmark | Score | Rank | Source | |
|---|---|---|---|---|
| Conceptual Reasoning Index | 42.1 | #94 of 151 | Official leaderboard | |
| LMCA | 29.5% | #92 of 147 | Official leaderboard | |
| ACCoRD | 55.3 | #106 of 152 | Official leaderboard | |
| Political Science LLM Benchmark | 65% | #30 of 42 | Official leaderboard |
Its lab's numbers put it at 144.6, in line with its ECI
The Self-Reported Index fits one score to the 68 capability results Qwen reported for this model, on the scale of the Epoch Capabilities Index. Labs choose which benchmarks to show and how to run them, so read it as the lab's claim, not an independent measurement. The 90% interval runs from 143.6 to 145.5. How we compute it.
- CountBench 97.8
- VLMs Are Blind 97
- MMLU-Redux 93.3
- V* 92.7
- AI2D 92.6
- IFEval 91.9
- MMBench-EN v1.1 91.5
- OCRBench 91
- C-Eval 90.2
- HMMT Nov 2025 89.2
- RefCOCO / RefCOCO+ / RefCOCOg 89.2
- HMMT 2025 89
- Global PIQA 86.6
- MAXIFE 86.6
- Video-MME 86.6
- MathVista 86.2
- MLVU 85.6
- MMLU-Pro 85.3
- MMMLU 85.2
- DynaMath 85
- GPQA Diamond 84.2
- RealWorldQA 84.1
- MATH-Vision (MathVision) 83.9
- EmbSpatialBench 83.1
- MMStar 81.9
- MMMU 81.4
- tau2-bench 81.2
- MMLU-ProX 81
- CC-OCR 80.7
- Video-MMMU 80.4
- INCLUDE 79.7
- LingoQA 79.2
- SLAKE 78.7
- CharXiv 77.5
- MMMU-Pro 75.1
- MVBench 74.8
- LiveCodeBench v6 74.6
- MMVU 72.3
- LVBench 71.4
- AndroidWorld 71.1
- IFBench 70.2
- BrowseComp-ZH 69.5
- SWE-bench Verified 69.2
- ScreenSpot-Pro 68.6
- HallusionBench 67.9
- BFCL V4 67.3
- PolyMath 64.4
- SuperGPQA 63.4
- PMC-VQA 62
- MedXpertQA-MM 61.4
- BrowseComp 61
- MultiChallenge 60
- MMLongBench-Doc 59.5
- LongBench v2 59
- AA-LCR (Artificial Analysis Long Context Reasoning) 58.5
- SimpleVQA 58.3
- WideSearch 57.1
- NOVA-63 57.1
- FullStackBench 56.5
- OSWorld-Verified 54.5
- Humanity's Last Exam 47.4
- ODinW-13 42.6
- Terminal-Bench 2.0 40.5
- BabyVision 38.4
- OJBench 36
- ZeroBench 34.1
- VITA-Bench 31.9
- DeepPlanning 22.8
Its lab's numbers next to 3 leaderboards
Where the benchmark's own leaderboard, or Epoch AI's run, also has this model. Gaps mostly come from different harnesses, effort levels and task subsets. Every pair.
| Benchmark | Lab says | Board says | Gap |
|---|---|---|---|
| GPQA Diamond | 84.2% GPQA Diamond | 83.5% Epoch AI run | +0.7 pts |
| MATH-Vision (MathVision) | 83.9% MathVision | 83.9% Leaderboard | Same |
| MMLU-Pro | 85.3% MMLU-Pro | 85.3% Leaderboard | Same |
Qwen's models over time
Qwen's models in color, against every other model on the Epoch Capabilities Index, by release date.
Source: Epoch Capabilities Index, Epoch AI (CC BY 4.0)
Its launch post
The benchmarks the lab chose to headline, with the scores it gave.