Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.4 Pro
92
Winner · 7/8 categoriesQwen3.5-35B-A3B
67
0/8 categoriesGPT-5.4 Pro· Qwen3.5-35B-A3B
Pick GPT-5.4 Pro 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 Pro is clearly ahead on the aggregate, 92 to 67. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.4 Pro's sharpest advantage is in agentic, where it averages 87.7 against 50.5. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 90% to 40.5%.
GPT-5.4 Pro is also the more expensive model on tokens at $30.00 input / $180.00 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 Pro 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 Pro | Qwen3.5-35B-A3B |
|---|---|---|
| AgenticGPT-5.4 Pro wins | ||
| Terminal-Bench 2.0 | 90% | 40.5% |
| BrowseComp | 89.3% | 61% |
| OSWorld-Verified | 84% | 54.5% |
| tau2-bench | — | 81.2% |
| CodingGPT-5.4 Pro wins | ||
| HumanEval | 95% | — |
| SWE-bench Verified | 86% | 69.2% |
| LiveCodeBench | 86% | 74.6% |
| SWE-bench Pro | 89% | — |
| Multimodal & GroundedGPT-5.4 Pro wins | ||
| MMMU-Pro | 94% | 75.1% |
| ReasoningGPT-5.4 Pro wins | ||
| MuSR | 95% | — |
| BBH | 98% | — |
| LongBench v2 | 95% | 59% |
| MRCRv2 | 97% | — |
| KnowledgeGPT-5.4 Pro wins | ||
| MMLU | 99% | — |
| GPQA | 99% | 84.2% |
| SuperGPQA | 97% | 63.4% |
| MMLU-Pro | 94% | 85.3% |
| HLE | 50% | — |
| FrontierScience | 92% | — |
| SimpleQA | 97% | — |
| Instruction FollowingGPT-5.4 Pro wins | ||
| IFEval | 97% | 91.9% |
| MultilingualGPT-5.4 Pro wins | ||
| MGSM | 97% | — |
| MMLU-ProX | 95% | 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 Pro is ahead overall, 92 to 67. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 90% and 40.5%.
GPT-5.4 Pro has the edge for knowledge tasks in this comparison, averaging 84.9 versus 79.3. Inside this category, SuperGPQA is the benchmark that creates the most daylight between them.
GPT-5.4 Pro has the edge for coding in this comparison, averaging 87.2 versus 72.6. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
GPT-5.4 Pro has the edge for reasoning in this comparison, averaging 95.7 versus 59. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
GPT-5.4 Pro has the edge for agentic tasks in this comparison, averaging 87.7 versus 50.5. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GPT-5.4 Pro has the edge for multimodal and grounded tasks in this comparison, averaging 94 versus 75.1. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GPT-5.4 Pro has the edge for instruction following in this comparison, averaging 97 versus 91.9. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-5.4 Pro has the edge for multilingual tasks in this comparison, averaging 95.7 versus 81. Inside this category, MMLU-ProX 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.