Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek-R1 is clearly ahead on the aggregate, 43 to 27. 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 22.9. The single biggest benchmark swing on the page is LongBench v2, 58 to 32.
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 3B. That is roughly Infinityx on output cost alone. DeepSeek-R1 is the reasoning model in the pair, while Ministral 3 3B 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 3B only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
DeepSeek-R1
44.5
Ministral 3 3B
22.9
DeepSeek-R1
21.5
Ministral 3 3B
6.2
DeepSeek-R1
47.5
Ministral 3 3B
30.4
DeepSeek-R1
50.9
Ministral 3 3B
30.1
DeepSeek-R1
37.9
Ministral 3 3B
24.5
DeepSeek-R1
69
Ministral 3 3B
67
DeepSeek-R1
60.4
Ministral 3 3B
59.7
DeepSeek-R1
52.5
Ministral 3 3B
36
DeepSeek-R1 is ahead overall, 43 to 27. The biggest single separator in this matchup is LongBench v2, where the scores are 58 and 32.
DeepSeek-R1 has the edge for knowledge tasks in this comparison, averaging 37.9 versus 24.5. 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 6.2. 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 36. 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 30.1. 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 22.9. 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 30.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
DeepSeek-R1 has the edge for instruction following in this comparison, averaging 69 versus 67. Inside this category, IFEval is the benchmark that creates the most daylight between them.
DeepSeek-R1 has the edge for multilingual tasks in this comparison, averaging 60.4 versus 59.7. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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