Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
GLM-5 (Reasoning) is clearly ahead on the aggregate, 84 to 28. 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 coding, where it averages 69.3 against 54.8. The single biggest benchmark swing on the page is HumanEval, 88 to 54.8.
GLM-5 (Reasoning) is the reasoning model in the pair, while Mixtral 8x22B Instruct v0.1 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 64K for Mixtral 8x22B Instruct v0.1.
Pick GLM-5 (Reasoning) if you want the stronger benchmark profile. Mixtral 8x22B Instruct v0.1 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
GLM-5 (Reasoning)
80.3
Mixtral 8x22B Instruct v0.1
71.4
GLM-5 (Reasoning)
69.3
Mixtral 8x22B Instruct v0.1
54.8
GLM-5 (Reasoning) is ahead overall, 84 to 28. The biggest single separator in this matchup is HumanEval, where the scores are 88 and 54.8.
GLM-5 (Reasoning) has the edge for knowledge tasks in this comparison, averaging 80.3 versus 71.4. 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 69.3 versus 54.8. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
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