Head-to-head comparison across 6benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Kimi K2.5
64
Qwen3.5-35B-A3B
56
Verified leaderboard positions: Kimi K2.5 #11 · Qwen3.5-35B-A3B #18
Pick Kimi K2.5 if you want the stronger benchmark profile. Qwen3.5-35B-A3B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Agentic
+4.0 difference
Coding
+5.8 difference
Reasoning
+2.0 difference
Knowledge
+14.2 difference
Multilingual
+1.3 difference
Inst. Following
+2.0 difference
Kimi K2.5
Qwen3.5-35B-A3B
$0.6 / $3
$0 / $0
45 t/s
N/A
2.38s
N/A
256K
262K
Pick Kimi K2.5 if you want the stronger benchmark profile. Qwen3.5-35B-A3B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Kimi K2.5 is clearly ahead on the provisional aggregate, 64 to 56. 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 coding, where it averages 64.2 against 58.4. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 50.8% to 40.5%. Qwen3.5-35B-A3B does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
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 Qwen3.5-35B-A3B. That is roughly Infinityx on output cost alone. Qwen3.5-35B-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. Qwen3.5-35B-A3B gives you the larger context window at 262K, compared with 256K for Kimi K2.5.
Kimi K2.5 is ahead on BenchLM's provisional leaderboard, 64 to 56. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 50.8% and 40.5%.
Qwen3.5-35B-A3B has the edge for knowledge tasks in this comparison, averaging 79.3 versus 65.1. Inside this category, SuperGPQA 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 58.4. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for reasoning in this comparison, averaging 61 versus 59. Inside this category, LongBench v2 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 50.6. Inside this category, Terminal-Bench 2.0 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 91.9. Inside this category, IFEval 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 81. 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.
For engineers, researchers, and the plain curious — a weekly brief on new models, ranking shifts, and pricing changes.
Free. No spam. Unsubscribe anytime.