Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek-R1 is clearly ahead on the aggregate, 43 to 32. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek-R1's sharpest advantage is in agentic, where it averages 44.5 against 28.9. The single biggest benchmark swing on the page is LongBench v2, 58 to 38. Ministral 3 8B does hit back in multilingual, so the answer changes if that is the part of the workload you care about most.
DeepSeek-R1 is also the more expensive model on tokens at $0.55 input / $2.19 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Ministral 3 8B. That is roughly Infinityx on output cost alone. DeepSeek-R1 is the reasoning model in the pair, while Ministral 3 8B 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.
Pick DeepSeek-R1 if you want the stronger benchmark profile. Ministral 3 8B only becomes the better choice if multilingual is the priority or you want the cheaper token bill.
DeepSeek-R1
44.5
Ministral 3 8B
28.9
DeepSeek-R1
21.5
Ministral 3 8B
14.2
DeepSeek-R1
47.5
Ministral 3 8B
32.4
DeepSeek-R1
50.9
Ministral 3 8B
36.1
DeepSeek-R1
37.9
Ministral 3 8B
28
DeepSeek-R1
69
Ministral 3 8B
69
DeepSeek-R1
60.4
Ministral 3 8B
61.7
DeepSeek-R1
52.5
Ministral 3 8B
43.3
DeepSeek-R1 is ahead overall, 43 to 32. The biggest single separator in this matchup is LongBench v2, where the scores are 58 and 38.
DeepSeek-R1 has the edge for knowledge tasks in this comparison, averaging 37.9 versus 28. Inside this category, MMLU is the benchmark that creates the most daylight between them.
DeepSeek-R1 has the edge for coding in this comparison, averaging 21.5 versus 14.2. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
DeepSeek-R1 has the edge for math in this comparison, averaging 52.5 versus 43.3. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
DeepSeek-R1 has the edge for reasoning in this comparison, averaging 50.9 versus 36.1. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
DeepSeek-R1 has the edge for agentic tasks in this comparison, averaging 44.5 versus 28.9. Inside this category, OSWorld-Verified is the benchmark that creates the most daylight between them.
DeepSeek-R1 has the edge for multimodal and grounded tasks in this comparison, averaging 47.5 versus 32.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
DeepSeek-R1 and Ministral 3 8B are effectively tied for instruction following here, both landing at 69 on average.
Ministral 3 8B has the edge for multilingual tasks in this comparison, averaging 61.7 versus 60.4. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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