Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
o1-pro is clearly ahead on the aggregate, 45 to 33. 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 42.3. The single biggest benchmark swing on the page is AIME 2024, 86 to 30. LFM2.5-1.2B-Thinking 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 $0.00 input / $0.00 output per 1M tokens for LFM2.5-1.2B-Thinking. That is roughly Infinityx on output cost alone. o1-pro gives you the larger context window at 200K, compared with 32K for LFM2.5-1.2B-Thinking.
Pick o1-pro if you want the stronger benchmark profile. LFM2.5-1.2B-Thinking only becomes the better choice if multilingual is the priority or you want the cheaper token bill.
o1-pro
39.7
LFM2.5-1.2B-Thinking
34.1
o1-pro
23
LFM2.5-1.2B-Thinking
8.2
o1-pro
48.5
LFM2.5-1.2B-Thinking
32.4
o1-pro
56.2
LFM2.5-1.2B-Thinking
38.4
o1-pro
69.9
LFM2.5-1.2B-Thinking
27
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
o1-pro
52
LFM2.5-1.2B-Thinking
60.7
o1-pro
86
LFM2.5-1.2B-Thinking
42.3
o1-pro is ahead overall, 45 to 33. The biggest single separator in this matchup is AIME 2024, where the scores are 86 and 30.
o1-pro has the edge for knowledge tasks in this comparison, averaging 69.9 versus 27. 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 8.2. 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 42.3. 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 38.4. 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 34.1. 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 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 multilingual tasks in this comparison, averaging 60.7 versus 52. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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