Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Claude Sonnet 4.6
84
Winner · 2/8 categoriesGemma 4 31B
73
1/8 categoriesClaude Sonnet 4.6· Gemma 4 31B
Pick Claude Sonnet 4.6 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.
Claude Sonnet 4.6 is clearly ahead on the aggregate, 84 to 73. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Claude Sonnet 4.6's sharpest advantage is in reasoning, where it averages 87 against 66.4. The single biggest benchmark swing on the page is MMMU-Pro, 95% to 76.9%. Gemma 4 31B does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Claude Sonnet 4.6 is also the more expensive model on tokens at $3.00 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. Gemma 4 31B is the reasoning model in the pair, while Claude Sonnet 4.6 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. Gemma 4 31B gives you the larger context window at 256K, compared with 200K for Claude Sonnet 4.6.
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 | Claude Sonnet 4.6 | Gemma 4 31B |
|---|---|---|
| Agentic | ||
| OSWorld-Verified | 72.5% | — |
| Claw-Eval | 66.3% | — |
| CodingGemma 4 31B wins | ||
| SWE-bench Verified | 79.6% | — |
| FLTEval | 23.7% | — |
| SWE-Rebench | 60.7% | — |
| React Native Evals | 77.9% | — |
| LiveCodeBench | — | 80% |
| Multimodal & GroundedClaude Sonnet 4.6 wins | ||
| MMMU-Pro | 95% | 76.9% |
| OfficeQA Pro | 88% | — |
| ReasoningClaude Sonnet 4.6 wins | ||
| MuSR | 93% | — |
| BBH | 88% | 74.4% |
| LongBench v2 | 83% | — |
| MRCRv2 | — | 66.4% |
| Knowledge | ||
| MMLU | 99% | — |
| GPQA | — | 84.3% |
| MMLU-Pro | — | 85.2% |
| HLE | — | 26.5% |
| HLE w/o tools | — | 19.5% |
| Instruction Following | ||
| Coming soon | ||
| Multilingual | ||
| MGSM | 91% | — |
| MMLU-ProX | 89% | — |
| Mathematics | ||
| AIME 2025 | 98% | — |
| HMMT Feb 2025 | 96% | — |
| BRUMO 2025 | 96% | — |
Claude Sonnet 4.6 is ahead overall, 84 to 73. The biggest single separator in this matchup is MMMU-Pro, where the scores are 95% and 76.9%.
Gemma 4 31B has the edge for coding in this comparison, averaging 80 versus 66.4. Claude Sonnet 4.6 stays close enough that the answer can still flip depending on your workload.
Claude Sonnet 4.6 has the edge for reasoning in this comparison, averaging 87 versus 66.4. Inside this category, BBH is the benchmark that creates the most daylight between them.
Claude Sonnet 4.6 has the edge for multimodal and grounded tasks in this comparison, averaging 91.9 versus 76.9. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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