Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-4.5 is clearly ahead on the aggregate, 36 to 31. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-4.5's sharpest advantage is in multimodal & grounded, where it averages 41 against 30.4. The single biggest benchmark swing on the page is HumanEval, 29 to 16. Ministral 3 3B (Reasoning) does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
Ministral 3 3B (Reasoning) is the reasoning model in the pair, while GLM-4.5 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 GLM-4.5 if you want the stronger benchmark profile. Ministral 3 3B (Reasoning) only becomes the better choice if agentic is the priority or you want the stronger reasoning-first profile.
GLM-4.5
31.3
Ministral 3 3B (Reasoning)
34
GLM-4.5
14.4
Ministral 3 3B (Reasoning)
7.2
GLM-4.5
41
Ministral 3 3B (Reasoning)
30.4
GLM-4.5
43.8
Ministral 3 3B (Reasoning)
35.3
GLM-4.5
32
Ministral 3 3B (Reasoning)
25.2
GLM-4.5
68
Ministral 3 3B (Reasoning)
68
GLM-4.5
58.1
Ministral 3 3B (Reasoning)
59.7
GLM-4.5
45.5
Ministral 3 3B (Reasoning)
40.9
GLM-4.5 is ahead overall, 36 to 31. The biggest single separator in this matchup is HumanEval, where the scores are 29 and 16.
GLM-4.5 has the edge for knowledge tasks in this comparison, averaging 32 versus 25.2. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GLM-4.5 has the edge for coding in this comparison, averaging 14.4 versus 7.2. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
GLM-4.5 has the edge for math in this comparison, averaging 45.5 versus 40.9. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
GLM-4.5 has the edge for reasoning in this comparison, averaging 43.8 versus 35.3. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
Ministral 3 3B (Reasoning) has the edge for agentic tasks in this comparison, averaging 34 versus 31.3. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GLM-4.5 has the edge for multimodal and grounded tasks in this comparison, averaging 41 versus 30.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GLM-4.5 and Ministral 3 3B (Reasoning) are effectively tied for instruction following here, both landing at 68 on average.
Ministral 3 3B (Reasoning) has the edge for multilingual tasks in this comparison, averaging 59.7 versus 58.1. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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