Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5
77
Kimi K2.5 (Reasoning)
78
Verified leaderboard positions: GLM-5 #13 · Kimi K2.5 (Reasoning) unranked
Pick Kimi K2.5 (Reasoning) if you want the stronger benchmark profile. GLM-5 only becomes the better choice if agentic is the priority or you need the larger 200K context window.
Agentic
+1.6 difference
Coding
+13.6 difference
Knowledge
+16.6 difference
GLM-5
Kimi K2.5 (Reasoning)
$0 / $0
$null / $null
74 t/s
N/A
1.64s
N/A
200K
128K
Pick Kimi K2.5 (Reasoning) if you want the stronger benchmark profile. GLM-5 only becomes the better choice if agentic is the priority or you need the larger 200K context window.
Kimi K2.5 (Reasoning) finishes one point ahead on BenchLM's provisional leaderboard, 78 to 77. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
Kimi K2.5 (Reasoning)'s sharpest advantage is in knowledge, where it averages 87.3 against 70.7. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 56.2% to 50.8%. GLM-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 (Reasoning) is the reasoning model in the pair, while GLM-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. GLM-5 gives you the larger context window at 200K, compared with 128K for Kimi K2.5 (Reasoning).
Kimi K2.5 (Reasoning) is ahead on BenchLM's provisional leaderboard, 78 to 77. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 56.2% and 50.8%.
Kimi K2.5 (Reasoning) has the edge for knowledge tasks in this comparison, averaging 87.3 versus 70.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Kimi K2.5 (Reasoning) has the edge for coding in this comparison, averaging 76.8 versus 63.2. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
GLM-5 has the edge for agentic tasks in this comparison, averaging 56.2 versus 54.6. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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