Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-5 is clearly ahead on the aggregate, 65 to 31. 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 35.3. The single biggest benchmark swing on the page is HumanEval, 80 to 16.
Ministral 3 3B (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 3B (Reasoning).
Pick GLM-5 if you want the stronger benchmark profile. Ministral 3 3B (Reasoning) only becomes the better choice if you want the stronger reasoning-first profile.
GLM-5
62.3
Ministral 3 3B (Reasoning)
34
GLM-5
41.1
Ministral 3 3B (Reasoning)
7.2
GLM-5
69.2
Ministral 3 3B (Reasoning)
30.4
GLM-5
79.4
Ministral 3 3B (Reasoning)
35.3
GLM-5
62.1
Ministral 3 3B (Reasoning)
25.2
GLM-5
85
Ministral 3 3B (Reasoning)
68
GLM-5
82.1
Ministral 3 3B (Reasoning)
59.7
GLM-5
84.8
Ministral 3 3B (Reasoning)
40.9
GLM-5 is ahead overall, 65 to 31. The biggest single separator in this matchup is HumanEval, where the scores are 80 and 16.
GLM-5 has the edge for knowledge tasks in this comparison, averaging 62.1 versus 25.2. 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 7.2. 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 40.9. 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 35.3. 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 34. Inside this category, Terminal-Bench 2.0 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 30.4. 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 68. 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 59.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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