Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 E4B
~47
Winner · 3/8 categoriesLlama 3 70B
44
1/8 categoriesGemma 4 E4B· Llama 3 70B
Pick Gemma 4 E4B if you want the stronger benchmark profile. Llama 3 70B only becomes the better choice if reasoning is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Gemma 4 E4B has the cleaner overall profile here, landing at 47 versus 44. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Gemma 4 E4B's sharpest advantage is in coding, where it averages 52 against 14.8. The single biggest benchmark swing on the page is BBH, 33.1% to 74%. Llama 3 70B does hit back in reasoning, so the answer changes if that is the part of the workload you care about most.
Gemma 4 E4B is the reasoning model in the pair, while Llama 3 70B 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 E4B | Llama 3 70B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 37% |
| BrowseComp | — | 48% |
| OSWorld-Verified | — | 41% |
| CodingGemma 4 E4B wins | ||
| LiveCodeBench | 52% | 19% |
| HumanEval | — | 50% |
| SWE-bench Verified | — | 9% |
| SWE-bench Pro | — | 14% |
| Multimodal & GroundedGemma 4 E4B wins | ||
| MMMU-Pro | 52.6% | 50% |
| OfficeQA Pro | — | 55% |
| ReasoningLlama 3 70B wins | ||
| BBH | 33.1% | 74% |
| MRCRv2 | 25.4% | 61% |
| MuSR | — | 54% |
| LongBench v2 | — | 61% |
| KnowledgeGemma 4 E4B wins | ||
| GPQA | 58.6% | 58% |
| MMLU-Pro | 69.4% | 55% |
| MMLU | — | 58% |
| SuperGPQA | — | 56% |
| HLE | — | 2% |
| FrontierScience | — | 54% |
| SimpleQA | — | 56% |
| Instruction Following | ||
| IFEval | — | 77% |
| Multilingual | ||
| MGSM | — | 72% |
| MMLU-ProX | — | 65% |
| Mathematics | ||
| AIME 2023 | — | 58% |
| AIME 2024 | — | 60% |
| AIME 2025 | — | 59% |
| HMMT Feb 2023 | — | 54% |
| HMMT Feb 2024 | — | 56% |
| HMMT Feb 2025 | — | 55% |
| BRUMO 2025 | — | 57% |
| MATH-500 | — | 71% |
Gemma 4 E4B is ahead overall, 47 to 44. The biggest single separator in this matchup is BBH, where the scores are 33.1% and 74%.
Gemma 4 E4B has the edge for knowledge tasks in this comparison, averaging 65.6 versus 43.2. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Gemma 4 E4B has the edge for coding in this comparison, averaging 52 versus 14.8. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
Llama 3 70B has the edge for reasoning in this comparison, averaging 59.1 versus 25.4. Inside this category, BBH is the benchmark that creates the most daylight between them.
Gemma 4 E4B has the edge for multimodal and grounded tasks in this comparison, averaging 52.6 versus 52.3. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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