Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 E2B
~39
0/8 categoriesMiniMax M2.7
~66
Winner · 1/8 categoriesGemma 4 E2B· MiniMax M2.7
Pick MiniMax M2.7 if you want the stronger benchmark profile. Gemma 4 E2B only becomes the better choice if you want the cheaper token bill or you want the stronger reasoning-first profile.
MiniMax M2.7 is clearly ahead on the aggregate, 66 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
MiniMax M2.7's sharpest advantage is in coding, where it averages 56.2 against 44.
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 E2B. That is roughly Infinityx on output cost alone. Gemma 4 E2B 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. MiniMax M2.7 gives you the larger context window at 200K, compared with 128K for Gemma 4 E2B.
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 E2B | 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% |
| CodingMiniMax M2.7 wins | ||
| LiveCodeBench | 44% | — |
| 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 | 44.2% | — |
| GDPval-AA | — | 1495 |
| Reasoning | ||
| BBH | 21.9% | — |
| MRCRv2 | 19.1% | — |
| Knowledge | ||
| GPQA | 43.4% | — |
| MMLU-Pro | 60% | — |
| GPQA-D | — | 86.2% |
| MMLU-Pro (Arcee) | — | 80.8% |
| Instruction Following | ||
| IFBench | — | 75.7% |
| Multilingual | ||
| Coming soon | ||
| Mathematics | ||
| AIME25 (Arcee) | — | 80.0% |
MiniMax M2.7 is ahead overall, 66 to 39.
MiniMax M2.7 has the edge for coding in this comparison, averaging 56.2 versus 44. Gemma 4 E2B stays close enough that the answer can still flip depending on your workload.
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