Head-to-head comparison across 4benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Kimi K2.5
68
Qwen3.6-27B
72
Verified leaderboard positions: Kimi K2.5 #9 · Qwen3.6-27B #10
Pick Qwen3.6-27B if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if knowledge is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Agentic
+4.7 difference
Coding
+6.4 difference
Knowledge
+2.9 difference
Multimodal
+2.7 difference
Kimi K2.5
Qwen3.6-27B
$0.5 / $2.8
$0 / $0
45 t/s
N/A
2.38s
N/A
256K
262K
Pick Qwen3.6-27B if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if knowledge is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.6-27B is clearly ahead on the provisional aggregate, 72 to 68. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.6-27B'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, 50.8% to 59.3%. Kimi K2.5 does hit back in knowledge, 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 Qwen3.6-27B. That is roughly Infinityx on output cost alone. Qwen3.6-27B 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. Qwen3.6-27B gives you the larger context window at 262K, compared with 256K for Kimi K2.5.
Qwen3.6-27B is ahead on BenchLM's provisional leaderboard, 72 to 68. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 50.8% and 59.3%.
Kimi K2.5 has the edge for knowledge tasks in this comparison, averaging 65.1 versus 62.2. Inside this category, HLE is the benchmark that creates the most daylight between them.
Qwen3.6-27B has the edge for coding in this comparison, averaging 70.6 versus 64.2. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Qwen3.6-27B has the edge for agentic tasks in this comparison, averaging 59.3 versus 54.6. Inside this category, Claw-Eval is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for multimodal and grounded tasks in this comparison, averaging 78.5 versus 75.8. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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