Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Kimi K2.6
85
Qwen3 235B 2507
32
Verified leaderboard positions: Kimi K2.6 #9 · Qwen3 235B 2507 unranked
Pick Kimi K2.6 if you want the stronger benchmark profile. Qwen3 235B 2507 only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Knowledge
+22.4 difference
Kimi K2.6
Qwen3 235B 2507
$0.95 / $4
$0 / $0
N/A
N/A
N/A
N/A
256K
128K
Pick Kimi K2.6 if you want the stronger benchmark profile. Qwen3 235B 2507 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, 85 to 32. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
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 235B 2507. That is roughly Infinityx on output cost alone. Kimi K2.6 is the reasoning model in the pair, while Qwen3 235B 2507 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. Kimi K2.6 gives you the larger context window at 256K, compared with 128K for Qwen3 235B 2507.
Kimi K2.6 is ahead on BenchLM's provisional leaderboard, 85 to 32. The biggest single separator in this matchup is GPQA, where the scores are 90.5% and 77.5%.
Qwen3 235B 2507 has the edge for knowledge tasks in this comparison, averaging 76.2 versus 53.8. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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