Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-OSS 20B has the cleaner overall profile here, landing at 35 versus 33. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
GPT-OSS 20B's sharpest advantage is in coding, where it averages 14.5 against 8.2. The single biggest benchmark swing on the page is SWE-bench Pro, 18 to 7. LFM2.5-1.2B-Thinking does hit back in instruction following, so the answer changes if that is the part of the workload you care about most.
LFM2.5-1.2B-Thinking is the reasoning model in the pair, while GPT-OSS 20B 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. GPT-OSS 20B gives you the larger context window at 128K, compared with 32K for LFM2.5-1.2B-Thinking.
Pick GPT-OSS 20B if you want the stronger benchmark profile. LFM2.5-1.2B-Thinking only becomes the better choice if instruction following is the priority or you want the stronger reasoning-first profile.
GPT-OSS 20B
35.4
LFM2.5-1.2B-Thinking
34.1
GPT-OSS 20B
14.5
LFM2.5-1.2B-Thinking
8.2
GPT-OSS 20B
36
LFM2.5-1.2B-Thinking
32.4
GPT-OSS 20B
40.4
LFM2.5-1.2B-Thinking
38.4
GPT-OSS 20B
29
LFM2.5-1.2B-Thinking
27
GPT-OSS 20B
67
LFM2.5-1.2B-Thinking
72
GPT-OSS 20B
59.7
LFM2.5-1.2B-Thinking
60.7
GPT-OSS 20B
43.1
LFM2.5-1.2B-Thinking
42.3
GPT-OSS 20B is ahead overall, 35 to 33. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 18 and 7.
GPT-OSS 20B has the edge for knowledge tasks in this comparison, averaging 29 versus 27. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-OSS 20B has the edge for coding in this comparison, averaging 14.5 versus 8.2. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
GPT-OSS 20B has the edge for math in this comparison, averaging 43.1 versus 42.3. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
GPT-OSS 20B has the edge for reasoning in this comparison, averaging 40.4 versus 38.4. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
GPT-OSS 20B has the edge for agentic tasks in this comparison, averaging 35.4 versus 34.1. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
GPT-OSS 20B has the edge for multimodal and grounded tasks in this comparison, averaging 36 versus 32.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
LFM2.5-1.2B-Thinking has the edge for instruction following in this comparison, averaging 72 versus 67. Inside this category, IFEval is the benchmark that creates the most daylight between them.
LFM2.5-1.2B-Thinking has the edge for multilingual tasks in this comparison, averaging 60.7 versus 59.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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