GLM 5.2
GLM-5.2 is Z.ai's (Zhipu) flagship model for project-scale coding and long-horizon engineering, with a 1M-token context and 128K output. Access it through Velokey's unified endpoint with below-list pricing.
Use cases
Codebase analysis & refactoring · Long-horizon engineering
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Output
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Billing
Per token
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Pricing details
Transparent usage-based pricing with no hidden fees.
Detailed Pricing
Usage-based pricing breakdown
| Spec | Price | Official | Savings |
|---|---|---|---|
| Input/1M tokens | 1.12 Credits≈$1.12 | 1.4 Credits | -20% |
| Output/1M tokens | 3.52 Credits≈$3.52 | 4.4 Credits | -20% |
| Cache Read/1M tokens | 0.208 Credits≈$0.208 | 0.26 Credits | -20% |
Billing Rules
- Output price varies by model, resolution, quality, duration, or token usage.
- Uploaded reference files may be charged separately when supported.
- Add-on capabilities such as web search or image search may be billed per request.
- Token-based models are billed by input and output tokens.
- If the primary route is unavailable, Velokey may automatically switch to a stable fallback route when possible.
- Usage is pay-as-you-go with transparent credit deduction.
* Final cost depends on the generated result.
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Affordable GLM-5.2 API
Access Z.ai's GLM-5.2 on Velokey — a 1M-token context, top-tier agentic coding, adjustable thinking modes, and full tool support, with a unified endpoint and below-list pricing for production apps.
Meet GLM-5.2 and its API access
GLM-5.2 is Z.ai's (Zhipu) flagship foundation model, built for project-scale coding and long-horizon engineering. It ships with a 1M-token context window and up to 128K tokens of output, adds multiple thinking modes controlled by a reasoning_effort parameter, and pushes agentic coding to the front tier — Terminal-Bench 2.1 rises to 81.0 from 62.0 and SWE-bench Pro to 62.1 from 58.4. It brings stronger project-level codebase comprehension, more stable multi-step execution, better adherence to engineering standards, and enhanced mobile and client-side capabilities. Velokey provides an affordable, easy-to-integrate GLM-5.2 API: a unified chat endpoint, a clear request flow, and below-list pricing for production systems, agent pipelines, and automation tools.
1M-token context & project-level comprehension
A 1M-token context window lets GLM-5.2 read an entire project-level codebase at once, connecting distant modules to reason coherently about architecture, dependencies, and cross-file changes.
View Documentation →
Top-tier agentic coding
GLM-5.2 pushes agentic coding to the front tier: Terminal-Bench 2.1 climbs to 81.0 from 62.0 and SWE-bench Pro to 62.1 from 58.4, running coding tools reliably across multi-step tasks.
View Documentation →
Stable long-horizon engineering
More stable multi-step task execution keeps long engineering plans — module decoupling, API migration, refactors — on track, with better adherence to production and engineering standards.
View Documentation →
Adjustable thinking & tool integration
Multiple thinking modes with a reasoning_effort parameter let you dial depth up or down, while native function calling, streaming, structured JSON output, and MCP integration make it agent-ready.
View Documentation →
How to deploy the GLM-5.2 API on Velokey
Get started with just a few steps.
Sign up and get an API Key
Sign in to the Velokey console to generate an API Key that authenticates every request sent to GLM-5.2.
Configure request parameters
Set model to glm-5-2 and submit messages. Use the reasoning_effort parameter to select a thinking mode, and enable function calling, structured output, or MCP as needed.
Integrate and start calling
Send requests to Velokey's unified /v1/chat/completions endpoint to power coding agents, engineering automation, and long-horizon tasks, with usage managed in one console.
Real-world use cases for GLM-5.2
From project-scale coding to mobile and client-side work, covering many high-autonomy scenarios.
Codebase analysis & refactoring
With a 1M-token context and stronger project comprehension, fit for analyzing and refactoring an entire codebase, including large cross-file changes.
Long-horizon engineering
Stable multi-step execution suits module decoupling, API migration, and other long-running engineering tasks that must stay on plan.
Production-standard enforcement
Better adherence to engineering standards fits enforcing conventions, review rules, and production-grade quality across a project.
Mobile & mini program development
Enhanced client-side capabilities fit Android and WeChat Mini Program development and debugging, plus mini game development.
Research paper reproduction
Strong reasoning and long context fit reproducing research papers, following methods, and rebuilding experiments from source material.
Code-to-video generation
Reliable code generation fits producing programmatic videos with Remotion and other code-to-video workflows.
Why choose Velokey for GLM-5.2
Discount on list price
Call GLM-5.2 at a lower price on Velokey — pay-as-you-go with no forced subscription, reducing upfront cost. The current price is shown in your dashboard.
Unified API interface
One API Key accesses GLM-5.2 and other models — no juggling multiple accounts and keys, simplifying integration.
Complete docs and migration guidance
Endpoint references, parameter details, and migration examples help you upgrade smoothly from GLM-5.1 and adopt long context, thinking modes, and tools.
Stable and highly available
Automatic failover and stable fallback routes maintain availability when the primary route is down, keeping production reliable.
API Reference
Complete API call examples and parameter descriptions
Endpoint
https://api.velokey.ai/v1/chat/completionsAuthentication
Bearer YOUR_API_KEYRequest Example
curl https://api.velokey.ai/v1/chat/completions \
-X POST \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "glm-5.2",
"messages": [{"role": "user", "content": "Hello!"}],
"temperature": 0.7
}'Response Example
{
"id": "req_abc123",
"model": "glm-5.2",
"created": 1234567890,
"data": { ... }
}GLM-5.2 API FAQ
What's improved over GLM-5.1?
GLM-5.2 delivers stronger project-level codebase comprehension, more stable multi-step task execution, and better adherence to engineering standards. On benchmarks, Terminal-Bench 2.1 rises to 81.0 (from 62.0) and SWE-bench Pro to 62.1 (from 58.4), alongside enhanced mobile and client-side capabilities.
How large is the context window and output?
GLM-5.2 ships with a 1M-token context window and can produce up to 128K tokens of output, so it can comprehend an entire project-level codebase at once and still generate large refactors or long responses. Context caching helps keep long conversations efficient.
How do the thinking modes and reasoning_effort work?
GLM-5.2 supports multiple thinking modes controlled by a reasoning_effort parameter (for example "max"), letting you dial reasoning depth up for hard problems or down for lower latency on simpler requests.
What tool and integration capabilities are available?
GLM-5.2 supports function calling and tool invocation, streaming responses, structured JSON output, and MCP integration, plus context caching for long conversations — everything needed to build real agent pipelines.
How is it billed on Velokey?
You call GLM-5.2 through one unified endpoint and API key at below-list pricing. This page's pricing area shows the current Velokey price in the dashboard, billed by input and output tokens with a discount on list price.
Start building with GLM 5.2 today
Use one API key to access low-cost, stable AI models through Velokey.