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 32. 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 36.1. The single biggest benchmark swing on the page is MMLU, 84 to 28.
Qwen2.5-1M gives you the larger context window at 1M, compared with 128K for Ministral 3 8B.
Pick Qwen2.5-1M if you want the stronger benchmark profile. Ministral 3 8B only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Qwen2.5-1M
64.7
Ministral 3 8B
28.9
Qwen2.5-1M
44.8
Ministral 3 8B
14.2
Qwen2.5-1M
68.4
Ministral 3 8B
32.4
Qwen2.5-1M
80.9
Ministral 3 8B
36.1
Qwen2.5-1M
60.4
Ministral 3 8B
28
Qwen2.5-1M
84
Ministral 3 8B
69
Qwen2.5-1M
80.4
Ministral 3 8B
61.7
Qwen2.5-1M
83.6
Ministral 3 8B
43.3
Qwen2.5-1M is ahead overall, 66 to 32. The biggest single separator in this matchup is MMLU, where the scores are 84 and 28.
Qwen2.5-1M has the edge for knowledge tasks in this comparison, averaging 60.4 versus 28. 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 14.2. 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 43.3. 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 36.1. 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 28.9. 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 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 69. 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.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.