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DeepSeekDeepSeek V4 FlashVSZ.ai (Zhipu AI)GLM-4.5

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

Our Take

While these models specialize in different coding tasks: DeepSeek V4 Flash is suited for complex codebases, multi-file repositories, and architectural planning, while GLM-4.5 excels at multi-file code and clearly defined tasks, they share very similar reasoning capabilities. DeepSeek V4 Flash is the smarter buy here as it offers the same level of performance while being 5.7x cheaper than GLM-4.5.
WHY?
Benchmark Calculations & Evidence:
  • Coding Evaluation: DeepSeek V4 Flash was evaluated on SWE-bench Pro (scoring 49.1%), while GLM-4.5 was evaluated on SWE-bench Verified (scoring 64.2%).
  • Reasoning Accuracy: Both models were evaluated on the GPQA Diamond benchmark. DeepSeek V4 Flash scored 80%, while GLM-4.5 scored 79.9%.
  • Cost Ratio: GLM-4.5 blended CPM is 5.7x higher than DeepSeek V4 Flash.
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    Model Specs

    DeepSeek V4 Flash

    Open SourceAPI Available

    Benchmarks & Scores

    Coding (swe-bench-pro)
    49.1%

    complex codebases, multi-file repositories, and architectural planning

    Reasoning (gpqa-diamond)Winner (+0.1%)
    80%

    graduate-level science QA

    Cost & Context

    Cost (per 1M tokens)5.7x cheaper
    $0.17Input: $0.14 | Output: $0.28
    Context WindowLarger
    1.05M tokens
    Model Specs

    GLM-4.5

    Open SourceAPI Available

    Benchmarks & Scores

    Coding (swe-bench-verified)
    64.2%

    multi-file code and clearly defined tasks

    Reasoning (gpqa-diamond)
    79.9%

    graduate-level science QA

    Cost & Context

    Cost (per 1M tokens)
    $1.00Input: $0.60 | Output: $2.20
    Context Window
    131.07k tokens

    Frequently Asked Questions about DeepSeek V4 Flash vs GLM-4.5

    DeepSeek V4 Flash is cheaper than GLM-4.5. DeepSeek V4 Flash has a blended cost of $0.17/1M tokens, which is about 5.7x cheaper than GLM-4.5 at $1.00/1M tokens.

    For coding tasks, DeepSeek V4 Flash scores 49.1% on swe-bench-pro (complex codebases, multi-file repositories, and architectural planning), while GLM-4.5 scores 64.2% on swe-bench-verified (multi-file code and clearly defined tasks).

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