Head-to-head comparison across 6benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Kimi K2.5
64
Qwen3.7 Max
93
Verified leaderboard positions: Kimi K2.5 #13 · Qwen3.7 Max #2
Pick Qwen3.7 Max if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if instruction following is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Agentic
+15.1 difference
Coding
+9.4 difference
Reasoning
+29.4 difference
Knowledge
+6.1 difference
Multilingual
+4.7 difference
Inst. Following
+4.9 difference
Kimi K2.5
Qwen3.7 Max
$0.6 / $3
$null / $null
45 t/s
N/A
2.38s
N/A
256K
1M
Pick Qwen3.7 Max if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if instruction following is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.7 Max is clearly ahead on the provisional aggregate, 93 to 64. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.7 Max's sharpest advantage is in reasoning, where it averages 90.4 against 61. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 50.8% to 69.7%. Kimi K2.5 does hit back in instruction following, so the answer changes if that is the part of the workload you care about most.
Qwen3.7 Max 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. Qwen3.7 Max gives you the larger context window at 1M, compared with 256K for Kimi K2.5.
Qwen3.7 Max is ahead on BenchLM's provisional leaderboard, 93 to 64. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 50.8% and 69.7%.
Qwen3.7 Max has the edge for knowledge tasks in this comparison, averaging 71.2 versus 65.1. Inside this category, HLE is the benchmark that creates the most daylight between them.
Qwen3.7 Max has the edge for coding in this comparison, averaging 73.6 versus 64.2. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Qwen3.7 Max has the edge for reasoning in this comparison, averaging 90.4 versus 61. Kimi K2.5 stays close enough that the answer can still flip depending on your workload.
Qwen3.7 Max has the edge for agentic tasks in this comparison, averaging 69.7 versus 54.6. Inside this category, MCP Atlas 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 89. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Qwen3.7 Max has the edge for multilingual tasks in this comparison, averaging 87 versus 82.3. 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.
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