Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
GLM-4.7 is clearly ahead on the aggregate, 74 to 32. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-4.7 is the reasoning model in the pair, while DBRX Instruct 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 32K for DBRX Instruct.
Pick GLM-4.7 if you want the stronger benchmark profile. DBRX Instruct only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
GLM-4.7
70.3
DBRX Instruct
73.7
GLM-4.7
54.7
DBRX Instruct
70.1
GLM-4.7 is ahead overall, 74 to 32. The biggest single separator in this matchup is MMLU, where the scores are 86 and 73.7.
DBRX Instruct has the edge for knowledge tasks in this comparison, averaging 73.7 versus 70.3. Inside this category, MMLU is the benchmark that creates the most daylight between them.
DBRX Instruct has the edge for coding in this comparison, averaging 70.1 versus 54.7. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
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