Head-to-head comparison across 4benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-4.7
71
Kimi K2.5
68
Verified leaderboard positions: GLM-4.7 unranked · Kimi K2.5 #9
Pick GLM-4.7 if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if agentic is the priority or you need the larger 256K context window.
Agentic
+9.3 difference
Coding
+6.4 difference
Knowledge
+4.5 difference
Math
+0.4 difference
GLM-4.7
Kimi K2.5
$0 / $0
$0.5 / $2.8
82 t/s
45 t/s
1.10s
2.38s
200K
256K
Pick GLM-4.7 if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if agentic is the priority or you need the larger 256K context window.
GLM-4.7 has the cleaner provisional overall profile here, landing at 71 versus 68. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
GLM-4.7's sharpest advantage is in coding, where it averages 70.6 against 64.2. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 41% to 50.8%. Kimi K2.5 does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
Kimi K2.5 is also the more expensive model on tokens at $0.50 input / $2.80 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 Kimi K2.5 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. Kimi K2.5 gives you the larger context window at 256K, compared with 200K for GLM-4.7.
GLM-4.7 is ahead on BenchLM's provisional leaderboard, 71 to 68. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 41% and 50.8%.
Kimi K2.5 has the edge for knowledge tasks in this comparison, averaging 65.1 versus 60.6. Inside this category, HLE is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for coding in this comparison, averaging 70.6 versus 64.2. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for math in this comparison, averaging 96.1 versus 95.7. Inside this category, AIME 2025 is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for agentic tasks in this comparison, averaging 54.6 versus 45.3. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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