Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-OSS 120B
51
Winner · 2/8 categoriesLFM2.5-350M
~39
0/8 categoriesGPT-OSS 120B· LFM2.5-350M
Pick GPT-OSS 120B if you want the stronger benchmark profile. LFM2.5-350M only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
GPT-OSS 120B is clearly ahead on the aggregate, 51 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-OSS 120B's sharpest advantage is in knowledge, where it averages 51.6 against 23.8. The single biggest benchmark swing on the page is MMLU-Pro, 90% to 20.0%.
GPT-OSS 120B gives you the larger context window at 128K, compared with 32K for LFM2.5-350M.
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-OSS 120B | LFM2.5-350M |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 43% | — |
| BrowseComp | 50% | — |
| OSWorld-Verified | 43% | — |
| Coding | ||
| HumanEval | 43% | — |
| SWE-bench Verified | 29% | — |
| LiveCodeBench | 25% | — |
| SWE-bench Pro | 31% | — |
| SWE-Rebench | 33.3% | — |
| React Native Evals | 66.4% | — |
| Multimodal & Grounded | ||
| MMMU-Pro | 42% | — |
| OfficeQA Pro | 57% | — |
| Reasoning | ||
| MuSR | 47% | — |
| BBH | 73% | — |
| LongBench v2 | 58% | — |
| MRCRv2 | 59% | — |
| KnowledgeGPT-OSS 120B wins | ||
| MMLU | 90% | — |
| GPQA | 80.9% | 30.6% |
| SuperGPQA | 48% | — |
| MMLU-Pro | 90% | 20.0% |
| HLE | 5% | — |
| FrontierScience | 49% | — |
| SimpleQA | 49% | — |
| Instruction FollowingGPT-OSS 120B wins | ||
| IFEval | 79% | 77.0% |
| Multilingual | ||
| MGSM | 72% | — |
| MMLU-ProX | 70% | — |
| Mathematics | ||
| AIME 2023 | 51% | — |
| AIME 2024 | 53% | — |
| AIME 2025 | 52% | — |
| HMMT Feb 2023 | 47% | — |
| HMMT Feb 2024 | 49% | — |
| HMMT Feb 2025 | 48% | — |
| BRUMO 2025 | 50% | — |
| MATH-500 | 71% | — |
GPT-OSS 120B is ahead overall, 51 to 39. The biggest single separator in this matchup is MMLU-Pro, where the scores are 90% and 20.0%.
GPT-OSS 120B has the edge for knowledge tasks in this comparison, averaging 51.6 versus 23.8. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
GPT-OSS 120B has the edge for instruction following in this comparison, averaging 79 versus 77. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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