Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
Qwen2.5-1M is clearly ahead on the aggregate, 73 to 51. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen2.5-1M's sharpest advantage is in coding, where it averages 54.3 against 41. The single biggest benchmark swing on the page is AIME 2024, 87 to 74.3. o1 does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
o1 is the reasoning model in the pair, while Qwen2.5-1M 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. Qwen2.5-1M gives you the larger context window at 1M, compared with 200K for o1.
Pick Qwen2.5-1M if you want the stronger benchmark profile. o1 only becomes the better choice if knowledge is the priority or you want the stronger reasoning-first profile.
Qwen2.5-1M
68.5
o1
83.8
Qwen2.5-1M
54.3
o1
41
Qwen2.5-1M
83.9
o1
74.3
Qwen2.5-1M
84
o1
92.2
Qwen2.5-1M is ahead overall, 73 to 51. The biggest single separator in this matchup is AIME 2024, where the scores are 87 and 74.3.
o1 has the edge for knowledge tasks in this comparison, averaging 83.8 versus 68.5. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Qwen2.5-1M has the edge for coding in this comparison, averaging 54.3 versus 41. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Qwen2.5-1M has the edge for math in this comparison, averaging 83.9 versus 74.3. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
o1 has the edge for instruction following in this comparison, averaging 92.2 versus 84. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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