GLM-4.7 is a state-of-the-art open-weight model optimized for coding and reasoning with significant improvements over its predecessor. It features Preserved Thinking to maintain reasoning context across multi-turn agentic tasks and supports thinking before acting.
GLM-4.7 is an advanced open-weight large language model specifically engineered as a coding and reasoning partner, representing a substantial evolution from the previous GLM-4.6 iteration. Developed by Z.ai, this model targets developers, engineers, and technical professionals who require robust AI assistance for software development, complex problem-solving, and creative technical tasks. Its core value lies in delivering measurable performance gains across coding benchmarks while introducing sophisticated reasoning mechanisms that enhance stability and control in multi-turn agentic workflows. The model's open-weight nature allows for both cloud-based API access and local deployment, providing flexibility for different implementation scenarios while maintaining competitive performance against leading proprietary models.
GLM-4.7 directly addresses the challenge of maintaining consistent reasoning context across extended coding sessions and complex technical tasks. Traditional AI coding assistants often struggle with information loss and reasoning inconsistencies when handling long-horizon projects that require multiple interactions. This becomes particularly problematic in agentic coding scenarios where the model must execute terminal commands, navigate codebases, and implement solutions across numerous steps. The pain point of re-deriving reasoning from scratch with each interaction leads to inefficiencies, errors, and frustration for developers who need reliable, context-aware assistance throughout their development workflow. By solving this, GLM-4.7 enables more productive and predictable AI collaboration in software engineering.
The model introduces Preserved Thinking as a fundamental feature group that maintains reasoning context across multi-turn agentic tasks. This mechanism automatically retains all thinking blocks throughout extended conversations, allowing the model to reuse existing reasoning rather than re-deriving solutions from scratch with each interaction. This approach significantly reduces information loss and inconsistencies that typically plague long-horizon coding projects. The preservation occurs automatically in coding agent scenarios, making it particularly valuable for complex software engineering tasks that unfold across multiple steps. This feature directly contributes to the model's improved performance on benchmarks like SWE-bench and Terminal Bench 2.0, where maintaining context is crucial for success.
Another major capability is Interleaved Thinking combined with Turn-level Thinking control, which represents the model's enhanced reasoning architecture. Interleaved Thinking means GLM-4.7 thinks before every response and tool calling, improving instruction following and generation quality. This systematic reasoning approach ensures more deliberate and accurate outputs. Complementing this, Turn-level Thinking provides per-session control over reasoning depth—users can disable thinking for lightweight requests to reduce latency and cost, while enabling it for complex tasks to improve accuracy and stability. This flexible control mechanism allows developers to optimize the model's behavior based on specific task requirements, balancing performance with efficiency in practical usage scenarios.
Publisher
A
admin
Launch Date2025-12-26
Platformweb
Pricingfree
Domain Authority
Ahrefs DR78/100