Agentic-RL Training
Optimized for autonomous workflows, the 5.2 model prevents objective drift during complex, multi-step tasks, ensuring high-fidelity results for coding agents.
glm-5.2 / web-search
The GLM 5.2 API delivers a frontier-class MoE model with a 1M context window. Optimized for autonomous coding and long-horizon tasks via Agentic-RL, it offers Claude-level performance with significant cost savings for developers.
$ 1.26
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$ 3.96
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GLM 5.2 API Key Technical Features
Technical specifications that define the GLM 5.2 API as a leader in open-weight intelligence.
IndexShare MoE Architecture
This innovative architecture reduces KV cache memory overhead by 2.9x, allowing for high-performance inference even when processing massive output sequences.
Selectable Reasoning Modes
Toggle between High and Max reasoning effort to balance speed and depth. Max mode is specifically tuned for architectural design and hard debugging.
1M Token Context Window
Ingest entire monorepos or massive document sets. The GLM 5.2 API maintains high retrieval accuracy across 1,048,576 tokens without performance loss.
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.
GLM 5.2 API Frequently Asked Questions
What makes the GLM 5.2 API unique for developers?
It offers a 1M-token context window with high retrieval accuracy. Unlike many models that struggle with long-form data, GLM 5.2 uses IndexShare architecture to manage memory efficiently, making it perfect for analyzing large codebases or complex legal documents at a fraction of the cost of closed models.
How does reasoning_effort work in the 5.2 model?
The 5.2 model introduces specific settings like High and Max effort. Max mode triggers deeper planning and verification loops, which are essential for tasks like architectural refactoring or security auditing where precision is more important than speed.
Is the GLM 5.2 API compatible with OpenAI SDKs?
Yes. You can easily integrate the GLM 5.2 API by updating your base URL and model name in existing OpenAI-compatible code. It supports tool-calling and structured JSON output, making migration from other frontier models seamless for engineering teams.
What are the licensing terms for the GLM 5.2 model?
The model is released under a permissive MIT license. This allows for unrestricted usage, including local deployment in private enterprise clouds, fine-tuning for specific industry data, and full commercial application without per-seat fees.
How does GLM 5.2 handle autonomous agent drift?
It is trained using Agentic-RL, a reinforcement learning framework specifically designed for long-horizon tasks. This training helps the model stay focused on the primary objective during autonomous loops that exceed 40 turns, reducing common drift issues.
What is the token throughput for the GLM 5.2 API?
The GLM 5.2 API typically delivers a Time to First Token (TTFT) of around 150ms. Once generating, users can expect a throughput of approximately 70 tokens per second, though this can vary slightly based on the selected reasoning effort level.