Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.1
83
Kimi K2.5
64
Verified leaderboard positions: GLM-5.1 #21 · Kimi K2.5 #11
Pick GLM-5.1 if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Agentic
+10.7 difference
Coding
+3.3 difference
Knowledge
+12.8 difference
GLM-5.1
Kimi K2.5
$1.4 / $4.4
$0.6 / $3
N/A
45 t/s
N/A
2.38s
203K
256K
Pick GLM-5.1 if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
GLM-5.1 is clearly ahead on the provisional aggregate, 83 to 64. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5.1's sharpest advantage is in agentic, where it averages 65.3 against 54.6. The single biggest benchmark swing on the page is HLE, 52.3% to 30.1%. Kimi K2.5 does hit back in knowledge, 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. GLM-5.1 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 203K for GLM-5.1.
GLM-5.1 is ahead on BenchLM's provisional leaderboard, 83 to 64. The biggest single separator in this matchup is HLE, where the scores are 52.3% and 30.1%.
Kimi K2.5 has the edge for knowledge tasks in this comparison, averaging 65.1 versus 52.3. Inside this category, HLE is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for coding in this comparison, averaging 64.2 versus 60.9. Inside this category, SWE-bench Pro 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, MCP Atlas is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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