GLM-4.7 vs MiniMax M3

Pick MiniMax M3 if you want the stronger benchmark profile. GLM-4.7 only becomes the better choice if coding is the priority or you want the cheaper token bill.

MiniMax M3 is clearly ahead on the provisional aggregate, 76 to 68. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

MiniMax M3's sharpest advantage is in agentic, where it averages 71.9 against 45.3. The single biggest benchmark swing on the page is BrowseComp, 52% to 83.5%. GLM-4.7 does hit back in coding, so the answer changes if that is the part of the workload you care about most.

MiniMax M3 is also the more expensive model on tokens at $0.30 input / $1.20 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 MiniMax M3 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. MiniMax M3 gives you the larger context window at 1M, compared with 200K for GLM-4.7.