Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Mistral 8x7B v0.2
27
0/8 categoriesQwen3.6 Plus
69
Winner · 7/8 categoriesMistral 8x7B v0.2· Qwen3.6 Plus
Pick Qwen3.6 Plus if you want the stronger benchmark profile. Mistral 8x7B v0.2 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.6 Plus is clearly ahead on the aggregate, 69 to 27. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.6 Plus's sharpest advantage is in coding, where it averages 64.9 against 12. The single biggest benchmark swing on the page is GPQA, 28% to 90.4%.
Qwen3.6 Plus is the reasoning model in the pair, while Mistral 8x7B v0.2 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. Qwen3.6 Plus gives you the larger context window at 1M, compared with 32K for Mistral 8x7B v0.2.
BenchLM keeps the benchmark table and the operator tradeoffs on the same page so a better score does not hide a materially slower, pricier, or smaller-context model.
Runtime metrics show N/A when BenchLM does not have a sourced snapshot for that exact model. The scoring rules and freshness policy are documented on the methodology page.
| Benchmark | Mistral 8x7B v0.2 | Qwen3.6 Plus |
|---|---|---|
| AgenticQwen3.6 Plus wins | ||
| Terminal-Bench 2.0 | 24% | 61.6% |
| BrowseComp | 34% | — |
| Claw-Eval | — | 58.7% |
| QwenClawBench | — | 57.2% |
| QwenWebBench | — | 1502 |
| TAU3-Bench | — | 70.7% |
| VITA-Bench | — | 44.3% |
| DeepPlanning | — | 41.5% |
| Toolathlon | — | 39.8% |
| MCP Atlas | — | 48.2% |
| MCP-Tasks | — | 74.1% |
| WideResearch | — | 74.3% |
| OSWorld-Verified | — | 62.5% |
| CodingQwen3.6 Plus wins | ||
| LiveCodeBench | 12% | — |
| SWE-bench Verified | — | 78.8% |
| SWE-bench Pro | — | 56.6% |
| SWE Multilingual | — | 73.8% |
| LiveCodeBench v6 | — | 87.1% |
| NL2Repo | — | 37.9% |
| Multimodal & GroundedQwen3.6 Plus wins | ||
| MMMU-Pro | 26% | 78.8% |
| OfficeQA Pro | 40% | — |
| MMMU | — | 86.0% |
| RealWorldQA | — | 85.4% |
| OmniDocBench 1.5 | — | 91.2% |
| Video-MME (with subtitle) | — | 87.8% |
| Video-MME (w/o subtitle) | — | 84.2% |
| MathVision | — | 88.0% |
| We-Math | — | 89.0% |
| DynaMath | — | 88.0% |
| MStar | — | 83.3% |
| SimpleVQA | — | 67.3% |
| ChatCVQA | — | 81.5% |
| MMLongBench-Doc | — | 62.0% |
| CC-OCR | — | 83.4% |
| AI2D_TEST | — | 94.4% |
| CountBench | — | 97.6% |
| RefCOCO (avg) | — | 93.5% |
| ODINW13 | — | 51.8% |
| ERQA | — | 65.7% |
| VideoMMMU | — | 84.0% |
| MLVU (M-Avg) | — | 86.7% |
| ScreenSpot Pro | — | 68.2% |
| ReasoningQwen3.6 Plus wins | ||
| MuSR | 25% | — |
| BBH | 62% | — |
| LongBench v2 | 39% | 62% |
| AI-Needle | — | 68.3% |
| KnowledgeQwen3.6 Plus wins | ||
| MMLU | 29% | — |
| GPQA | 28% | 90.4% |
| SuperGPQA | 26% | 71.6% |
| SimpleQA | 27% | — |
| MMLU-Pro | — | 88.5% |
| MMLU-Redux | — | 94.5% |
| C-Eval | — | 93.3% |
| HLE | — | 28.8% |
| Instruction FollowingQwen3.6 Plus wins | ||
| IFEval | 67% | 94.3% |
| IFBench | — | 74.2% |
| MultilingualQwen3.6 Plus wins | ||
| MGSM | 62% | — |
| MMLU-ProX | — | 84.7% |
| NOVA-63 | — | 57.9% |
| INCLUDE | — | 85.1% |
| PolyMath | — | 77.4% |
| VWT2k-lite | — | 84.3% |
| MAXIFE | — | 88.2% |
| Mathematics | ||
| AIME 2025 | 30% | — |
| HMMT Feb 2025 | 26% | — |
| AIME26 | — | 95.3% |
| HMMT Feb 2025 | — | 96.7% |
| HMMT Nov 2025 | — | 94.6% |
| HMMT Feb 2026 | — | 87.8% |
| MMAnswerBench | — | 83.8% |
Qwen3.6 Plus is ahead overall, 69 to 27. The biggest single separator in this matchup is GPQA, where the scores are 28% and 90.4%.
Qwen3.6 Plus has the edge for knowledge tasks in this comparison, averaging 66 versus 27. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Qwen3.6 Plus has the edge for coding in this comparison, averaging 64.9 versus 12. Mistral 8x7B v0.2 stays close enough that the answer can still flip depending on your workload.
Qwen3.6 Plus has the edge for reasoning in this comparison, averaging 62 versus 33.4. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Qwen3.6 Plus has the edge for agentic tasks in this comparison, averaging 62 versus 27.8. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Qwen3.6 Plus has the edge for multimodal and grounded tasks in this comparison, averaging 78.8 versus 32.3. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Qwen3.6 Plus has the edge for instruction following in this comparison, averaging 94.3 versus 67. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Qwen3.6 Plus has the edge for multilingual tasks in this comparison, averaging 84.7 versus 62. Mistral 8x7B v0.2 stays close enough that the answer can still flip depending on your workload.
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