Head-to-head comparison across 4benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Kimi K2.5 (Reasoning)
78
Qwen3.6-27B
72
Verified leaderboard positions: Kimi K2.5 (Reasoning) unranked · Qwen3.6-27B #10
Pick Kimi K2.5 (Reasoning) if you want the stronger benchmark profile. Qwen3.6-27B only becomes the better choice if agentic is the priority or you need the larger 262K context window.
Agentic
+4.7 difference
Coding
+6.2 difference
Knowledge
+25.1 difference
Multimodal
+2.7 difference
Kimi K2.5 (Reasoning)
Qwen3.6-27B
$null / $null
$0 / $0
N/A
N/A
N/A
N/A
128K
262K
Pick Kimi K2.5 (Reasoning) if you want the stronger benchmark profile. Qwen3.6-27B only becomes the better choice if agentic is the priority or you need the larger 262K context window.
Kimi K2.5 (Reasoning) is clearly ahead on the provisional aggregate, 78 to 72. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Kimi K2.5 (Reasoning)'s sharpest advantage is in knowledge, where it averages 87.3 against 62.2. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 50.8% to 59.3%. Qwen3.6-27B does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
Qwen3.6-27B gives you the larger context window at 262K, compared with 128K for Kimi K2.5 (Reasoning).
Kimi K2.5 (Reasoning) is ahead on BenchLM's provisional leaderboard, 78 to 72. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 50.8% and 59.3%.
Kimi K2.5 (Reasoning) has the edge for knowledge tasks in this comparison, averaging 87.3 versus 62.2. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Kimi K2.5 (Reasoning) has the edge for coding in this comparison, averaging 76.8 versus 70.6. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Qwen3.6-27B has the edge for agentic tasks in this comparison, averaging 59.3 versus 54.6. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Kimi K2.5 (Reasoning) has the edge for multimodal and grounded tasks in this comparison, averaging 78.5 versus 75.8. Inside this category, MMMU-Pro 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.