Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
LFM2.5-350M
~39
0/8 categorieso1-preview
72
Winner · 2/8 categoriesLFM2.5-350M· o1-preview
Pick o1-preview if you want the stronger benchmark profile. LFM2.5-350M only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
o1-preview is clearly ahead on the aggregate, 72 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o1-preview's sharpest advantage is in knowledge, where it averages 72.7 against 23.8. The single biggest benchmark swing on the page is MMLU-Pro, 20.0% to 80%.
o1-preview is the reasoning model in the pair, while LFM2.5-350M 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. o1-preview gives you the larger context window at 200K, 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 | LFM2.5-350M | o1-preview |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 77% |
| BrowseComp | — | 79% |
| OSWorld-Verified | — | 71% |
| Coding | ||
| HumanEval | — | 86% |
| SWE-bench Verified | — | 65% |
| LiveCodeBench | — | 60% |
| SWE-bench Pro | — | 69% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 72% |
| OfficeQA Pro | — | 80% |
| Reasoning | ||
| MuSR | — | 86% |
| BBH | — | 93% |
| LongBench v2 | — | 87% |
| MRCRv2 | — | 83% |
| Knowledgeo1-preview wins | ||
| GPQA | 30.6% | 90% |
| MMLU-Pro | 20.0% | 80% |
| MMLU | — | 92% |
| SuperGPQA | — | 88% |
| HLE | — | 32% |
| FrontierScience | — | 83% |
| SimpleQA | — | 88% |
| Instruction Followingo1-preview wins | ||
| IFEval | 77.0% | 88% |
| Multilingual | ||
| MGSM | — | 90% |
| MMLU-ProX | — | 86% |
| Mathematics | ||
| AIME 2023 | — | 94% |
| AIME 2024 | — | 96% |
| AIME 2025 | — | 95% |
| HMMT Feb 2023 | — | 90% |
| HMMT Feb 2024 | — | 92% |
| HMMT Feb 2025 | — | 91% |
| BRUMO 2025 | — | 93% |
| MATH-500 | — | 94% |
o1-preview is ahead overall, 72 to 39. The biggest single separator in this matchup is MMLU-Pro, where the scores are 20.0% and 80%.
o1-preview has the edge for knowledge tasks in this comparison, averaging 72.7 versus 23.8. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
o1-preview has the edge for instruction following in this comparison, averaging 88 versus 77. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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