Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.1
74
Kimi K2.5 (Reasoning)
75
Verified leaderboard positions: GLM-5.1 #30 · Kimi K2.5 (Reasoning) unranked
Pick Kimi K2.5 (Reasoning) if you want the stronger benchmark profile. GLM-5.1 only becomes the better choice if agentic is the priority or you need the larger 203K context window.
Agentic
+10.7 difference
Coding
+15.9 difference
Knowledge
+35.0 difference
GLM-5.1
Kimi K2.5 (Reasoning)
$1.4 / $4.4
$0.6 / $3
N/A
N/A
N/A
N/A
203K
128K
Pick Kimi K2.5 (Reasoning) if you want the stronger benchmark profile. GLM-5.1 only becomes the better choice if agentic is the priority or you need the larger 203K context window.
Kimi K2.5 (Reasoning) finishes one point ahead on BenchLM's provisional leaderboard, 75 to 74. 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 52.3. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 63.5% to 50.8%. GLM-5.1 does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
GLM-5.1 is also the more expensive model on tokens at $1.40 input / $4.40 output per 1M tokens, versus $0.60 input / $3.00 output per 1M tokens for Kimi K2.5 (Reasoning). GLM-5.1 gives you the larger context window at 203K, compared with 128K for Kimi K2.5 (Reasoning).
Kimi K2.5 (Reasoning) is ahead on BenchLM's provisional leaderboard, 75 to 74. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 63.5% and 50.8%.
Kimi K2.5 (Reasoning) has the edge for knowledge tasks in this comparison, averaging 87.3 versus 52.3. Inside this category, AA-Omniscience Hallucination Rate 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 60.9. Inside this category, Vibe Code Bench is the benchmark that creates the most daylight between them.
GLM-5.1 has the edge for agentic tasks in this comparison, averaging 65.3 versus 54.6. Inside this category, Gert Labs is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
For engineers, researchers, and the plain curious — a weekly brief on new models, ranking shifts, and pricing changes.
Free. No spam. Unsubscribe anytime.