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 31. 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 35.3. The single biggest benchmark swing on the page is HumanEval, 76 to 16.
Ministral 3 3B (Reasoning) 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 128K for Ministral 3 3B (Reasoning).
Pick Qwen2.5-1M if you want the stronger benchmark profile. Ministral 3 3B (Reasoning) only becomes the better choice if you want the stronger reasoning-first profile.
Qwen2.5-1M
64.7
Ministral 3 3B (Reasoning)
34
Qwen2.5-1M
44.8
Ministral 3 3B (Reasoning)
7.2
Qwen2.5-1M
68.4
Ministral 3 3B (Reasoning)
30.4
Qwen2.5-1M
80.9
Ministral 3 3B (Reasoning)
35.3
Qwen2.5-1M
60.4
Ministral 3 3B (Reasoning)
25.2
Qwen2.5-1M
84
Ministral 3 3B (Reasoning)
68
Qwen2.5-1M
80.4
Ministral 3 3B (Reasoning)
59.7
Qwen2.5-1M
83.6
Ministral 3 3B (Reasoning)
40.9
Qwen2.5-1M is ahead overall, 66 to 31. The biggest single separator in this matchup is HumanEval, where the scores are 76 and 16.
Qwen2.5-1M has the edge for knowledge tasks in this comparison, averaging 60.4 versus 25.2. 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 7.2. 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 40.9. 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 35.3. 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. 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 30.4. 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 68. 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 59.7. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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