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 27. 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 30.1. The single biggest benchmark swing on the page is AIME 2023, 85 to 23.
Qwen2.5-1M gives you the larger context window at 1M, compared with 128K for Ministral 3 3B.
Pick Qwen2.5-1M if you want the stronger benchmark profile. Ministral 3 3B only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Qwen2.5-1M
64.7
Ministral 3 3B
22.9
Qwen2.5-1M
44.8
Ministral 3 3B
6.2
Qwen2.5-1M
68.4
Ministral 3 3B
30.4
Qwen2.5-1M
80.9
Ministral 3 3B
30.1
Qwen2.5-1M
60.4
Ministral 3 3B
24.5
Qwen2.5-1M
84
Ministral 3 3B
67
Qwen2.5-1M
80.4
Ministral 3 3B
59.7
Qwen2.5-1M
83.6
Ministral 3 3B
36
Qwen2.5-1M is ahead overall, 66 to 27. The biggest single separator in this matchup is AIME 2023, where the scores are 85 and 23.
Qwen2.5-1M has the edge for knowledge tasks in this comparison, averaging 60.4 versus 24.5. 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 44.8 versus 6.2. 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 36. 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 30.1. 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 22.9. Inside this category, Terminal-Bench 2.0 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 67. 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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