Head-to-head comparison across 7benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Kimi K2.5
64
Qwen3.5-122B-A10B
64
Verified leaderboard positions: Kimi K2.5 #15 · Qwen3.5-122B-A10B #11
Treat this as a split decision. Kimi K2.5 makes more sense if multimodal & grounded is the priority or you would rather avoid the extra latency and token burn of a reasoning model; Qwen3.5-122B-A10B is the better fit if knowledge is the priority or you want the cheaper token bill.
Agentic
+1.5 difference
Coding
+7.8 difference
Reasoning
+0.8 difference
Knowledge
+16.5 difference
Multilingual
+0.1 difference
Multimodal
+1.3 difference
Inst. Following
+0.5 difference
Kimi K2.5
Qwen3.5-122B-A10B
$0.6 / $3
$0 / $0
45 t/s
N/A
2.38s
N/A
256K
262K
Treat this as a split decision. Kimi K2.5 makes more sense if multimodal & grounded is the priority or you would rather avoid the extra latency and token burn of a reasoning model; Qwen3.5-122B-A10B is the better fit if knowledge is the priority or you want the cheaper token bill.
Kimi K2.5 and Qwen3.5-122B-A10B 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.
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 Qwen3.5-122B-A10B. That is roughly Infinityx on output cost alone. Qwen3.5-122B-A10B 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. Qwen3.5-122B-A10B gives you the larger context window at 262K, compared with 256K for Kimi K2.5.
Kimi K2.5 and Qwen3.5-122B-A10B are tied on the provisional overall score, so the right pick depends on which category matters most for your use case.
Qwen3.5-122B-A10B has the edge for knowledge tasks in this comparison, averaging 81.6 versus 65.1. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.
Qwen3.5-122B-A10B has the edge for coding in this comparison, averaging 72 versus 64.2. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for reasoning in this comparison, averaging 61 versus 60.2. Inside this category, CritPt is the benchmark that creates the most daylight between them.
Qwen3.5-122B-A10B has the edge for agentic tasks in this comparison, averaging 56.1 versus 54.6. Inside this category, GDPval-AA 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 77.2. Inside this category, MMVU 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 93.4. Inside this category, AA-IFBench 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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