Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-5 (Reasoning) is clearly ahead on the aggregate, 78 to 27. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5 (Reasoning)'s sharpest advantage is in reasoning, where it averages 88.9 against 30.1. The single biggest benchmark swing on the page is AIME 2023, 98 to 23.
GLM-5 (Reasoning) 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. GLM-5 (Reasoning) gives you the larger context window at 200K, compared with 128K for Ministral 3 3B.
Pick GLM-5 (Reasoning) if you want the stronger benchmark profile. Ministral 3 3B only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
GLM-5 (Reasoning)
78.3
Ministral 3 3B
22.9
GLM-5 (Reasoning)
62.5
Ministral 3 3B
6.2
GLM-5 (Reasoning)
78.5
Ministral 3 3B
30.4
GLM-5 (Reasoning)
88.9
Ministral 3 3B
30.1
GLM-5 (Reasoning)
72
Ministral 3 3B
24.5
GLM-5 (Reasoning)
92
Ministral 3 3B
67
GLM-5 (Reasoning)
86.4
Ministral 3 3B
59.7
GLM-5 (Reasoning)
94.4
Ministral 3 3B
36
GLM-5 (Reasoning) is ahead overall, 78 to 27. The biggest single separator in this matchup is AIME 2023, where the scores are 98 and 23.
GLM-5 (Reasoning) has the edge for knowledge tasks in this comparison, averaging 72 versus 24.5. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GLM-5 (Reasoning) has the edge for coding in this comparison, averaging 62.5 versus 6.2. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
GLM-5 (Reasoning) has the edge for math in this comparison, averaging 94.4 versus 36. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
GLM-5 (Reasoning) has the edge for reasoning in this comparison, averaging 88.9 versus 30.1. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
GLM-5 (Reasoning) has the edge for agentic tasks in this comparison, averaging 78.3 versus 22.9. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GLM-5 (Reasoning) has the edge for multimodal and grounded tasks in this comparison, averaging 78.5 versus 30.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GLM-5 (Reasoning) has the edge for instruction following in this comparison, averaging 92 versus 67. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GLM-5 (Reasoning) has the edge for multilingual tasks in this comparison, averaging 86.4 versus 59.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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