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AnthropicClaude Haiku 4.5VSZ.ai (Zhipu AI)GLM-5.1

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

Our Take

We recommend GLM-5.1 for complex complex codebases, multi-file repositories, and architectural planning, or the 1.1x cheaper Claude Haiku 4.5 if your budget requires optimizing costs for very high-volume pipelines. While GLM-5.1 offers a clear reasoning advantage, it carries a moderate price premium. Choose GLM-5.1 for architectural codebase planning, or Claude Haiku 4.5 to save on API costs for simple scripts.
WHY?
Benchmark Calculations & Evidence:
  • Coding Evaluation: Claude Haiku 4.5 was evaluated on SWE-bench Verified (scoring 73.3%), while GLM-5.1 was evaluated on SWE-bench Pro (scoring 58.4%).
  • Reasoning Accuracy: Both models were evaluated on the GPQA Diamond benchmark. GLM-5.1 scored 86.2%, while Claude Haiku 4.5 scored 73%.
  • Cost Efficiency: Claude Haiku 4.5 pricing ($1/M input, $5/M output) is 1.1x cheaper than GLM-5.1 ($1.4/M input, $4.4/M output).
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    Model Specs

    Claude Haiku 4.5

    Benchmarks & Scores

    Coding (swe-bench-verified)
    73.3%

    multi-file code and clearly defined tasks

    Reasoning (gpqa-diamond)
    73%

    graduate-level science QA

    Cost & Context

    Cost (per 1M tokens)1.1x cheaper
    $2.00Input: $1.00 | Output: $5.00
    Context Window
    200k tokens
    Model Specs

    GLM-5.1

    Open SourceAPI Available

    Benchmarks & Scores

    Coding (swe-bench-pro)
    58.4%

    complex codebases, multi-file repositories, and architectural planning

    Reasoning (gpqa-diamond)Winner (+13.2%)
    86.2%

    graduate-level science QA

    Cost & Context

    Cost (per 1M tokens)
    $2.15Input: $1.40 | Output: $4.40
    Context WindowLarger
    202.75k tokens

    Frequently Asked Questions about Claude Haiku 4.5 vs GLM-5.1

    Claude Haiku 4.5 is cheaper than GLM-5.1. Claude Haiku 4.5 has a blended cost of $2.00/1M tokens, which is about 1.1x cheaper than GLM-5.1 at $2.15/1M tokens.

    For coding tasks, Claude Haiku 4.5 scores 73.3% on swe-bench-verified (multi-file code and clearly defined tasks), while GLM-5.1 scores 58.4% on swe-bench-pro (complex codebases, multi-file repositories, and architectural planning).

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