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 61. 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 agentic, where it averages 78.3 against 55.3. The single biggest benchmark swing on the page is AIME 2023, 98 to 67.
GLM-5 (Reasoning) is the reasoning model in the pair, while Nemotron 3 Super 120B A12B 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. Nemotron 3 Super 120B A12B gives you the larger context window at 256K, compared with 200K for GLM-5 (Reasoning).
Pick GLM-5 (Reasoning) if you want the stronger benchmark profile. Nemotron 3 Super 120B A12B only becomes the better choice if you need the larger 256K context window or you would rather avoid the extra latency and token burn of a reasoning model.
GLM-5 (Reasoning)
78.3
Nemotron 3 Super 120B A12B
55.3
GLM-5 (Reasoning)
62.5
Nemotron 3 Super 120B A12B
44.2
GLM-5 (Reasoning)
78.5
Nemotron 3 Super 120B A12B
60.4
GLM-5 (Reasoning)
88.9
Nemotron 3 Super 120B A12B
71.8
GLM-5 (Reasoning)
72
Nemotron 3 Super 120B A12B
55.8
GLM-5 (Reasoning)
92
Nemotron 3 Super 120B A12B
86
GLM-5 (Reasoning)
86.4
Nemotron 3 Super 120B A12B
81.5
GLM-5 (Reasoning)
94.4
Nemotron 3 Super 120B A12B
74.6
GLM-5 (Reasoning) is ahead overall, 78 to 61. The biggest single separator in this matchup is AIME 2023, where the scores are 98 and 67.
GLM-5 (Reasoning) has the edge for knowledge tasks in this comparison, averaging 72 versus 55.8. 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 44.2. 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 74.6. 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 71.8. 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 55.3. 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 60.4. 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 86. 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 81.5. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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