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Alibaba Cloud (Qwen)Qwen3.7-MaxVSMoonshot AI (Kimi)kimi-k2.5

Analysis by:the whichllmmodel Editorial Team|Updated: June 2026

Our Take

We recommend Qwen3.7-Max if you need peak intelligence for reasoning and coding tasks, or the 3.1x cheaper kimi-k2.5 to optimize your budget for high-volume pipelines. While Qwen3.7-Max holds a clear performance lead, it carries a heavy price premium. Choose Qwen3.7-Max for complex logic, or kimi-k2.5 for budget efficiency.
▶WHY?
Benchmark Calculations & Evidence:
  • Coding Benchmarks: Both models were evaluated on the SWE-bench Pro benchmark. Qwen3.7-Max scored 60.6%, while kimi-k2.5 scored 50.7%.
  • Reasoning Benchmarks: Both models were evaluated on the GPQA Diamond benchmark. Qwen3.7-Max scored 92.4%, while kimi-k2.5 scored 87.6%.
  • Cost Efficiency: kimi-k2.5 pricing ($0.6/M input, $3/M output) is 3.1x cheaper than Qwen3.7-Max ($2.5/M input, $7.5/M output).
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    Model Specs

    Qwen3.7-Max

    Website

    Benchmarks & Scores

    Coding (swe-bench-pro)Winner (+9.9%)
    60.6%

    complex codebases, multi-file repositories, and architectural planning

    Reasoning (gpqa-diamond)Winner (+4.8%)
    92.4%

    graduate-level science QA

    Cost & Context

    Cost (per 1M tokens)
    $3.75Input: $2.50 | Output: $7.50
    Context WindowLarger
    1.05M tokens
    Model Specs

    kimi-k2.5

    Open SourceAPI Available
    Website🤗HF

    Benchmarks & Scores

    Coding (swe-bench-pro)
    50.7%

    complex codebases, multi-file repositories, and architectural planning

    Reasoning (gpqa-diamond)
    87.6%

    graduate-level science QA

    Cost & Context

    Cost (per 1M tokens)3.1x cheaper
    $1.20Input: $0.60 | Output: $3.00
    Context Window
    262.14k tokens
    Read our data collection methodology

    Frequently Asked Questions about Qwen3.7-Max vs kimi-k2.5

    kimi-k2.5 is cheaper than Qwen3.7-Max. kimi-k2.5 has a blended cost of $1.20/1M tokens, which is about 3.1x 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-k2.5 which scores 50.7%.

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