Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek LLM 2.0 is clearly ahead on the aggregate, 62 to 32. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek LLM 2.0's sharpest advantage is in mathematics, where it averages 80.8 against 43.3. The single biggest benchmark swing on the page is MMLU, 79 to 28.
Pick DeepSeek LLM 2.0 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.
DeepSeek LLM 2.0
57.9
Ministral 3 8B
28.9
DeepSeek LLM 2.0
42.9
Ministral 3 8B
14.2
DeepSeek LLM 2.0
64.5
Ministral 3 8B
32.4
DeepSeek LLM 2.0
73.6
Ministral 3 8B
36.1
DeepSeek LLM 2.0
57.5
Ministral 3 8B
28
DeepSeek LLM 2.0
85
Ministral 3 8B
69
DeepSeek LLM 2.0
78.8
Ministral 3 8B
61.7
DeepSeek LLM 2.0
80.8
Ministral 3 8B
43.3
DeepSeek LLM 2.0 is ahead overall, 62 to 32. The biggest single separator in this matchup is MMLU, where the scores are 79 and 28.
DeepSeek LLM 2.0 has the edge for knowledge tasks in this comparison, averaging 57.5 versus 28. Inside this category, MMLU is the benchmark that creates the most daylight between them.
DeepSeek LLM 2.0 has the edge for coding in this comparison, averaging 42.9 versus 14.2. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
DeepSeek LLM 2.0 has the edge for math in this comparison, averaging 80.8 versus 43.3. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
DeepSeek LLM 2.0 has the edge for reasoning in this comparison, averaging 73.6 versus 36.1. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
DeepSeek LLM 2.0 has the edge for agentic tasks in this comparison, averaging 57.9 versus 28.9. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
DeepSeek LLM 2.0 has the edge for multimodal and grounded tasks in this comparison, averaging 64.5 versus 32.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
DeepSeek LLM 2.0 has the edge for instruction following in this comparison, averaging 85 versus 69. Inside this category, IFEval is the benchmark that creates the most daylight between them.
DeepSeek LLM 2.0 has the edge for multilingual tasks in this comparison, averaging 78.8 versus 61.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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