Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.1-Codex-Max
81
Winner · 3/8 categoriesGranite-4.0-350M
~27
0/8 categoriesGPT-5.1-Codex-Max· Granite-4.0-350M
Pick GPT-5.1-Codex-Max 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-5.1-Codex-Max is clearly ahead on the aggregate, 81 to 27. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.1-Codex-Max's sharpest advantage is in multilingual, where it averages 87.7 against 16.2. The single biggest benchmark swing on the page is MGSM, 89% to 16.2%.
GPT-5.1-Codex-Max is also the more expensive model on tokens at $2.00 input / $8.00 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-5.1-Codex-Max 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-5.1-Codex-Max gives you the larger context window at 400K, 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-5.1-Codex-Max | Granite-4.0-350M |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 90% | — |
| BrowseComp | 85% | — |
| OSWorld-Verified | 82% | — |
| Coding | ||
| HumanEval | 94% | 38% |
| SWE-bench Verified | 77.9% | — |
| LiveCodeBench | 67% | — |
| SWE-bench Pro | 84% | — |
| Multimodal & Grounded | ||
| MMMU-Pro | 85% | — |
| OfficeQA Pro | 92% | — |
| Reasoning | ||
| MuSR | 92% | — |
| BBH | 92% | 33.3% |
| LongBench v2 | 90% | — |
| MRCRv2 | 93% | — |
| KnowledgeGPT-5.1-Codex-Max wins | ||
| MMLU | 98% | 36.2% |
| GPQA | 96% | 26.1% |
| SuperGPQA | 94% | — |
| MMLU-Pro | 82% | 14.4% |
| HLE | 27% | — |
| FrontierScience | 84% | — |
| SimpleQA | 94% | — |
| Instruction FollowingGPT-5.1-Codex-Max wins | ||
| IFEval | 91% | 61.6% |
| MultilingualGPT-5.1-Codex-Max wins | ||
| MGSM | 89% | 16.2% |
| MMLU-ProX | 87% | — |
| Mathematics | ||
| AIME 2023 | 99% | — |
| AIME 2024 | 99% | — |
| AIME 2025 | 98% | — |
| HMMT Feb 2023 | 95% | — |
| HMMT Feb 2024 | 97% | — |
| HMMT Feb 2025 | 96% | — |
| BRUMO 2025 | 96% | — |
| MATH-500 | 93% | — |
GPT-5.1-Codex-Max is ahead overall, 81 to 27. The biggest single separator in this matchup is MGSM, where the scores are 89% and 16.2%.
GPT-5.1-Codex-Max has the edge for knowledge tasks in this comparison, averaging 74.4 versus 18.5. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GPT-5.1-Codex-Max has the edge for instruction following in this comparison, averaging 91 versus 61.6. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-5.1-Codex-Max has the edge for multilingual tasks in this comparison, averaging 87.7 versus 16.2. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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