Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
LFM2-24B-A2B has the cleaner overall profile here, landing at 38 versus 35. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
LFM2-24B-A2B's sharpest advantage is in mathematics, where it averages 50.4 against 43.1. The single biggest benchmark swing on the page is HumanEval, 42 to 23. GPT-OSS 20B does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
GPT-OSS 20B gives you the larger context window at 128K, compared with 32K for LFM2-24B-A2B.
Pick LFM2-24B-A2B if you want the stronger benchmark profile. GPT-OSS 20B only becomes the better choice if agentic is the priority or you need the larger 128K context window.
LFM2-24B-A2B
33.4
GPT-OSS 20B
35.4
LFM2-24B-A2B
18
GPT-OSS 20B
14.5
LFM2-24B-A2B
41.7
GPT-OSS 20B
36
LFM2-24B-A2B
46.6
GPT-OSS 20B
40.4
LFM2-24B-A2B
35.6
GPT-OSS 20B
29
LFM2-24B-A2B
68
GPT-OSS 20B
67
LFM2-24B-A2B
61.4
GPT-OSS 20B
59.7
LFM2-24B-A2B
50.4
GPT-OSS 20B
43.1
LFM2-24B-A2B is ahead overall, 38 to 35. The biggest single separator in this matchup is HumanEval, where the scores are 42 and 23.
LFM2-24B-A2B has the edge for knowledge tasks in this comparison, averaging 35.6 versus 29. Inside this category, MMLU is the benchmark that creates the most daylight between them.
LFM2-24B-A2B has the edge for coding in this comparison, averaging 18 versus 14.5. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
LFM2-24B-A2B has the edge for math in this comparison, averaging 50.4 versus 43.1. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
LFM2-24B-A2B has the edge for reasoning in this comparison, averaging 46.6 versus 40.4. Inside this category, SimpleQA 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 33.4. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
LFM2-24B-A2B has the edge for multimodal and grounded tasks in this comparison, averaging 41.7 versus 36. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
LFM2-24B-A2B has the edge for instruction following in this comparison, averaging 68 versus 67. Inside this category, IFEval is the benchmark that creates the most daylight between them.
LFM2-24B-A2B has the edge for multilingual tasks in this comparison, averaging 61.4 versus 59.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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