SINGAPORE - 26 December 2025 - Z.ai has released GLM-4.7, the latest version of its open-source large language model, ahead of Christmas, as the company steps up efforts to position its models for real-world software development and production use.

The new model is designed around practical engineering workflows, with a focus on long-running task execution, stable tool calling, and multi-step reasoning, capabilities that have become increasingly important as developers deploy large language models in complex, agent-based systems.

Compared with its predecessor, GLM-4.6, GLM-4.7 shows notable gains in code generation, complex reasoning, and agent execution. According to Z.ai, the model delivers more consistent and controllable performance over extended tasks, while producing cleaner and more concise language output, addressing a common weakness in many open-source models.

To evaluate performance in realistic settings, Z.ai tested GLM-4.7 on 100 practical programming tasks in production-like environments such as Claude Code, spanning front-end, back-end, and command-execution scenarios. The company said GLM-4.7 achieved higher task completion rates and greater stability than GLM-4.6, and has since been adopted as the default model for its GLM Coding Plan.

Benchmark results also place GLM-4.7 among the strongest open-source models currently available. It scored 67.5 on BrowseComp and 87.4 on τ²-Bench, the latter marking a new high for open-source systems. In coding-focused evaluations, including SWE-bench Verified and LiveCodeBench v6, its overall performance approaches that of Claude Sonnet 4.5. In Code Arena's large-scale blind evaluation, which aggregates votes from more than one million comparisons, GLM-4.7 ranked first among open-source models.

The model is available through the BigModel.cn API and has been integrated into Z.ai's full-stack development platform, according to the company. As open-source models take on a more prominent role in the global technology ecosystem, Z.ai's progress offers a clear indication of how such systems may continue to evolve, and what they might enable next.

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