Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
o1-pro is clearly ahead on the aggregate, 45 to 38. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o1-pro's sharpest advantage is in mathematics, where it averages 86 against 50.4. The single biggest benchmark swing on the page is AIME 2024, 86 to 48. LFM2-24B-A2B does hit back in multilingual, so the answer changes if that is the part of the workload you care about most.
o1-pro is also the more expensive model on tokens at $150.00 input / $600.00 output per 1M tokens, versus $0.03 input / $0.12 output per 1M tokens for LFM2-24B-A2B. That is roughly 5000.0x on output cost alone. o1-pro is the reasoning model in the pair, while LFM2-24B-A2B 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-pro gives you the larger context window at 200K, compared with 32K for LFM2-24B-A2B.
Pick o1-pro if you want the stronger benchmark profile. LFM2-24B-A2B only becomes the better choice if multilingual is the priority or you want the cheaper token bill.
o1-pro
39.7
LFM2-24B-A2B
33.4
o1-pro
23
LFM2-24B-A2B
18
o1-pro
48.5
LFM2-24B-A2B
41.7
o1-pro
56.2
LFM2-24B-A2B
46.6
o1-pro
69.9
LFM2-24B-A2B
35.6
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
o1-pro
52
LFM2-24B-A2B
61.4
o1-pro
86
LFM2-24B-A2B
50.4
o1-pro is ahead overall, 45 to 38. The biggest single separator in this matchup is AIME 2024, where the scores are 86 and 48.
o1-pro has the edge for knowledge tasks in this comparison, averaging 69.9 versus 35.6. Inside this category, GPQA is the benchmark that creates the most daylight between them.
o1-pro has the edge for coding in this comparison, averaging 23 versus 18. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
o1-pro has the edge for math in this comparison, averaging 86 versus 50.4. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
o1-pro has the edge for reasoning in this comparison, averaging 56.2 versus 46.6. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
o1-pro has the edge for agentic tasks in this comparison, averaging 39.7 versus 33.4. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
o1-pro has the edge for multimodal and grounded tasks in this comparison, averaging 48.5 versus 41.7. Inside this category, MMMU-Pro 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 52. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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