Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5 (medium)
76
Winner · 3/8 categoriesGranite-4.0-H-1B
~43
0/8 categoriesGPT-5 (medium)· Granite-4.0-H-1B
Pick GPT-5 (medium) if you want the stronger benchmark profile. Granite-4.0-H-1B only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
GPT-5 (medium) is clearly ahead on the aggregate, 76 to 43. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5 (medium)'s sharpest advantage is in multilingual, where it averages 88.1 against 37.8. The single biggest benchmark swing on the page is GPQA, 89% to 29.9%.
GPT-5 (medium) is the reasoning model in the pair, while Granite-4.0-H-1B 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 | GPT-5 (medium) | Granite-4.0-H-1B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 77% | — |
| BrowseComp | 78% | — |
| OSWorld-Verified | 72% | — |
| Coding | ||
| HumanEval | 83% | 74% |
| SWE-bench Verified | 67% | — |
| LiveCodeBench | 60% | — |
| SWE-bench Pro | 72% | — |
| Multimodal & Grounded | ||
| MMMU-Pro | 89% | — |
| OfficeQA Pro | 87% | — |
| Reasoning | ||
| MuSR | 85% | — |
| BBH | 92% | 60.4% |
| LongBench v2 | 81% | — |
| MRCRv2 | 81% | — |
| KnowledgeGPT-5 (medium) wins | ||
| MMLU | 91% | 59.4% |
| GPQA | 89% | 29.9% |
| SuperGPQA | 87% | — |
| MMLU-Pro | 81% | 34.0% |
| HLE | 27% | — |
| FrontierScience | 82% | — |
| SimpleQA | 87% | — |
| Instruction FollowingGPT-5 (medium) wins | ||
| IFEval | 88% | 77.4% |
| MultilingualGPT-5 (medium) wins | ||
| MGSM | 90% | 37.8% |
| MMLU-ProX | 87% | — |
| Mathematics | ||
| AIME 2023 | 93% | — |
| AIME 2024 | 95% | — |
| AIME 2025 | 94% | — |
| HMMT Feb 2023 | 89% | — |
| HMMT Feb 2024 | 91% | — |
| HMMT Feb 2025 | 90% | — |
| BRUMO 2025 | 92% | — |
| MATH-500 | 92% | — |
GPT-5 (medium) is ahead overall, 76 to 43. The biggest single separator in this matchup is GPQA, where the scores are 89% and 29.9%.
GPT-5 (medium) has the edge for knowledge tasks in this comparison, averaging 71.2 versus 32.6. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GPT-5 (medium) has the edge for instruction following in this comparison, averaging 88 versus 77.4. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-5 (medium) has the edge for multilingual tasks in this comparison, averaging 88.1 versus 37.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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