Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Mistral Small 4 (Reasoning)
~64
0/8 categoriesQwen3.5-35B-A3B
67
Winner · 3/8 categoriesMistral Small 4 (Reasoning)· Qwen3.5-35B-A3B
Pick Qwen3.5-35B-A3B if you want the stronger benchmark profile. Mistral Small 4 (Reasoning) only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Qwen3.5-35B-A3B has the cleaner overall profile here, landing at 67 versus 64. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Qwen3.5-35B-A3B's sharpest advantage is in multimodal & grounded, where it averages 75.1 against 60. The single biggest benchmark swing on the page is MMMU-Pro, 60% to 75.1%.
Qwen3.5-35B-A3B gives you the larger context window at 262K, compared with 256K for Mistral Small 4 (Reasoning).
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 Small 4 (Reasoning) | Qwen3.5-35B-A3B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 40.5% |
| BrowseComp | — | 61% |
| OSWorld-Verified | — | 54.5% |
| tau2-bench | — | 81.2% |
| CodingQwen3.5-35B-A3B wins | ||
| LiveCodeBench | 63.6% | 74.6% |
| SWE-bench Verified | — | 69.2% |
| Multimodal & GroundedQwen3.5-35B-A3B wins | ||
| MMMU-Pro | 60% | 75.1% |
| Reasoning | ||
| LongBench v2 | — | 59% |
| KnowledgeQwen3.5-35B-A3B wins | ||
| GPQA | 71.2% | 84.2% |
| MMLU-Pro | 78% | 85.3% |
| SuperGPQA | — | 63.4% |
| Instruction Following | ||
| IFEval | — | 91.9% |
| Multilingual | ||
| MMLU-ProX | — | 81% |
| Mathematics | ||
| AIME 2025 | 83.8% | — |
Qwen3.5-35B-A3B is ahead overall, 67 to 64. The biggest single separator in this matchup is MMMU-Pro, where the scores are 60% and 75.1%.
Qwen3.5-35B-A3B has the edge for knowledge tasks in this comparison, averaging 79.3 versus 75.6. 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 63.6. Inside this category, LiveCodeBench 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 60. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.