Model:
USD estimate $0.0025-$0.005: highest recharge package uses $1 = 2,000 credits; entry package uses $1 = 1,000 credits.
History
Saved locally in this browser
0 running · 0 completed
Input
≈ $0.0019-$0.0037
Output
≈ $0.0063-$0.0125
Cache
≈ $0.0004-$0.0008
Context
Max output
Model overview
Zhipu AI GLM-5.2 is a frontier bilingual model for Chinese and English reasoning, code generation, tool use, and agent workflows.
Evaluate the model against reasoning depth, workflow fit, and production constraints before standardizing a route.
APIAny exposes GLM-5.2 through one documented API surface with live pricing, model status, request examples, and an on-page playground. Teams can evaluate the model here, then keep authentication, usage records, and production routing in the same platform.
Try GLM-5.2 in the playground
Use cases
These workflows show where GLM-5.2 fits in a real product. Test the same inputs in the playground, compare the output with related models, and choose the route that meets your quality, latency, and budget requirements.
01
Use GLM-5.2 for repository analysis, implementation planning, code generation, review, and tool-assisted development. Provide clear constraints and validate generated changes with the same tests used by your engineering team.
Try GLM-5.2 in the playground
02
Build assistants that understand and respond across languages with GLM-5.2. Test terminology, tone, locale-specific formatting, and policy behavior with representative conversations before launch.
Try GLM-5.2 in the playground
03
Use GLM-5.2 to classify tasks, select tools, prepare structured arguments, and summarize results inside a larger workflow. Keep permissions and action validation outside the model boundary.
Try GLM-5.2 in the playground
APIAny advantages
APIAny combines direct model access with the operational controls needed to move from evaluation to production without maintaining a separate integration for every provider.
Call GLM-5.2 with an APIAny key and a documented request format. Keep your application integration stable while model versions and upstream routes evolve behind the gateway.
Review the live GLM-5.2 pricing rules before a request, estimate credits in the playground, and inspect request-level usage records after the call completes.
Use model status, channel health, retry policy, and usage logs to operate GLM-5.2 as part of a production workflow instead of treating it as an isolated demo.
Core capabilities
The following capabilities explain the practical input, output, and workflow characteristics that matter when evaluating GLM-5.2. Available request parameters remain visible in the live API reference on this page.
/01
Use GLM-5.2 to break down multi-step problems, compare alternatives, and produce structured conclusions that can be reviewed.
/02
Apply GLM-5.2 to implementation, debugging, refactoring, repository navigation, and technical explanation with test-based verification.
/03
Use GLM-5.2 for bilingual understanding, generation, coding, and agent tasks that move between Chinese and English context.
/04
Let GLM-5.2 prepare or select tool calls while your application enforces permissions, validates arguments, and records actions.
/05
Render supported GLM-5.2 text output incrementally for interactive assistants and long-running generation tasks.
Integration
Move from a playground test to an authenticated production request in three steps. The page keeps the model identifier, request schema, pricing, and response examples together. Move from a representative prompt to monitored production traffic with explicit quality and fallback checks.
Sign in to APIAny, create a project API key, and assign only the model scope and budget controls your application needs.
Copy the request example from this page, set the model field to the selected GLM-5.2 version, and send it to the documented APIAny endpoint.
Track status, latency, credits, and returned usage. Compare versions or related models with the same workload before shifting production traffic.
FAQ
Direct answers about capabilities, use cases, integration, and choosing the right GLM-5.2 route.
Zhipu AI GLM-5.2 is a frontier bilingual model for Chinese and English reasoning, code generation, tool use, and agent workflows. APIAny makes this model callable through an authenticated API with live pricing, status information, request examples, and a browser playground on the same page.
Common GLM-5.2 workflows include Coding agents and software delivery, Multilingual assistants, Tool-connected workflow automation. The best fit depends on the input format, output quality, latency, and cost your product requires.
Create an APIAny key, use the endpoint documented on this page, and set the request model field to the selected GLM-5.2 version. The playground can generate a working payload before you integrate it into application code.
Compare the versions shown on this page by price, supported parameters, and capabilities such as Complex reasoning, Code generation and analysis, Chinese and English workflows. Test a representative production input before choosing the default route.
Authentication
Every request needs a Bearer token in the Authorization header. Create an API key in the console.
Authorization: Bearer YOUR_API_KEYhttps://apiany.ai/v1/chat/completionsRequest parameters
| Parameter | Type | Required | Notes |
|---|---|---|---|
model | string | Required | Model identifier to invoke. Use this model's ID. |
messages | array | Required | Conversation history in OpenAI chat format (role + content). |
stream | boolean | Optional | When true, the response streams back as server-sent events. |
Request example
curl "https://apiany.ai/v1/chat/completions" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "glm-5.2",
"messages": [
{
"role": "user",
"content": "Summarize APIAny's multi-channel model aggregation in three sentences, and give one production-ready recommendation."
}
],
"temperature": 0.2,
"max_tokens": 400
}'Response example
{
"id": "chatcmpl-abc123",
"object": "chat.completion",
"model": "glm-5.2",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Hello! How can I help?"
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 12,
"completion_tokens": 18,
"total_tokens": 30
}
}
