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Alibaba Cloud (Qwen)Qwen3.7-MaxVSMoonshot AI (Kimi)kimi-k2.6

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

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).
  • Was this recommendation helpful?
    Model Specs

    Qwen3.7-Max

    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: $7.50
    Context WindowLarger
    1.05M tokens
    Model Specs

    kimi-k2.6

    Open SourceAPI Available

    Benchmarks & 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: $4.00
    Context Window
    262.14k tokens

    Frequently 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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