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AnthropicClaude Fable 5VSZ.ai (Zhipu AI)GLM-5.1

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

Our Take

We recommend Claude Fable 5 if you need peak intelligence for reasoning and coding tasks, or the 9.3x cheaper GLM-5.1 to optimize your budget for high-volume pipelines. While Claude Fable 5 holds a clear performance lead, it carries a heavy price premium. Choose Claude Fable 5 for complex logic, or GLM-5.1 for budget efficiency.
▶WHY?
Benchmark Calculations & Evidence:
  • Coding Benchmarks: Both models were evaluated on the SWE-bench Pro benchmark. Claude Fable 5 scored 80.3%, while GLM-5.1 scored 58.4%.
  • Reasoning Benchmarks: Both models were evaluated on the GPQA Diamond benchmark. Claude Fable 5 scored 94.1%, while GLM-5.1 scored 86.2%.
  • Cost Efficiency: GLM-5.1 pricing ($1.4/M input, $4.4/M output) is 9.3x cheaper than Claude Fable 5 ($10/M input, $50/M output).
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    Model Specs

    Claude Fable 5

    Website

    Benchmarks & Scores

    Coding (swe-bench-pro)Winner (+21.9%)
    80.3%

    complex codebases, multi-file repositories, and architectural planning

    Reasoning (gpqa-diamond)Winner (+7.9%)
    94.1%

    graduate-level science QA

    Cost & Context

    Cost (per 1M tokens)
    $20.00Input: $10.00 | Output: $50.00
    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)9.3x cheaper
    $2.15Input: $1.40 | Output: $4.40
    Context Window
    202.75k tokens
    Read our data collection methodology

    Frequently Asked Questions about Claude Fable 5 vs GLM-5.1

    GLM-5.1 is cheaper than Claude Fable 5. GLM-5.1 has a blended cost of $2.15/1M tokens, which is about 9.3x cheaper than Claude Fable 5 at $20.00/1M tokens.

    Claude Fable 5 is better for coding tasks on this benchmark. It scores 80.3% 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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