Head-to-head comparison across 6benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Kimi K2.5
64
Qwen3.5-27B
63
Verified leaderboard positions: Kimi K2.5 #11 · Qwen3.5-27B #16
Pick Kimi K2.5 if you want the stronger benchmark profile. Qwen3.5-27B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Agentic
+3.0 difference
Coding
+1.2 difference
Reasoning
+0.4 difference
Knowledge
+15.5 difference
Multilingual
+0.1 difference
Inst. Following
+1.1 difference
Kimi K2.5
Qwen3.5-27B
$0.6 / $3
$0 / $0
45 t/s
N/A
2.38s
N/A
256K
262K
Pick Kimi K2.5 if you want the stronger benchmark profile. Qwen3.5-27B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Kimi K2.5 finishes one point ahead on BenchLM's provisional leaderboard, 64 to 63. 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's sharpest advantage is in agentic, where it averages 54.6 against 51.6. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 50.8% to 41.6%. Qwen3.5-27B 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 $0.60 input / $3.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.5-27B. That is roughly Infinityx on output cost alone. Qwen3.5-27B 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. Qwen3.5-27B gives you the larger context window at 262K, compared with 256K for Kimi K2.5.
Kimi K2.5 is ahead on BenchLM's provisional leaderboard, 64 to 63. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 50.8% and 41.6%.
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 80.6 versus 65.1. Inside this category, SuperGPQA 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. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for reasoning in this comparison, averaging 61 versus 60.6. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for agentic tasks in this comparison, averaging 54.6 versus 51.6. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for instruction following in this comparison, averaging 95 versus 93.9. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for multilingual tasks in this comparison, averaging 82.3 versus 82.2. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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