Head-to-head comparison across 6benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5
77
Kimi K2.5
68
Verified leaderboard positions: GLM-5 #12 · Kimi K2.5 #9
Pick GLM-5 if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if instruction following is the priority or you need the larger 256K context window.
Agentic
+1.6 difference
Coding
+1.0 difference
Reasoning
+0.2 difference
Knowledge
+5.6 difference
Multilingual
+0.8 difference
Inst. Following
+1.3 difference
GLM-5
Kimi K2.5
$0 / $0
$0.5 / $2.8
74 t/s
45 t/s
1.64s
2.38s
200K
256K
Pick GLM-5 if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if instruction following is the priority or you need the larger 256K context window.
GLM-5 is clearly ahead on the provisional aggregate, 77 to 68. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5's sharpest advantage is in knowledge, where it averages 70.7 against 65.1. The single biggest benchmark swing on the page is HLE, 50.4% to 30.1%. Kimi K2.5 does hit back in instruction following, 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-5. That is roughly Infinityx on output cost alone. Kimi K2.5 gives you the larger context window at 256K, compared with 200K for GLM-5.
GLM-5 is ahead on BenchLM's provisional leaderboard, 77 to 68. The biggest single separator in this matchup is HLE, where the scores are 50.4% and 30.1%.
GLM-5 has the edge for knowledge tasks in this comparison, averaging 70.7 versus 65.1. 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 63.2. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for reasoning in this comparison, averaging 61 versus 60.8. Inside this category, LongBench v2 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, Toolathlon is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for instruction following in this comparison, averaging 93.9 versus 92.6. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GLM-5 has the edge for multilingual tasks in this comparison, averaging 83.1 versus 82.3. Inside this category, NOVA-63 is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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