Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Qwen2.5-1M is clearly ahead on the aggregate, 66 to 33. 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 reasoning, where it averages 80.9 against 38.4. The single biggest benchmark swing on the page is HumanEval, 76 to 17.
LFM2.5-1.2B-Thinking 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 32K for LFM2.5-1.2B-Thinking.
Pick Qwen2.5-1M if you want the stronger benchmark profile. LFM2.5-1.2B-Thinking only becomes the better choice if you want the stronger reasoning-first profile.
Qwen2.5-1M
64.7
LFM2.5-1.2B-Thinking
34.1
Qwen2.5-1M
44.8
LFM2.5-1.2B-Thinking
8.2
Qwen2.5-1M
68.4
LFM2.5-1.2B-Thinking
32.4
Qwen2.5-1M
80.9
LFM2.5-1.2B-Thinking
38.4
Qwen2.5-1M
60.4
LFM2.5-1.2B-Thinking
27
Qwen2.5-1M
84
LFM2.5-1.2B-Thinking
72
Qwen2.5-1M
80.4
LFM2.5-1.2B-Thinking
60.7
Qwen2.5-1M
83.6
LFM2.5-1.2B-Thinking
42.3
Qwen2.5-1M is ahead overall, 66 to 33. The biggest single separator in this matchup is HumanEval, where the scores are 76 and 17.
Qwen2.5-1M has the edge for knowledge tasks in this comparison, averaging 60.4 versus 27. 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 44.8 versus 8.2. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
Qwen2.5-1M has the edge for math in this comparison, averaging 83.6 versus 42.3. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
Qwen2.5-1M has the edge for reasoning in this comparison, averaging 80.9 versus 38.4. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
Qwen2.5-1M has the edge for agentic tasks in this comparison, averaging 64.7 versus 34.1. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
Qwen2.5-1M has the edge for multimodal and grounded tasks in this comparison, averaging 68.4 versus 32.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Qwen2.5-1M has the edge for instruction following in this comparison, averaging 84 versus 72. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Qwen2.5-1M has the edge for multilingual tasks in this comparison, averaging 80.4 versus 60.7. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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