Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4o
55
2/8 categoriesQwen3.5-35B-A3B
68
Winner · 5/8 categoriesGPT-4o· Qwen3.5-35B-A3B
Pick Qwen3.5-35B-A3B if you want the stronger benchmark profile. GPT-4o only becomes the better choice if reasoning is the priority or 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 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 coding, where it averages 72.6 against 30.4. The single biggest benchmark swing on the page is SWE-bench Verified, 20% to 69.2%. GPT-4o does hit back in reasoning, so the answer changes if that is the part of the workload you care about most.
GPT-4o is also the more expensive model on tokens at $2.50 input / $10.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.5-35B-A3B. That is roughly Infinityx on output cost alone. Qwen3.5-35B-A3B is the reasoning model in the pair, while GPT-4o 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 128K for GPT-4o.
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 | GPT-4o | Qwen3.5-35B-A3B |
|---|---|---|
| AgenticGPT-4o wins | ||
| Terminal-Bench 2.0 | 49% | 40.5% |
| BrowseComp | 59% | 61% |
| OSWorld-Verified | 48% | 54.5% |
| tau2-bench | — | 81.2% |
| CodingQwen3.5-35B-A3B wins | ||
| HumanEval | 58% | — |
| SWE-bench Verified | 20% | 69.2% |
| LiveCodeBench | 38% | 74.6% |
| SWE-bench Pro | 29% | — |
| Multimodal & GroundedQwen3.5-35B-A3B wins | ||
| MMMU-Pro | 74% | 75.1% |
| OfficeQA Pro | 70% | — |
| ReasoningGPT-4o wins | ||
| MuSR | 62% | — |
| BBH | 82% | — |
| LongBench v2 | 62% | 59% |
| MRCRv2 | 63% | — |
| KnowledgeQwen3.5-35B-A3B wins | ||
| MMLU | 66% | — |
| GPQA | 66% | 84.2% |
| SuperGPQA | 64% | 63.4% |
| MMLU-Pro | 64% | 85.3% |
| HLE | 1% | — |
| FrontierScience | 58% | — |
| SimpleQA | 64% | — |
| Instruction FollowingQwen3.5-35B-A3B wins | ||
| IFEval | 82% | 91.9% |
| MultilingualQwen3.5-35B-A3B wins | ||
| MGSM | 82% | — |
| MMLU-ProX | 72% | 81% |
| Mathematics | ||
| AIME 2023 | 66% | — |
| AIME 2024 | 68% | — |
| AIME 2025 | 67% | — |
| HMMT Feb 2023 | 62% | — |
| HMMT Feb 2024 | 64% | — |
| HMMT Feb 2025 | 63% | — |
| BRUMO 2025 | 65% | — |
| MATH-500 | 80% | — |
Qwen3.5-35B-A3B is ahead overall, 68 to 55. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 20% and 69.2%.
Qwen3.5-35B-A3B has the edge for knowledge tasks in this comparison, averaging 79.3 versus 48.7. Inside this category, MMLU-Pro 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 30.4. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
GPT-4o has the edge for reasoning in this comparison, averaging 62.3 versus 59. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
GPT-4o has the edge for agentic tasks in this comparison, averaging 51.2 versus 50.5. Inside this category, Terminal-Bench 2.0 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 72.2. 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 82. 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 75.5. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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