Head-to-head comparison across 7benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Kimi K2.5
64
Qwen3.5 397B
64
Verified leaderboard positions: Kimi K2.5 #11 · Qwen3.5 397B #15
Treat this as a split decision. Kimi K2.5 makes more sense if coding is the priority or you want the cheaper token bill; Qwen3.5 397B is the better fit 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
Treat this as a split decision. Kimi K2.5 makes more sense if coding is the priority or you want the cheaper token bill; Qwen3.5 397B is the better fit if multilingual is the priority.
Kimi K2.5 and Qwen3.5 397B finish on the same provisional overall score, so this is less about a single winner and more about where the edge shows up. The provisional headline says tie; the benchmark table is where the real choice happens.
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 and Qwen3.5 397B are tied on the provisional overall score, so the right pick depends on which category matters most for your use case.
Qwen3.5 397B has the edge for knowledge tasks in this comparison, averaging 65.2 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 60.3. Inside this category, LiveCodeBench v6 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, LongBench v2 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, DeepPlanning 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, VideoMMMU 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.
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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