Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Claude Haiku 4.5
63
Winner · 4/8 categoriesGemma 4 E2B
~39
0/8 categoriesClaude Haiku 4.5· Gemma 4 E2B
Pick Claude Haiku 4.5 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.
Claude Haiku 4.5 is clearly ahead on the aggregate, 63 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Claude Haiku 4.5's sharpest advantage is in reasoning, where it averages 68.9 against 19.1. The single biggest benchmark swing on the page is BBH, 81% to 21.9%.
Claude Haiku 4.5 is also the more expensive model on tokens at $0.80 input / $4.00 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 Claude Haiku 4.5 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. Claude Haiku 4.5 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 | Claude Haiku 4.5 | Gemma 4 E2B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 41% | — |
| BrowseComp | 62% | — |
| OSWorld-Verified | 57% | — |
| CodingClaude Haiku 4.5 wins | ||
| HumanEval | 60% | — |
| SWE-bench Verified | 73.3% | — |
| LiveCodeBench | 36% | 44% |
| SWE-bench Pro | 46% | — |
| FLTEval | 23% | — |
| Multimodal & GroundedClaude Haiku 4.5 wins | ||
| MMMU-Pro | 82% | 44.2% |
| OfficeQA Pro | 74% | — |
| ReasoningClaude Haiku 4.5 wins | ||
| MuSR | 63% | — |
| BBH | 81% | 21.9% |
| LongBench v2 | 72% | — |
| MRCRv2 | 70% | 19.1% |
| KnowledgeClaude Haiku 4.5 wins | ||
| MMLU | 68% | — |
| GPQA | 67% | 43.4% |
| SuperGPQA | 65% | — |
| MMLU-Pro | 73% | 60% |
| HLE | 11% | — |
| FrontierScience | 64% | — |
| SimpleQA | 65% | — |
| Instruction Following | ||
| IFEval | 86% | — |
| Multilingual | ||
| MGSM | 82% | — |
| MMLU-ProX | 79% | — |
| Mathematics | ||
| AIME 2023 | 68% | — |
| AIME 2024 | 70% | — |
| AIME 2025 | 69% | — |
| HMMT Feb 2023 | 64% | — |
| HMMT Feb 2024 | 66% | — |
| HMMT Feb 2025 | 65% | — |
| BRUMO 2025 | 67% | — |
| MATH-500 | 81% | — |
Claude Haiku 4.5 is ahead overall, 63 to 39. The biggest single separator in this matchup is BBH, where the scores are 81% and 21.9%.
Claude Haiku 4.5 has the edge for knowledge tasks in this comparison, averaging 54.4 versus 54.1. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Claude Haiku 4.5 has the edge for coding in this comparison, averaging 48.5 versus 44. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
Claude Haiku 4.5 has the edge for reasoning in this comparison, averaging 68.9 versus 19.1. Inside this category, BBH is the benchmark that creates the most daylight between them.
Claude Haiku 4.5 has the edge for multimodal and grounded tasks in this comparison, averaging 78.4 versus 44.2. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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