GLM-4.7 is a Mixture of Experts foundation model designed to unify reasoning, coding, and agentic capabilities within a single architecture. With hundreds of billions of total parameters but a much smaller active parameter count during inference, the model balances computational efficiency with strong performance across demanding tasks. It targets developers and enterprises working in production environments where tasks span long cycles, require frequent tool interactions, and demand consistent behavior across multiple steps. The architecture supports thinking before acting, enabling the model to deliberate on complex problems rather than immediately responding, which proves valuable for terminal-based tasks, multilingual coding scenarios, and framework integrations like Claude Code, Kilo Code, Cline, and Roo Code.

The model builds on its predecessor with targeted improvements in agentic coding, mathematical reasoning, and tool use. Benchmark gains are substantial: double-digit percentage improvements on SWE-bench for software engineering tasks, Terminal Bench for command-line workflows, and the HLE benchmark for advanced mathematical reasoning. GLM-4.7 also introduces "Vibe Coding" capabilities, producing cleaner, more modern webpage layouts and better-formatted slides with improved sizing accuracy. Tool calling follows the OpenAI-style format, and the model demonstrates significantly better performance on τ²-Bench and web browsing benchmarks like BrowseComp. As an open-weight model, it serves developers seeking a production-ready foundation that can handle intelligent agent applications while remaining accessible for customization and self-hosting through frameworks like vLLM and SGLang.