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glm-4.7-flash

GLM-4.7-Flash is a 30B-class model engineered specifically to bridge the gap between lightweight inference and high-reasoning performance. Unlike general-purpose small models, this iteration is fine-tuned for agentic workflows, prioritizing long-horizon task planning and complex coding logic. For developers building autonomous agents or integrated IDE tools, it offers a high-density intelligence profile that minimizes latency without sacrificing the structural accuracy required for code generation. With a massive 200k context window, it handles large-scale repository analysis and multi-step instruction sets effectively. It serves as a strategic alternative to larger frontier models when your deployment requires a balance of rapid token throughput and sophisticated reasoning capabilities for specialized developer tools.

z-aitext generation
01 / MODEL CARD

Model card

GLM-4.7-Flash is a 30B-class model engineered specifically to bridge the gap between lightweight inference and high-reasoning performance. Unlike general-purpose small models, this iteration is fine-tuned for agentic workflows, prioritizing long-horizon task planning and complex coding logic. For developers building autonomous agents or integrated IDE tools, it offers a high-density intelligence profile that minimizes latency without sacrificing the structural accuracy required for code generation. With a massive 200k context window, it handles large-scale repository analysis and multi-step instruction sets effectively. It serves as a strategic alternative to larger frontier models when your deployment requires a balance of rapid token throughput and sophisticated reasoning capabilities for specialized developer tools.

Model typetext generation
Providerz-ai
LicenseAPI
02 / FILES & VERSIONS

Model files and versions

Model cardModel description and metadata available in this entry
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Source repositoryhttps://openrouter.ai/z-ai/glm-4.7-flash
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03 / DOWNLOAD

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04 / WORKFLOW

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  1. 01
    Step 1

    Read the model card and source information.

  2. 02
    Step 2

    Start with a small, non-sensitive evaluation.

  3. 03
    Step 3

    Review quality, licensing and usage limits.

  4. 04
    Step 4

    Adopt it only after validation.

05 / DISCUSSIONS

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