DeepSeek V3 vs GLM-4.7

Pick GLM-4.7 if you want the stronger benchmark profile. DeepSeek V3 only becomes the better choice if knowledge is the priority or you would rather avoid the extra latency and token burn of a reasoning model.

GLM-4.7 is clearly ahead on the provisional aggregate, 69 to 36. 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 coding, where it averages 70.6 against 39.2. The single biggest benchmark swing on the page is LiveCodeBench, 37.6% to 84.9%. DeepSeek V3 does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.

DeepSeek V3 is also the more expensive model on tokens at $0.27 input / $1.10 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for GLM-4.7. That is roughly Infinityx on output cost alone. GLM-4.7 is the reasoning model in the pair, while DeepSeek V3 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 DeepSeek V3.