Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-4.7 is clearly ahead on the aggregate, 67 to 32. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-4.7's sharpest advantage is in reasoning, where it averages 80.2 against 36.1. The single biggest benchmark swing on the page is MMLU, 86 to 28.
GLM-4.7 is the reasoning model in the pair, while Ministral 3 8B 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-4.7 gives you the larger context window at 200K, compared with 128K for Ministral 3 8B.
Pick GLM-4.7 if you want the stronger benchmark profile. Ministral 3 8B only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
GLM-4.7
66.1
Ministral 3 8B
28.9
GLM-4.7
46.6
Ministral 3 8B
14.2
GLM-4.7
70.5
Ministral 3 8B
32.4
GLM-4.7
80.2
Ministral 3 8B
36.1
GLM-4.7
61.8
Ministral 3 8B
28
GLM-4.7
85
Ministral 3 8B
69
GLM-4.7
79.1
Ministral 3 8B
61.7
GLM-4.7
85
Ministral 3 8B
43.3
GLM-4.7 is ahead overall, 67 to 32. The biggest single separator in this matchup is MMLU, where the scores are 86 and 28.
GLM-4.7 has the edge for knowledge tasks in this comparison, averaging 61.8 versus 28. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for coding in this comparison, averaging 46.6 versus 14.2. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for math in this comparison, averaging 85 versus 43.3. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for reasoning in this comparison, averaging 80.2 versus 36.1. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for agentic tasks in this comparison, averaging 66.1 versus 28.9. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for multimodal and grounded tasks in this comparison, averaging 70.5 versus 32.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GLM-4.7 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.
GLM-4.7 has the edge for multilingual tasks in this comparison, averaging 79.1 versus 61.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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