Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 E2B
~39
2/8 categoriesMistral 8x7B
44
Winner · 2/8 categoriesGemma 4 E2B· Mistral 8x7B
Pick Mistral 8x7B if you want the stronger benchmark profile. Gemma 4 E2B only becomes the better choice if coding is the priority or you need the larger 128K context window.
Mistral 8x7B is clearly ahead on the aggregate, 44 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Mistral 8x7B's sharpest advantage is in reasoning, where it averages 56.7 against 19.1. The single biggest benchmark swing on the page is BBH, 21.9% to 67.1%. Gemma 4 E2B does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Gemma 4 E2B is the reasoning model in the pair, while Mistral 8x7B 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 E2B gives you the larger context window at 128K, compared with 32K for Mistral 8x7B.
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 | Mistral 8x7B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 40% |
| BrowseComp | — | 47% |
| OSWorld-Verified | — | 38% |
| CodingGemma 4 E2B wins | ||
| LiveCodeBench | 44% | 23% |
| HumanEval | — | 32.3% |
| SWE-bench Verified | — | 28% |
| SWE-bench Pro | — | 28% |
| Multimodal & GroundedMistral 8x7B wins | ||
| MMMU-Pro | 44.2% | 42% |
| OfficeQA Pro | — | 56% |
| ReasoningMistral 8x7B wins | ||
| BBH | 21.9% | 67.1% |
| MRCRv2 | 19.1% | 53% |
| MuSR | — | 61% |
| LongBench v2 | — | 57% |
| KnowledgeGemma 4 E2B wins | ||
| GPQA | 43.4% | 64% |
| MMLU-Pro | 60% | 65% |
| MMLU | — | 71.3% |
| SuperGPQA | — | 62% |
| HLE | — | 8% |
| FrontierScience | — | 56% |
| SimpleQA | — | 63% |
| Instruction Following | ||
| IFEval | — | 78% |
| Multilingual | ||
| MGSM | — | 74% |
| MMLU-ProX | — | 71% |
| Mathematics | ||
| AIME 2023 | — | 65% |
| AIME 2024 | — | 67% |
| AIME 2025 | — | 66% |
| HMMT Feb 2023 | — | 61% |
| HMMT Feb 2024 | — | 63% |
| HMMT Feb 2025 | — | 62% |
| BRUMO 2025 | — | 64% |
| MATH-500 | — | 73% |
Mistral 8x7B is ahead overall, 44 to 39. The biggest single separator in this matchup is BBH, where the scores are 21.9% and 67.1%.
Gemma 4 E2B has the edge for knowledge tasks in this comparison, averaging 54.1 versus 49.5. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Gemma 4 E2B has the edge for coding in this comparison, averaging 44 versus 26.1. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
Mistral 8x7B has the edge for reasoning in this comparison, averaging 56.7 versus 19.1. Inside this category, BBH is the benchmark that creates the most daylight between them.
Mistral 8x7B has the edge for multimodal and grounded tasks in this comparison, averaging 48.3 versus 44.2. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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