Head-to-head comparison across 4benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Kimi K2.6
84
Qwen3.5-122B-A10B
65
Verified leaderboard positions: Kimi K2.6 #6 · Qwen3.5-122B-A10B #8
Pick Kimi K2.6 if you want the stronger benchmark profile. Qwen3.5-122B-A10B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Agentic
+17.0 difference
Coding
Knowledge
+27.8 difference
Multimodal
+2.5 difference
Kimi K2.6
Qwen3.5-122B-A10B
$0.95 / $4
$0 / $0
N/A
N/A
N/A
N/A
256K
262K
Pick Kimi K2.6 if you want the stronger benchmark profile. Qwen3.5-122B-A10B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Kimi K2.6 is clearly ahead on the provisional aggregate, 84 to 65. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Kimi K2.6's sharpest advantage is in agentic, where it averages 73.1 against 56.1. The single biggest benchmark swing on the page is BrowseComp, 83.2% to 63.8%. Qwen3.5-122B-A10B does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
Kimi K2.6 is also the more expensive model on tokens at $0.95 input / $4.00 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 gives you the larger context window at 262K, compared with 256K for Kimi K2.6.
Kimi K2.6 is ahead on BenchLM's provisional leaderboard, 84 to 65. The biggest single separator in this matchup is BrowseComp, where the scores are 83.2% and 63.8%.
Qwen3.5-122B-A10B has the edge for knowledge tasks in this comparison, averaging 81.6 versus 53.8. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Kimi K2.6 and Qwen3.5-122B-A10B are effectively tied for coding here, both landing at 72 on average.
Kimi K2.6 has the edge for agentic tasks in this comparison, averaging 73.1 versus 56.1. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
Kimi K2.6 has the edge for multimodal and grounded tasks in this comparison, averaging 79.7 versus 77.2. Inside this category, V* is the benchmark that creates the most daylight between them.
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