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 36. 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 42.1. The single biggest benchmark swing on the page is MMLU, 84 to 30.
Ministral 3 8B (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 8B (Reasoning).
Pick Qwen2.5-1M if you want the stronger benchmark profile. Ministral 3 8B (Reasoning) only becomes the better choice if you want the stronger reasoning-first profile.
Qwen2.5-1M
64.7
Ministral 3 8B (Reasoning)
38.5
Qwen2.5-1M
44.8
Ministral 3 8B (Reasoning)
15.2
Qwen2.5-1M
68.4
Ministral 3 8B (Reasoning)
33.4
Qwen2.5-1M
80.9
Ministral 3 8B (Reasoning)
42.1
Qwen2.5-1M
60.4
Ministral 3 8B (Reasoning)
30
Qwen2.5-1M
84
Ministral 3 8B (Reasoning)
70
Qwen2.5-1M
80.4
Ministral 3 8B (Reasoning)
61.7
Qwen2.5-1M
83.6
Ministral 3 8B (Reasoning)
47.8
Qwen2.5-1M is ahead overall, 66 to 36. The biggest single separator in this matchup is MMLU, where the scores are 84 and 30.
Qwen2.5-1M has the edge for knowledge tasks in this comparison, averaging 60.4 versus 30. 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 15.2. 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 47.8. 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 42.1. 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 38.5. 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 33.4. 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 70. 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 61.7. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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