Selectable Reasoning Effort
Toggle between High and Max reasoning modes to enable deeper planning loops and verification for complex architectural refactoring and debugging.
glm-5.2 / image-to-text
ai glm 5.2 is a frontier-class open-weight MoE model optimized for autonomous coding and agentic tasks. With a 1M token context window and IndexShare architecture, it delivers Claude-level performance for deep repo analysis and logic.
$ 1.26
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$ 3.96
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Core Features of ai glm 5.2
Discover the technical innovations that make ai glm 5.2 a leader in the open-weight MoE space for agentic coding and reasoning.
Efficient IndexShare Design
Reduce KV cache memory overhead by 2.9x. This architecture allows for faster token generation and lower latency during long-form code production.
Permissive MIT License
Deploy the 744B MoE model on your own hardware or private cloud. Enjoy full commercial freedom and data privacy without restrictive licensing.
1M-Token Lossless Context
Ingest entire codebases without performance drops. The model maintains high accuracy across a 1,048,576 token window for deep dependency understanding.
How to Get a glm 5.2 API Key
Getting a glm 5.2 API key takes four steps and a few minutes. Create a free GPTProto account, add credits, generate your key, and make your first call — at $1.26 / $3.96 it's a cheaper glm 5.2 API key than going direct, and one key works across every model on the platform. Full glm 5.2 Documentation is in the docs.
Sign up
Create your free GPT Proto account to begin. You can set up an organization for your team at any time.
Top up
Your balance can be used across all models on the platform, including glm 5.2, giving you the flexibility to experiment and scale as needed.
Generate your API key
In your dashboard, create an API key — you'll need it to authenticate when making requests to glm 5.2.
Make your first API call
Use your API key with our sample code to send a request to glm 5.2 via GPT Proto and see instant AI-powered results.
ai glm 5.2 FAQ: Capabilities & Integration
What is the context window for ai glm 5.2?
The model features a massive 1,048,576 token (1M) context window. Unlike other models that suffer from performance degradation beyond 128k, this architecture maintains high retrieval accuracy across the entire range. This allows developers to ingest entire monorepos or massive document sets in a single prompt without losing critical details, making it a top choice for deep technical analysis and complex repository auditing.
How does ai glm 5.2 handle autonomous coding?
It was trained using a specialized Agentic-RL framework designed for long-horizon tasks. This reduces 'drift' during complex, multi-turn loops. With its 'Max' reasoning effort setting, the model can perform deep planning and verification, ensuring that code generated for architectural refactoring is logically sound and adheres to existing dependency trees across hundreds of files without losing track of the primary goal.
What is the IndexShare architecture in GLM?
IndexShare is a resource-saving innovation that reuses attention indexers across every four layers. This reduces the KV cache memory overhead by approximately 2.9x at maximum context compared to standard Transformer models. For developers, this means faster inference and significantly lower hardware requirements when running the model locally or through high-performance API endpoints, even during 1M token processing.
Is ai glm 5.2 truly open-weight?
Yes, it is released under a permissive MIT License. This allows for unrestricted open-weights usage, including local deployment on private clusters, fine-tuning for specific enterprise needs, and commercial application without per-seat licensing fees. It provides a level of control and privacy that closed-source frontier models simply cannot match, especially for businesses handling sensitive intellectual property.
How does the pricing compare to Claude Opus?
The model is roughly 5 to 8 times more cost-effective. With input prices at $1.40 and output at $4.40 per 1M tokens, it provides a massive pricing advantage for high-volume engineering tasks. Furthermore, users can access up to 80% discounts on context caching for repetitive prefixes and 50% discounts for non-urgent batch processing, making it highly scalable for startups and large enterprises alike.
Does the model support JSON mode and tool use?
Absolutely. It features native support for function calling and structured outputs via the response_format parameter. Built with an agent-first architecture, it handles tool-use seamlessly, allowing it to interact with external environments and APIs reliably. This makes it a robust backbone for AI agents like Cursor or custom internal developer tools that require structured data and predictable responses.