Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-5 is clearly ahead on the aggregate, 65 to 36. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5's sharpest advantage is in reasoning, where it averages 79.4 against 42.1. The single biggest benchmark swing on the page is MMLU, 88 to 30.
Ministral 3 8B (Reasoning) is the reasoning model in the pair, while GLM-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. GLM-5 gives you the larger context window at 200K, compared with 128K for Ministral 3 8B (Reasoning).
Pick GLM-5 if you want the stronger benchmark profile. Ministral 3 8B (Reasoning) only becomes the better choice if you want the stronger reasoning-first profile.
GLM-5
62.3
Ministral 3 8B (Reasoning)
38.5
GLM-5
41.1
Ministral 3 8B (Reasoning)
15.2
GLM-5
69.2
Ministral 3 8B (Reasoning)
33.4
GLM-5
79.4
Ministral 3 8B (Reasoning)
42.1
GLM-5
62.1
Ministral 3 8B (Reasoning)
30
GLM-5
85
Ministral 3 8B (Reasoning)
70
GLM-5
82.1
Ministral 3 8B (Reasoning)
61.7
GLM-5
84.8
Ministral 3 8B (Reasoning)
47.8
GLM-5 is ahead overall, 65 to 36. The biggest single separator in this matchup is MMLU, where the scores are 88 and 30.
GLM-5 has the edge for knowledge tasks in this comparison, averaging 62.1 versus 30. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GLM-5 has the edge for coding in this comparison, averaging 41.1 versus 15.2. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
GLM-5 has the edge for math in this comparison, averaging 84.8 versus 47.8. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
GLM-5 has the edge for reasoning in this comparison, averaging 79.4 versus 42.1. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
GLM-5 has the edge for agentic tasks in this comparison, averaging 62.3 versus 38.5. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
GLM-5 has the edge for multimodal and grounded tasks in this comparison, averaging 69.2 versus 33.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GLM-5 has the edge for instruction following in this comparison, averaging 85 versus 70. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GLM-5 has the edge for multilingual tasks in this comparison, averaging 82.1 versus 61.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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