Head-to-head comparison across 7benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Kimi K2.5
64
Qwen3.5 397B
63
Verified leaderboard positions: Kimi K2.5 #15 · Qwen3.5 397B #19
Pick Kimi K2.5 if you want the stronger benchmark profile. Qwen3.5 397B only becomes the better choice if multilingual is the priority.
Agentic
+1.6 difference
Coding
+3.9 difference
Reasoning
+2.2 difference
Knowledge
+0.1 difference
Multilingual
+2.4 difference
Multimodal
+1.1 difference
Inst. Following
+1.3 difference
Kimi K2.5
Qwen3.5 397B
$0.6 / $3
$0.6 / $3.6
45 t/s
96 t/s
2.38s
2.44s
256K
128K
Pick Kimi K2.5 if you want the stronger benchmark profile. Qwen3.5 397B only becomes the better choice if multilingual is the priority.
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 coding, where it averages 64.2 against 60.3. The single biggest benchmark swing on the page is MMLU-ProX, 82.3% to 84.7%. Qwen3.5 397B does hit back in multilingual, so the answer changes if that is the part of the workload you care about most.
Qwen3.5 397B is also the more expensive model on tokens at $0.60 input / $3.60 output per 1M tokens, versus $0.60 input / $3.00 output per 1M tokens for Kimi K2.5. Kimi K2.5 gives you the larger context window at 256K, compared with 128K for Qwen3.5 397B.
Kimi K2.5 is ahead on BenchLM's provisional leaderboard, 64 to 63. The biggest single separator in this matchup is MMLU-ProX, where the scores are 82.3% and 84.7%.
Qwen3.5 397B has the edge for knowledge tasks in this comparison, averaging 65.2 versus 65.1. Inside this category, AA-Omniscience Index 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 60.3. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
Qwen3.5 397B has the edge for reasoning in this comparison, averaging 63.2 versus 61. Inside this category, AA-LCR is the benchmark that creates the most daylight between them.
Qwen3.5 397B has the edge for agentic tasks in this comparison, averaging 56.2 versus 54.6. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
Qwen3.5 397B has the edge for multimodal and grounded tasks in this comparison, averaging 79.6 versus 78.5. Inside this category, AA-MMMU-Pro 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, AA-IFBench is the benchmark that creates the most daylight between them.
Qwen3.5 397B has the edge for multilingual tasks in this comparison, averaging 84.7 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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