Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Claude 3 Haiku
42
2/8 categoriesGranite-4.0-H-1B
~43
Winner · 1/8 categoriesClaude 3 Haiku· Granite-4.0-H-1B
Pick Granite-4.0-H-1B if you want the stronger benchmark profile. Claude 3 Haiku only becomes the better choice if multilingual is the priority or you need the larger 200K context window.
Granite-4.0-H-1B finishes one point ahead overall, 43 to 42. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
Granite-4.0-H-1B's sharpest advantage is in instruction following, where it averages 77.4 against 76. The single biggest benchmark swing on the page is MGSM, 73% to 37.8%. Claude 3 Haiku does hit back in multilingual, so the answer changes if that is the part of the workload you care about most.
Claude 3 Haiku gives you the larger context window at 200K, compared with 128K for Granite-4.0-H-1B.
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 3 Haiku | Granite-4.0-H-1B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 40% | — |
| BrowseComp | 53% | — |
| OSWorld-Verified | 42% | — |
| Coding | ||
| HumanEval | 73% | 74% |
| SWE-bench Verified | 17% | — |
| LiveCodeBench | 20% | — |
| SWE-bench Pro | 19% | — |
| Multimodal & Grounded | ||
| MMMU-Pro | 70% | — |
| OfficeQA Pro | 67% | — |
| Reasoning | ||
| MuSR | 52% | — |
| BBH | 74% | 60.4% |
| LongBench v2 | 63% | — |
| MRCRv2 | 63% | — |
| KnowledgeClaude 3 Haiku wins | ||
| MMLU | 75.2% | 59.4% |
| GPQA | 56% | 29.9% |
| SuperGPQA | 54% | — |
| MMLU-Pro | 63% | 34.0% |
| HLE | 2% | — |
| FrontierScience | 50% | — |
| SimpleQA | 54% | — |
| Instruction FollowingGranite-4.0-H-1B wins | ||
| IFEval | 76% | 77.4% |
| MultilingualClaude 3 Haiku wins | ||
| MGSM | 73% | 37.8% |
| MMLU-ProX | 70% | — |
| Mathematics | ||
| AIME 2023 | 56% | — |
| AIME 2024 | 58% | — |
| AIME 2025 | 57% | — |
| HMMT Feb 2023 | 52% | — |
| HMMT Feb 2024 | 54% | — |
| HMMT Feb 2025 | 53% | — |
| BRUMO 2025 | 55% | — |
| MATH-500 | 71% | — |
Granite-4.0-H-1B is ahead overall, 43 to 42. The biggest single separator in this matchup is MGSM, where the scores are 73% and 37.8%.
Claude 3 Haiku has the edge for knowledge tasks in this comparison, averaging 43.5 versus 32.6. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Granite-4.0-H-1B has the edge for instruction following in this comparison, averaging 77.4 versus 76. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Claude 3 Haiku has the edge for multilingual tasks in this comparison, averaging 71.1 versus 37.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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