GLM-4.7
by Z.ai (GLM project)
Next-generation open-source coding model with advanced thinking capabilities and MoE architecture
See vendor docs and community benchmarks
Summary
GLM-4.7 is Z.ai’s latest flagship model released December 2025, built specifically for real development workflows. It features enhanced programming capabilities, more stable multi-step reasoning, and introduces three levels of thinking: Interleaved, Preserved, and Turn-level. The model uses a Mixture-of-Experts (MoE) architecture with 200k token context window and 128k token output capacity.
Features
- 200k token context window with 128k max output tokens
- Mixture-of-Experts (MoE) architecture for computational efficiency
- Interleaved Thinking: Automatic reasoning before responses and tool calls
- Preserved Thinking: Retains reasoning across multi-turn conversations in coding agents
- Turn-level Thinking: Per-turn control over reasoning (enable for complex tasks, disable for simple requests)
- Enhanced multilingual coding support with strong terminal-based task performance
- Advanced “Vibe Coding” for UI/webpage generation with cleaner, modern outputs
- Superior tool use capabilities (87.4 on τ²-Bench, highest among open-source models)
Superpowers
GLM-4.7 excels at agentic coding workflows with significant improvements in SWE-bench (73.8%, +5.8% over GLM-4.6), SWE-bench Multilingual (66.7%, +12.9%), and Terminal Bench 2.0 (41%, +16.5%). Integrates seamlessly with Claude Code, Kilo Code, Cline, and Roo Code frameworks. Ranks #1 among open-source models on Code Arena and #6 overall in WebDev benchmarks. Strong mathematical reasoning with 42.8% (+12.4%) on HLE benchmark.
Pricing & access
- Available via Z.ai API Platform
- Open-source weights on HuggingFace and ModelScope
- Supports vLLM and SGLang inference frameworks for local deployment
- Accessible through OpenRouter
Known limitations & notes
- Requires substantial infrastructure for self-hosting (GPU RAM, NVMe for 200k context)
- Turn-level thinking requires manual configuration for optimal latency vs accuracy tradeoff
- Best performance achieved when thinking is enabled for complex tasks
Sources / notes: