Alibaba Cloud (Qwen)Qwen3.7-MaxVSMoonshot AI (Kimi)kimi-k2.6
Our Take
We recommend kimi-k2.6 for a 2.2x API cost saving at identical performance levels. While both models deliver similar intelligence, kimi-k2.6 is the optimal choice for high-volume pipelines. Choose kimi-k2.6 for budget efficiency without sacrificing quality.
▶WHY?
Benchmark Calculations & Evidence:
Performance Match: Both models perform almost identically, with an average score gap of just 2.0% across reasoning and coding benchmarks.
Coding Benchmarks: Both models were evaluated on the SWE-bench Pro benchmark. Qwen3.7-Max scored 60.6%, while kimi-k2.6 scored 58.6%.
Reasoning Benchmarks: Both models were evaluated on the GPQA Diamond benchmark. Qwen3.7-Max scored 92.4%, while kimi-k2.6 scored 90.5%.
Cost Efficiency: kimi-k2.6 pricing ($0.95/M input, $4/M output) is 2.2x cheaper than Qwen3.7-Max ($2.5/M input, $7.5/M output).
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Benchmarks & Scores
Coding (swe-bench-pro)Winner (+2.0%)
60.6%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)Winner (+1.9%)
92.4%graduate-level science QA
Cost & Context
Cost (per 1M tokens)
$3.75Input: $2.50 | Output: $7.50Context WindowLarger
1.05M tokensBenchmarks & Scores
Coding (swe-bench-pro)
58.6%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)
90.5%graduate-level science QA
Cost & Context
Cost (per 1M tokens)2.2x cheaper
$1.71Input: $0.95 | Output: $4.00Context Window
262.14k tokensFrequently Asked Questions about Qwen3.7-Max vs kimi-k2.6
kimi-k2.6 is cheaper than Qwen3.7-Max. kimi-k2.6 has a blended cost of $1.71/1M tokens, which is about 2.2x cheaper than Qwen3.7-Max at $3.75/1M tokens.
Qwen3.7-Max is better for coding tasks on this benchmark. It scores 60.6% on swe-bench-pro (complex codebases, multi-file repositories, and architectural planning) compared to kimi-k2.6 which scores 58.6%.
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