Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 E2B
~39
2/8 categoriesMistral Large 3
58
Winner · 2/8 categoriesGemma 4 E2B· Mistral Large 3
Pick Mistral Large 3 if you want the stronger benchmark profile. Gemma 4 E2B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Mistral Large 3 is clearly ahead on the aggregate, 58 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Mistral Large 3's sharpest advantage is in reasoning, where it averages 68.1 against 19.1. The single biggest benchmark swing on the page is BBH, 21.9% to 81%. Gemma 4 E2B does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
Mistral Large 3 is also the more expensive model on tokens at $2.00 input / $6.00 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 Mistral Large 3 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.
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 Large 3 |
|---|---|---|
| Agentic | ||
| OSWorld-Verified | — | 49% |
| CodingGemma 4 E2B wins | ||
| LiveCodeBench | 44% | 39% |
| HumanEval | — | 92.3% |
| SWE-bench Verified | — | 45% |
| SWE-bench Pro | — | 42% |
| Multimodal & GroundedMistral Large 3 wins | ||
| MMMU-Pro | 44.2% | 75% |
| OfficeQA Pro | — | 76% |
| ReasoningMistral Large 3 wins | ||
| BBH | 21.9% | 81% |
| MRCRv2 | 19.1% | 67% |
| MuSR | — | 71% |
| LongBench v2 | — | 67% |
| KnowledgeGemma 4 E2B wins | ||
| GPQA | 43.4% | 43.9% |
| MMLU-Pro | 60% | 73.1% |
| SuperGPQA | — | 73% |
| HLE | — | 12% |
| FrontierScience | — | 67% |
| SimpleQA | — | 24% |
| Instruction Following | ||
| IFEval | — | 89.4% |
| Multilingual | ||
| MGSM | — | 82% |
| MMLU-ProX | — | 77% |
| Mathematics | ||
| AIME 2023 | — | 76% |
| AIME 2024 | — | 78% |
| AIME 2025 | — | 77% |
| HMMT Feb 2023 | — | 72% |
| HMMT Feb 2024 | — | 74% |
| HMMT Feb 2025 | — | 73% |
| BRUMO 2025 | — | 75% |
| MATH-500 | — | 93.6% |
Mistral Large 3 is ahead overall, 58 to 39. The biggest single separator in this matchup is BBH, where the scores are 21.9% and 81%.
Gemma 4 E2B has the edge for knowledge tasks in this comparison, averaging 54.1 versus 48.1. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Gemma 4 E2B has the edge for coding in this comparison, averaging 44 versus 41.5. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
Mistral Large 3 has the edge for reasoning in this comparison, averaging 68.1 versus 19.1. Inside this category, BBH is the benchmark that creates the most daylight between them.
Mistral Large 3 has the edge for multimodal and grounded tasks in this comparison, averaging 75.5 versus 44.2. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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