Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-4.7
71
MiMo-V2.5
74
Pick MiMo-V2.5 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.
Agentic
+20.5 difference
Coding
+14.5 difference
GLM-4.7
MiMo-V2.5
$0 / $0
$0.4 / $2
82 t/s
N/A
1.10s
N/A
200K
1M
Pick MiMo-V2.5 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.
MiMo-V2.5 has the cleaner provisional overall profile here, landing at 74 versus 71. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
MiMo-V2.5's sharpest advantage is in agentic, where it averages 65.8 against 45.3. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 41% to 65.8%. GLM-4.7 does hit back in coding, so the answer changes if that is the part of the workload you care about most.
MiMo-V2.5 is also the more expensive model on tokens at $0.40 input / $2.00 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. MiMo-V2.5 gives you the larger context window at 1M, compared with 200K for GLM-4.7.
MiMo-V2.5 is ahead on BenchLM's provisional leaderboard, 74 to 71. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 41% and 65.8%.
GLM-4.7 has the edge for coding in this comparison, averaging 70.6 versus 56.1. MiMo-V2.5 stays close enough that the answer can still flip depending on your workload.
MiMo-V2.5 has the edge for agentic tasks in this comparison, averaging 65.8 versus 45.3. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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