Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
o1 is clearly ahead on the aggregate, 68 to 33. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o1's sharpest advantage is in knowledge, where it averages 69.6 against 27. The single biggest benchmark swing on the page is MMLU, 91.8 to 27.
o1 is also the more expensive model on tokens at $15.00 input / $60.00 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 gives you the larger context window at 200K, compared with 32K for LFM2.5-1.2B-Thinking.
Pick o1 if you want the stronger benchmark profile. LFM2.5-1.2B-Thinking only becomes the better choice if you want the cheaper token bill.
o1
65.4
LFM2.5-1.2B-Thinking
34.1
o1
48.4
LFM2.5-1.2B-Thinking
8.2
o1
70.7
LFM2.5-1.2B-Thinking
32.4
o1
78.1
LFM2.5-1.2B-Thinking
38.4
o1
69.6
LFM2.5-1.2B-Thinking
27
o1
92.2
LFM2.5-1.2B-Thinking
72
o1
77
LFM2.5-1.2B-Thinking
60.7
o1
74.3
LFM2.5-1.2B-Thinking
42.3
o1 is ahead overall, 68 to 33. The biggest single separator in this matchup is MMLU, where the scores are 91.8 and 27.
o1 has the edge for knowledge tasks in this comparison, averaging 69.6 versus 27. Inside this category, MMLU is the benchmark that creates the most daylight between them.
o1 has the edge for coding in this comparison, averaging 48.4 versus 8.2. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
o1 has the edge for math in this comparison, averaging 74.3 versus 42.3. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
o1 has the edge for reasoning in this comparison, averaging 78.1 versus 38.4. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
o1 has the edge for agentic tasks in this comparison, averaging 65.4 versus 34.1. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
o1 has the edge for multimodal and grounded tasks in this comparison, averaging 70.7 versus 32.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
o1 has the edge for instruction following in this comparison, averaging 92.2 versus 72. Inside this category, IFEval is the benchmark that creates the most daylight between them.
o1 has the edge for multilingual tasks in this comparison, averaging 77 versus 60.7. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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