Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.4
82
Winner · 7/8 categoriesQwen3.5-35B-A3B
67
0/8 categoriesGPT-5.4· Qwen3.5-35B-A3B
Pick GPT-5.4 if you want the stronger benchmark profile. Qwen3.5-35B-A3B only becomes the better choice if you want the cheaper token bill.
GPT-5.4 is clearly ahead on the aggregate, 82 to 67. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.4's sharpest advantage is in reasoning, where it averages 87.7 against 59. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 75.1% to 40.5%.
GPT-5.4 is also the more expensive model on tokens at $2.50 input / $15.00 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. GPT-5.4 gives you the larger context window at 1.05M, 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 | Qwen3.5-35B-A3B |
|---|---|---|
| AgenticGPT-5.4 wins | ||
| Terminal-Bench 2.0 | 75.1% | 40.5% |
| BrowseComp | 82.7% | 61% |
| OSWorld-Verified | 75% | 54.5% |
| MCP Atlas | 67.2% | — |
| Toolathlon | 54.6% | — |
| tau2-bench | 98.9% | 81.2% |
| CodingGPT-5.4 wins | ||
| HumanEval | 95% | — |
| SWE-bench Verified | 84% | 69.2% |
| LiveCodeBench | 84% | 74.6% |
| SWE-bench Pro | 57.7% | — |
| React Native Evals | 82.6% | — |
| Multimodal & GroundedGPT-5.4 wins | ||
| MMMU-Pro | 81.2% | 75.1% |
| OfficeQA Pro | 96% | — |
| MMMU-Pro w/ Python | 81.5% | — |
| OmniDocBench 1.5 | 0.1090 | — |
| ReasoningGPT-5.4 wins | ||
| MuSR | 94% | — |
| BBH | 97% | — |
| MRCRv2 | 97% | — |
| MRCR v2 64K-128K | 86% | — |
| MRCR v2 128K-256K | 79.3% | — |
| Graphwalks BFS 128K | 93.1% | — |
| Graphwalks Parents 128K | 89.8% | — |
| ARC-AGI-2 | 73.3% | — |
| LongBench v2 | — | 59% |
| KnowledgeGPT-5.4 wins | ||
| MMLU | 99% | — |
| GPQA | 92.8% | 84.2% |
| SuperGPQA | 96% | 63.4% |
| MMLU-Pro | 93% | 85.3% |
| HLE | 48% | — |
| FrontierScience | 91% | — |
| HLE w/o tools | 39.8% | — |
| SimpleQA | 97% | — |
| Instruction FollowingGPT-5.4 wins | ||
| IFEval | 96% | 91.9% |
| MultilingualGPT-5.4 wins | ||
| MGSM | 96% | — |
| MMLU-ProX | 94% | 81% |
| Mathematics | ||
| AIME 2023 | 99% | — |
| AIME 2024 | 99% | — |
| AIME 2025 | 99% | — |
| HMMT Feb 2023 | 96% | — |
| HMMT Feb 2024 | 98% | — |
| HMMT Feb 2025 | 97% | — |
| BRUMO 2025 | 97% | — |
| MATH-500 | 99% | — |
GPT-5.4 is ahead overall, 82 to 67. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 75.1% and 40.5%.
GPT-5.4 has the edge for knowledge tasks in this comparison, averaging 83.1 versus 79.3. Inside this category, SuperGPQA is the benchmark that creates the most daylight between them.
GPT-5.4 has the edge for coding in this comparison, averaging 73.9 versus 72.6. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
GPT-5.4 has the edge for reasoning in this comparison, averaging 87.7 versus 59. Qwen3.5-35B-A3B stays close enough that the answer can still flip depending on your workload.
GPT-5.4 has the edge for agentic tasks in this comparison, averaging 77 versus 50.5. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GPT-5.4 has the edge for multimodal and grounded tasks in this comparison, averaging 87.9 versus 75.1. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GPT-5.4 has the edge for instruction following in this comparison, averaging 96 versus 91.9. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-5.4 has the edge for multilingual tasks in this comparison, averaging 94.7 versus 81. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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