Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4o
51
Winner · 3/8 categoriesGranite-4.0-350M
~27
0/8 categoriesGPT-4o· Granite-4.0-350M
Pick GPT-4o if you want the stronger benchmark profile. Granite-4.0-350M only becomes the better choice if you want the cheaper token bill.
GPT-4o is clearly ahead on the aggregate, 51 to 27. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-4o's sharpest advantage is in multilingual, where it averages 75.5 against 16.2. The single biggest benchmark swing on the page is MGSM, 82% to 16.2%.
GPT-4o is also the more expensive model on tokens at $2.50 input / $10.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Granite-4.0-350M. That is roughly Infinityx on output cost alone. GPT-4o gives you the larger context window at 128K, compared with 32K for Granite-4.0-350M.
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-4o | Granite-4.0-350M |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 49% | — |
| BrowseComp | 59% | — |
| OSWorld-Verified | 48% | — |
| Coding | ||
| HumanEval | 58% | 38% |
| SWE-bench Verified | 20% | — |
| LiveCodeBench | 38% | — |
| SWE-bench Pro | 29% | — |
| Multimodal & Grounded | ||
| MMMU-Pro | 74% | — |
| OfficeQA Pro | 70% | — |
| Reasoning | ||
| MuSR | 62% | — |
| BBH | 82% | 33.3% |
| LongBench v2 | 62% | — |
| MRCRv2 | 63% | — |
| KnowledgeGPT-4o wins | ||
| MMLU | 66% | 36.2% |
| GPQA | 66% | 26.1% |
| SuperGPQA | 64% | — |
| MMLU-Pro | 64% | 14.4% |
| HLE | 1% | — |
| FrontierScience | 58% | — |
| SimpleQA | 64% | — |
| Instruction FollowingGPT-4o wins | ||
| IFEval | 82% | 61.6% |
| MultilingualGPT-4o wins | ||
| MGSM | 82% | 16.2% |
| MMLU-ProX | 72% | — |
| Mathematics | ||
| AIME 2023 | 66% | — |
| AIME 2024 | 68% | — |
| AIME 2025 | 67% | — |
| HMMT Feb 2023 | 62% | — |
| HMMT Feb 2024 | 64% | — |
| HMMT Feb 2025 | 63% | — |
| BRUMO 2025 | 65% | — |
| MATH-500 | 80% | — |
GPT-4o is ahead overall, 51 to 27. The biggest single separator in this matchup is MGSM, where the scores are 82% and 16.2%.
GPT-4o has the edge for knowledge tasks in this comparison, averaging 48.7 versus 18.5. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
GPT-4o has the edge for instruction following in this comparison, averaging 82 versus 61.6. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-4o has the edge for multilingual tasks in this comparison, averaging 75.5 versus 16.2. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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