Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Llama 4 Behemoth is clearly ahead on the aggregate, 40 to 31. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Llama 4 Behemoth's sharpest advantage is in multimodal & grounded, where it averages 55.1 against 30.4. The single biggest benchmark swing on the page is MMMU-Pro, 60 to 25.
Ministral 3 3B (Reasoning) is the reasoning model in the pair, while Llama 4 Behemoth 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. Ministral 3 3B (Reasoning) gives you the larger context window at 128K, compared with 32K for Llama 4 Behemoth.
Pick Llama 4 Behemoth if you want the stronger benchmark profile. Ministral 3 3B (Reasoning) only becomes the better choice if you need the larger 128K context window or you want the stronger reasoning-first profile.
Llama 4 Behemoth
34.6
Ministral 3 3B (Reasoning)
34
Llama 4 Behemoth
14.1
Ministral 3 3B (Reasoning)
7.2
Llama 4 Behemoth
55.1
Ministral 3 3B (Reasoning)
30.4
Llama 4 Behemoth
47.1
Ministral 3 3B (Reasoning)
35.3
Llama 4 Behemoth
36.7
Ministral 3 3B (Reasoning)
25.2
Llama 4 Behemoth
68
Ministral 3 3B (Reasoning)
68
Llama 4 Behemoth
62.8
Ministral 3 3B (Reasoning)
59.7
Llama 4 Behemoth
52.9
Ministral 3 3B (Reasoning)
40.9
Llama 4 Behemoth is ahead overall, 40 to 31. The biggest single separator in this matchup is MMMU-Pro, where the scores are 60 and 25.
Llama 4 Behemoth has the edge for knowledge tasks in this comparison, averaging 36.7 versus 25.2. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Llama 4 Behemoth has the edge for coding in this comparison, averaging 14.1 versus 7.2. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
Llama 4 Behemoth has the edge for math in this comparison, averaging 52.9 versus 40.9. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
Llama 4 Behemoth has the edge for reasoning in this comparison, averaging 47.1 versus 35.3. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
Llama 4 Behemoth has the edge for agentic tasks in this comparison, averaging 34.6 versus 34. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
Llama 4 Behemoth has the edge for multimodal and grounded tasks in this comparison, averaging 55.1 versus 30.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Llama 4 Behemoth and Ministral 3 3B (Reasoning) are effectively tied for instruction following here, both landing at 68 on average.
Llama 4 Behemoth has the edge for multilingual tasks in this comparison, averaging 62.8 versus 59.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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