Head-to-head comparison across 4benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Kimi K2.5
68
Qwen3.6-35B-A3B
64
Verified leaderboard positions: Kimi K2.5 #11 · Qwen3.6-35B-A3B #13
Pick Kimi K2.5 if you want the stronger benchmark profile. Qwen3.6-35B-A3B only becomes the better choice if coding is the priority or you need the larger 262K context window.
Agentic
+3.1 difference
Coding
+7.9 difference
Knowledge
+4.6 difference
Multimodal
+3.2 difference
Kimi K2.5
Qwen3.6-35B-A3B
$0.5 / $2.8
N/A
45 t/s
N/A
2.38s
N/A
128K
262K
Pick Kimi K2.5 if you want the stronger benchmark profile. Qwen3.6-35B-A3B only becomes the better choice if coding is the priority or you need the larger 262K context window.
Kimi K2.5 is clearly ahead on the provisional aggregate, 68 to 64. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Kimi K2.5's sharpest advantage is in knowledge, where it averages 65.1 against 60.5. The single biggest benchmark swing on the page is MCP Atlas, 29.5% to 62.8%. Qwen3.6-35B-A3B does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Qwen3.6-35B-A3B 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. Qwen3.6-35B-A3B gives you the larger context window at 262K, compared with 128K for Kimi K2.5.
Kimi K2.5 is ahead on BenchLM's provisional leaderboard, 68 to 64. The biggest single separator in this matchup is MCP Atlas, where the scores are 29.5% and 62.8%.
Kimi K2.5 has the edge for knowledge tasks in this comparison, averaging 65.1 versus 60.5. Inside this category, HLE is the benchmark that creates the most daylight between them.
Qwen3.6-35B-A3B has the edge for coding in this comparison, averaging 66.9 versus 59. Inside this category, SWE Multilingual 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 51.5. Inside this category, MCP Atlas 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 75.3. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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