Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek-R1
45
Winner · 2/8 categoriesLFM2.5-350M
~39
0/8 categoriesDeepSeek-R1· LFM2.5-350M
Pick DeepSeek-R1 if you want the stronger benchmark profile. LFM2.5-350M only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
DeepSeek-R1 is clearly ahead on the aggregate, 45 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek-R1's sharpest advantage is in knowledge, where it averages 47 against 23.8. The single biggest benchmark swing on the page is MMLU-Pro, 84% to 20.0%.
DeepSeek-R1 is also the more expensive model on tokens at $0.55 input / $2.19 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for LFM2.5-350M. That is roughly Infinityx on output cost alone. DeepSeek-R1 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. DeepSeek-R1 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 | DeepSeek-R1 | LFM2.5-350M |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 42% | — |
| BrowseComp | 49% | — |
| OSWorld-Verified | 44% | — |
| Coding | ||
| HumanEval | 92% | — |
| SWE-bench Verified | 49.2% | — |
| LiveCodeBench | 19% | — |
| SWE-bench Pro | 25% | — |
| Multimodal & Grounded | ||
| MMMU-Pro | 43% | — |
| OfficeQA Pro | 53% | — |
| Reasoning | ||
| MuSR | 40% | — |
| BBH | 66% | — |
| LongBench v2 | 58% | — |
| MRCRv2 | 57% | — |
| ARC-AGI-2 | 1.3% | — |
| KnowledgeDeepSeek-R1 wins | ||
| MMLU | 90.8% | — |
| GPQA | 71.5% | 30.6% |
| SuperGPQA | 41% | — |
| MMLU-Pro | 84% | 20.0% |
| HLE | 14% | — |
| FrontierScience | 44% | — |
| SimpleQA | 30.1% | — |
| Instruction FollowingDeepSeek-R1 wins | ||
| IFEval | 83.3% | 77.0% |
| Multilingual | ||
| MGSM | 61% | — |
| MMLU-ProX | 60% | — |
| Mathematics | ||
| AIME 2023 | 44% | — |
| AIME 2024 | 79.8% | — |
| AIME 2025 | 45% | — |
| HMMT Feb 2023 | 40% | — |
| HMMT Feb 2024 | 42% | — |
| HMMT Feb 2025 | 41% | — |
| BRUMO 2025 | 43% | — |
| MATH-500 | 97.3% | — |
DeepSeek-R1 is ahead overall, 45 to 39. The biggest single separator in this matchup is MMLU-Pro, where the scores are 84% and 20.0%.
DeepSeek-R1 has the edge for knowledge tasks in this comparison, averaging 47 versus 23.8. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
DeepSeek-R1 has the edge for instruction following in this comparison, averaging 83.3 versus 77. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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