Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Mistral 8x7B v0.2
31
0/8 categoriesQwen3.5-35B-A3B
68
Winner · 7/8 categoriesMistral 8x7B v0.2· Qwen3.5-35B-A3B
Pick Qwen3.5-35B-A3B 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.
Qwen3.5-35B-A3B is clearly ahead on the aggregate, 68 to 31. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.5-35B-A3B's sharpest advantage is in coding, where it averages 72.6 against 13.7. The single biggest benchmark swing on the page is LiveCodeBench, 12% to 74.6%.
Qwen3.5-35B-A3B 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. Qwen3.5-35B-A3B gives you the larger context window at 262K, 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 | Qwen3.5-35B-A3B |
|---|---|---|
| AgenticQwen3.5-35B-A3B wins | ||
| Terminal-Bench 2.0 | 24% | 40.5% |
| BrowseComp | 34% | 61% |
| OSWorld-Verified | 28% | 54.5% |
| tau2-bench | — | 81.2% |
| CodingQwen3.5-35B-A3B wins | ||
| HumanEval | 21% | — |
| SWE-bench Verified | 16% | 69.2% |
| LiveCodeBench | 12% | 74.6% |
| SWE-bench Pro | 14% | — |
| Multimodal & GroundedQwen3.5-35B-A3B wins | ||
| MMMU-Pro | 26% | 75.1% |
| OfficeQA Pro | 40% | — |
| ReasoningQwen3.5-35B-A3B wins | ||
| MuSR | 25% | — |
| BBH | 62% | — |
| LongBench v2 | 39% | 59% |
| MRCRv2 | 38% | — |
| KnowledgeQwen3.5-35B-A3B wins | ||
| MMLU | 29% | — |
| GPQA | 28% | 84.2% |
| SuperGPQA | 26% | 63.4% |
| MMLU-Pro | 52% | 85.3% |
| HLE | 3% | — |
| FrontierScience | 35% | — |
| SimpleQA | 27% | — |
| Instruction FollowingQwen3.5-35B-A3B wins | ||
| IFEval | 67% | 91.9% |
| MultilingualQwen3.5-35B-A3B wins | ||
| MGSM | 62% | — |
| MMLU-ProX | 57% | 81% |
| Mathematics | ||
| AIME 2023 | 29% | — |
| AIME 2024 | 31% | — |
| AIME 2025 | 30% | — |
| HMMT Feb 2023 | 25% | — |
| HMMT Feb 2024 | 27% | — |
| HMMT Feb 2025 | 26% | — |
| BRUMO 2025 | 28% | — |
| MATH-500 | 59% | — |
Qwen3.5-35B-A3B is ahead overall, 68 to 31. The biggest single separator in this matchup is LiveCodeBench, where the scores are 12% and 74.6%.
Qwen3.5-35B-A3B has the edge for knowledge tasks in this comparison, averaging 79.3 versus 28.4. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Qwen3.5-35B-A3B has the edge for coding in this comparison, averaging 72.6 versus 13.7. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
Qwen3.5-35B-A3B has the edge for reasoning in this comparison, averaging 59 versus 34.9. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Qwen3.5-35B-A3B has the edge for agentic tasks in this comparison, averaging 50.5 versus 27.9. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
Qwen3.5-35B-A3B has the edge for multimodal and grounded tasks in this comparison, averaging 75.1 versus 32.3. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Qwen3.5-35B-A3B has the edge for instruction following in this comparison, averaging 91.9 versus 67. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Qwen3.5-35B-A3B has the edge for multilingual tasks in this comparison, averaging 81 versus 58.8. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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