Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.4 nano
58
0/8 categoriesQwen3.5-35B-A3B
67
Winner · 5/8 categoriesGPT-5.4 nano· Qwen3.5-35B-A3B
Pick Qwen3.5-35B-A3B if you want the stronger benchmark profile. GPT-5.4 nano only becomes the better choice if you need the larger 400K context window.
Qwen3.5-35B-A3B is clearly ahead on the aggregate, 67 to 58. 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 knowledge, where it averages 79.3 against 53.2. The single biggest benchmark swing on the page is OSWorld-Verified, 39% to 54.5%.
GPT-5.4 nano is also the more expensive model on tokens at $0.20 input / $1.25 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. GPT-5.4 nano gives you the larger context window at 400K, compared with 262K for Qwen3.5-35B-A3B.
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-5.4 nano | Qwen3.5-35B-A3B |
|---|---|---|
| AgenticQwen3.5-35B-A3B wins | ||
| Terminal-Bench 2.0 | 46.3% | 40.5% |
| OSWorld-Verified | 39% | 54.5% |
| MCP Atlas | 56.1% | — |
| Toolathlon | 35.5% | — |
| tau2-bench | 92.5% | 81.2% |
| BrowseComp | — | 61% |
| CodingQwen3.5-35B-A3B wins | ||
| SWE-bench Pro | 52.4% | — |
| SWE-bench Verified | — | 69.2% |
| LiveCodeBench | — | 74.6% |
| Multimodal & GroundedQwen3.5-35B-A3B wins | ||
| MMMU-Pro | 66.1% | 75.1% |
| MMMU-Pro w/ Python | 69.5% | — |
| OmniDocBench 1.5 | 0.2419 | — |
| ReasoningQwen3.5-35B-A3B wins | ||
| MRCRv2 | 38.7% | — |
| MRCR v2 64K-128K | 44.2% | — |
| MRCR v2 128K-256K | 33.1% | — |
| Graphwalks BFS 128K | 73.4% | — |
| Graphwalks Parents 128K | 50.8% | — |
| LongBench v2 | — | 59% |
| KnowledgeQwen3.5-35B-A3B wins | ||
| GPQA | 82.8% | 84.2% |
| HLE | 37.7% | — |
| HLE w/o tools | 24.3% | — |
| MMLU-Pro | — | 85.3% |
| SuperGPQA | — | 63.4% |
| Instruction Following | ||
| IFEval | — | 91.9% |
| Multilingual | ||
| MMLU-ProX | — | 81% |
| Mathematics | ||
| Coming soon | ||
Qwen3.5-35B-A3B is ahead overall, 67 to 58. The biggest single separator in this matchup is OSWorld-Verified, where the scores are 39% and 54.5%.
Qwen3.5-35B-A3B has the edge for knowledge tasks in this comparison, averaging 79.3 versus 53.2. 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 52.4. GPT-5.4 nano stays close enough that the answer can still flip depending on your workload.
Qwen3.5-35B-A3B has the edge for reasoning in this comparison, averaging 59 versus 38.7. GPT-5.4 nano stays close enough that the answer can still flip depending on your workload.
Qwen3.5-35B-A3B has the edge for agentic tasks in this comparison, averaging 50.5 versus 42.9. Inside this category, OSWorld-Verified 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 66.1. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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