Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 31B
73
1/8 categoriesGPT-5.1-Codex-Max
81
Winner · 3/8 categoriesGemma 4 31B· GPT-5.1-Codex-Max
Pick GPT-5.1-Codex-Max if you want the stronger benchmark profile. Gemma 4 31B only becomes the better choice if coding is the priority or you want the cheaper token bill.
GPT-5.1-Codex-Max is clearly ahead on the aggregate, 81 to 73. 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 reasoning, where it averages 91.5 against 66.4. The single biggest benchmark swing on the page is MRCRv2, 66.4% to 93%. Gemma 4 31B does hit back in coding, so the answer changes if that is the part of the workload you care about most.
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 Gemma 4 31B. That is roughly Infinityx on output cost alone. GPT-5.1-Codex-Max gives you the larger context window at 400K, compared with 256K for Gemma 4 31B.
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 | Gemma 4 31B | GPT-5.1-Codex-Max |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 90% |
| BrowseComp | — | 85% |
| OSWorld-Verified | — | 82% |
| CodingGemma 4 31B wins | ||
| LiveCodeBench | 80% | 67% |
| HumanEval | — | 94% |
| SWE-bench Verified | — | 77.9% |
| SWE-bench Pro | — | 84% |
| Multimodal & GroundedGPT-5.1-Codex-Max wins | ||
| MMMU-Pro | 76.9% | 85% |
| OfficeQA Pro | — | 92% |
| ReasoningGPT-5.1-Codex-Max wins | ||
| BBH | 74.4% | 92% |
| MRCRv2 | 66.4% | 93% |
| MuSR | — | 92% |
| LongBench v2 | — | 90% |
| KnowledgeGPT-5.1-Codex-Max wins | ||
| GPQA | 84.3% | 96% |
| MMLU-Pro | 85.2% | 82% |
| HLE | 26.5% | 27% |
| HLE w/o tools | 19.5% | — |
| MMLU | — | 98% |
| SuperGPQA | — | 94% |
| FrontierScience | — | 84% |
| SimpleQA | — | 94% |
| Instruction Following | ||
| IFEval | — | 91% |
| Multilingual | ||
| MGSM | — | 89% |
| 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 73. The biggest single separator in this matchup is MRCRv2, where the scores are 66.4% and 93%.
GPT-5.1-Codex-Max has the edge for knowledge tasks in this comparison, averaging 74.4 versus 61.3. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Gemma 4 31B has the edge for coding in this comparison, averaging 80 versus 76.1. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
GPT-5.1-Codex-Max has the edge for reasoning in this comparison, averaging 91.5 versus 66.4. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
GPT-5.1-Codex-Max has the edge for multimodal and grounded tasks in this comparison, averaging 88.2 versus 76.9. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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