Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Qwen3 235B 2507 (Reasoning)
55
0/8 categoriesQwen3.5-35B-A3B
67
Winner · 7/8 categoriesQwen3 235B 2507 (Reasoning)· Qwen3.5-35B-A3B
Pick Qwen3.5-35B-A3B if you want the stronger benchmark profile. Qwen3 235B 2507 (Reasoning) only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Qwen3.5-35B-A3B is clearly ahead on the aggregate, 67 to 55. 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 multimodal & grounded, where it averages 75.1 against 42.1. The single biggest benchmark swing on the page is SWE-bench Verified, 16% to 69.2%.
Qwen3.5-35B-A3B gives you the larger context window at 262K, compared with 128K for Qwen3 235B 2507 (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 | Qwen3 235B 2507 (Reasoning) | Qwen3.5-35B-A3B |
|---|---|---|
| AgenticQwen3.5-35B-A3B wins | ||
| Terminal-Bench 2.0 | 47% | 40.5% |
| BrowseComp | 48% | 61% |
| OSWorld-Verified | 43% | 54.5% |
| tau2-bench | — | 81.2% |
| CodingQwen3.5-35B-A3B wins | ||
| HumanEval | 32% | — |
| SWE-bench Verified | 16% | 69.2% |
| LiveCodeBench | 74.1% | 74.6% |
| SWE-bench Pro | 29% | — |
| Multimodal & GroundedQwen3.5-35B-A3B wins | ||
| MMMU-Pro | 38% | 75.1% |
| OfficeQA Pro | 47% | — |
| ReasoningQwen3.5-35B-A3B wins | ||
| MuSR | 36% | — |
| BBH | 63% | — |
| LongBench v2 | 58% | 59% |
| MRCRv2 | 58% | — |
| KnowledgeQwen3.5-35B-A3B wins | ||
| MMLU | 40% | — |
| GPQA | 81.1% | 84.2% |
| SuperGPQA | 64.9% | 63.4% |
| MMLU-Pro | 84.4% | 85.3% |
| HLE | 6% | — |
| FrontierScience | 42% | — |
| SimpleQA | 38% | — |
| Instruction FollowingQwen3.5-35B-A3B wins | ||
| IFEval | 87.8% | 91.9% |
| MultilingualQwen3.5-35B-A3B wins | ||
| MGSM | 62% | — |
| MMLU-ProX | 81% | 81% |
| Mathematics | ||
| AIME 2023 | 40% | — |
| AIME 2024 | 42% | — |
| AIME 2025 | 92.3% | — |
| HMMT Feb 2023 | 36% | — |
| HMMT Feb 2024 | 38% | — |
| HMMT Feb 2025 | 37% | — |
| BRUMO 2025 | 39% | — |
| MATH-500 | 60% | — |
Qwen3.5-35B-A3B is ahead overall, 67 to 55. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 16% and 69.2%.
Qwen3.5-35B-A3B has the edge for knowledge tasks in this comparison, averaging 79.3 versus 50. 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 43.3. Inside this category, SWE-bench Verified 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 52.1. 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 45.9. 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 42.1. 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 87.8. 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 74.4. Qwen3 235B 2507 (Reasoning) stays close enough that the answer can still flip depending on your workload.
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