Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.4
82
Winner · 3/8 categoriesGranite-4.0-350M
~27
0/8 categoriesGPT-5.4· Granite-4.0-350M
Pick GPT-5.4 if you want the stronger benchmark profile. Granite-4.0-350M only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
GPT-5.4 is clearly ahead on the aggregate, 82 to 27. 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 multilingual, where it averages 94.7 against 16.2. The single biggest benchmark swing on the page is MGSM, 96% to 16.2%.
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 Granite-4.0-350M. That is roughly Infinityx on output cost alone. GPT-5.4 is the reasoning model in the pair, while Granite-4.0-350M 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. GPT-5.4 gives you the larger context window at 1.05M, compared with 32K for Granite-4.0-350M.
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 | Granite-4.0-350M |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 75.1% | — |
| BrowseComp | 82.7% | — |
| OSWorld-Verified | 75% | — |
| MCP Atlas | 67.2% | — |
| Toolathlon | 54.6% | — |
| tau2-bench | 98.9% | — |
| Coding | ||
| HumanEval | 95% | 38% |
| SWE-bench Verified | 84% | — |
| LiveCodeBench | 84% | — |
| SWE-bench Pro | 57.7% | — |
| React Native Evals | 82.6% | — |
| Multimodal & Grounded | ||
| MMMU-Pro | 81.2% | — |
| OfficeQA Pro | 96% | — |
| MMMU-Pro w/ Python | 81.5% | — |
| OmniDocBench 1.5 | 0.1090 | — |
| Reasoning | ||
| MuSR | 94% | — |
| BBH | 97% | 33.3% |
| 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% | — |
| KnowledgeGPT-5.4 wins | ||
| MMLU | 99% | 36.2% |
| GPQA | 92.8% | 26.1% |
| SuperGPQA | 96% | — |
| MMLU-Pro | 93% | 14.4% |
| HLE | 48% | — |
| FrontierScience | 91% | — |
| HLE w/o tools | 39.8% | — |
| SimpleQA | 97% | — |
| Instruction FollowingGPT-5.4 wins | ||
| IFEval | 96% | 61.6% |
| MultilingualGPT-5.4 wins | ||
| MGSM | 96% | 16.2% |
| MMLU-ProX | 94% | — |
| 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 27. The biggest single separator in this matchup is MGSM, where the scores are 96% and 16.2%.
GPT-5.4 has the edge for knowledge tasks in this comparison, averaging 83.1 versus 18.5. Inside this category, MMLU-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 61.6. 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 16.2. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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