Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 31B
73
Winner · 1/8 categoriesMiniMax M2.7
~66
0/8 categoriesGemma 4 31B· MiniMax M2.7
Pick Gemma 4 31B if you want the stronger benchmark profile. MiniMax M2.7 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Gemma 4 31B is clearly ahead on the aggregate, 73 to 66. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Gemma 4 31B's sharpest advantage is in coding, where it averages 80 against 56.2.
MiniMax M2.7 is also the more expensive model on tokens at $0.30 input / $1.20 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 MiniMax M2.7 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 MiniMax M2.7.
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 | MiniMax M2.7 |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 57% |
| Tau2-Airline | — | 80.0% |
| Tau2-Telecom | — | 84.8% |
| PinchBench | — | 89.8% |
| BFCL v4 | — | 70.6% |
| Toolathlon | — | 46.3% |
| MLE-Bench Lite | — | 66.6% |
| MM-ClawBench | — | 62.7% |
| Claw-Eval | — | 51.9% |
| CodingGemma 4 31B wins | ||
| LiveCodeBench | 80% | — |
| SWE-bench Verified* | — | 75.4% |
| SWE-bench Pro | — | 56.2% |
| SWE Multilingual | — | 76.5% |
| Multi-SWE Bench | — | 52.7% |
| VIBE-Pro | — | 55.6% |
| NL2Repo | — | 39.8% |
| Multimodal & Grounded | ||
| MMMU-Pro | 76.9% | — |
| GDPval-AA | — | 1495 |
| Reasoning | ||
| BBH | 74.4% | — |
| MRCRv2 | 66.4% | — |
| Knowledge | ||
| GPQA | 84.3% | — |
| MMLU-Pro | 85.2% | — |
| HLE | 26.5% | — |
| HLE w/o tools | 19.5% | — |
| GPQA-D | — | 86.2% |
| MMLU-Pro (Arcee) | — | 80.8% |
| Instruction Following | ||
| IFBench | — | 75.7% |
| Multilingual | ||
| Coming soon | ||
| Mathematics | ||
| AIME25 (Arcee) | — | 80.0% |
Gemma 4 31B is ahead overall, 73 to 66.
Gemma 4 31B has the edge for coding in this comparison, averaging 80 versus 56.2. MiniMax M2.7 stays close enough that the answer can still flip depending on your workload.
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