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Alibaba Cloud (Qwen)Qwen3.7-MaxVSZ.ai (Zhipu AI)GLM-5.1

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

Our Take

We recommend Qwen3.7-Max for its clear benchmark advantage, or the 1.7x cheaper GLM-5.1 only if your budget requires optimizing costs for very high-volume pipelines. While Qwen3.7-Max offers superior reasoning and coding, it carries a moderate price premium. Choose Qwen3.7-Max for quality, or GLM-5.1 for cost optimization.
▶WHY?
Benchmark Calculations & Evidence:
  • Coding Benchmarks: Both models were evaluated on the SWE-bench Pro benchmark. Qwen3.7-Max scored 60.6%, while GLM-5.1 scored 58.4%.
  • Reasoning Benchmarks: Both models were evaluated on the GPQA Diamond benchmark. Qwen3.7-Max scored 92.4%, while GLM-5.1 scored 86.2%.
  • Cost Efficiency: GLM-5.1 pricing ($1.4/M input, $4.4/M output) is 1.7x 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 (+2.2%)
    60.6%

    complex codebases, multi-file repositories, and architectural planning

    Reasoning (gpqa-diamond)Winner (+6.2%)
    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

    GLM-5.1

    Open SourceAPI Available
    Website🤗HF

    Benchmarks & Scores

    Coding (swe-bench-pro)
    58.4%

    complex codebases, multi-file repositories, and architectural planning

    Reasoning (gpqa-diamond)
    86.2%

    graduate-level science QA

    Cost & Context

    Cost (per 1M tokens)1.7x cheaper
    $2.15Input: $1.40 | Output: $4.40
    Context Window
    202.75k tokens
    Read our data collection methodology

    Frequently Asked Questions about Qwen3.7-Max vs GLM-5.1

    GLM-5.1 is cheaper than Qwen3.7-Max. GLM-5.1 has a blended cost of $2.15/1M tokens, which is about 1.7x 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 GLM-5.1 which scores 58.4%.

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