Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 31B
73
1/8 categoriesGPT-5.4
82
Winner · 3/8 categoriesGemma 4 31B· GPT-5.4
Pick GPT-5.4 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-5.4 is clearly ahead on the aggregate, 82 to 73. 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 89.9 against 66.4. The single biggest benchmark swing on the page is MRCRv2, 66.4% to 97%. 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-5.4 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 Gemma 4 31B. That is roughly Infinityx on output cost alone. GPT-5.4 gives you the larger context window at 1.05M, 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-5.4 |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 75.1% |
| BrowseComp | — | 82.7% |
| OSWorld-Verified | — | 75% |
| MCP Atlas | — | 67.2% |
| Toolathlon | — | 54.6% |
| Tau2-Telecom | — | 98.9% |
| Claw-Eval | — | 66.3% |
| CodingGemma 4 31B wins | ||
| LiveCodeBench | 80% | 84% |
| HumanEval | — | 95% |
| SWE-bench Verified | — | 84% |
| SWE-bench Pro | — | 57.7% |
| React Native Evals | — | 82.6% |
| Multimodal & GroundedGPT-5.4 wins | ||
| MMMU-Pro | 76.9% | 81.2% |
| OfficeQA Pro | — | 96% |
| MMMU-Pro w/ Python | — | 81.5% |
| ReasoningGPT-5.4 wins | ||
| BBH | 74.4% | 97% |
| MRCRv2 | 66.4% | 97% |
| MuSR | — | 94% |
| LongBench v2 | — | 95% |
| 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 | ||
| GPQA | 84.3% | 92.8% |
| MMLU-Pro | 85.2% | 93% |
| HLE | 26.5% | 48% |
| HLE w/o tools | 19.5% | 39.8% |
| MMLU | — | 99% |
| SuperGPQA | — | 96% |
| FrontierScience | — | 91% |
| SimpleQA | — | 97% |
| Instruction Following | ||
| IFEval | — | 96% |
| Multilingual | ||
| MGSM | — | 96% |
| 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 73. The biggest single separator in this matchup is MRCRv2, where the scores are 66.4% and 97%.
GPT-5.4 has the edge for knowledge tasks in this comparison, averaging 83.1 versus 61.3. Inside this category, HLE is the benchmark that creates the most daylight between them.
Gemma 4 31B has the edge for coding in this comparison, averaging 80 versus 73.9. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
GPT-5.4 has the edge for reasoning in this comparison, averaging 89.9 versus 66.4. Inside this category, MRCRv2 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 76.9. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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