> ## Documentation Index > Fetch the complete documentation index at: https://evolink.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # GLM-5.2 - OpenAI Compatible API > - Use the OpenAI Chat Completions protocol to call the GLM-5.2 model - Synchronous processing mode, returning conversation content in real time - **Plain-text conversation**: Single-turn or multi-turn contextual dialogue - **System prompts**: Customize the AI's role and behavior via `role=system` messages - **Deep thinking**: Toggle the chain of thought via `thinking.type`, and adjust reasoning intensity with `reasoning_effort`; the reasoning process is returned via `reasoning_content` - **Streaming output**: Supports SSE streaming responses (`stream=true`) - **Tool calling**: Supports Function Calling, knowledge base retrieval (retrieval), web search (web_search), and MCP (up to 128 tools) - **Structured output**: Enable JSON mode via `response_format` **Streaming response notes**: When `stream=true`, responses are returned via Server-Sent Events, with each message formatted as `data: {JSON}`, and `data: [DONE]` returned at the end. Each data chunk (`ChatCompletionChunk`) contains `id`, `created`, `model`, `choices`, and optional `usage` and `content_filter`; within it, `choices[].delta` incrementally returns `role` / `content` / `reasoning_content` / `tool_calls`, and `choices[].finish_reason` provides the termination reason in the final chunk. **BaseURL**: The default BaseURL is `https://direct.evolink.ai`, which has better support for text models and long-lived connections. `https://api.evolink.ai` is the primary endpoint for multimodal services and serves as a fallback address for text models. ## OpenAPI ````yaml en/api-manual/language-series/glm-5.2/glm-5.2-api.json POST /v1/chat/completions openapi: 3.1.0 info: title: GLM-5.2 Complete API Reference (OpenAI-Compatible) description: >- Complete API reference for the GLM-5.2 chat interface. **Model Capabilities**: - Latest flagship model, featuring complex reasoning, ultra-long context, and exceptional inference speed - Maximum output: **131,072 tokens** (128K), recommended no less than **1,024 tokens** - Deep thinking: Toggle the chain of thought via the `thinking` field, and adjust reasoning intensity with `reasoning_effort` (exclusive to GLM-5.2) - Tool calling: Supports Function Calling, knowledge base retrieval, web search, and MCP (up to 128 tools) - Streaming output: Supports SSE streaming responses - Structured output: Supports both `text` and `json_object` response formats license: name: MIT version: 1.0.0 servers: - url: https://direct.evolink.ai description: Production (Recommended) - url: https://api.evolink.ai description: Alternative URL security: - bearerAuth: [] tags: - name: Chat Completion description: AI chat generation related interfaces paths: /v1/chat/completions: post: tags: - Chat Completion summary: GLM-5.2 Chat Interface (OpenAI-Compatible) description: >- - Use the OpenAI Chat Completions protocol to call the GLM-5.2 model - Synchronous processing mode, returning conversation content in real time - **Plain-text conversation**: Single-turn or multi-turn contextual dialogue - **System prompts**: Customize the AI's role and behavior via `role=system` messages - **Deep thinking**: Toggle the chain of thought via `thinking.type`, and adjust reasoning intensity with `reasoning_effort`; the reasoning process is returned via `reasoning_content` - **Streaming output**: Supports SSE streaming responses (`stream=true`) - **Tool calling**: Supports Function Calling, knowledge base retrieval (retrieval), web search (web_search), and MCP (up to 128 tools) - **Structured output**: Enable JSON mode via `response_format` **Streaming response notes**: When `stream=true`, responses are returned via Server-Sent Events, with each message formatted as `data: {JSON}`, and `data: [DONE]` returned at the end. Each data chunk (`ChatCompletionChunk`) contains `id`, `created`, `model`, `choices`, and optional `usage` and `content_filter`; within it, `choices[].delta` incrementally returns `role` / `content` / `reasoning_content` / `tool_calls`, and `choices[].finish_reason` provides the termination reason in the final chunk. operationId: createChatCompletionGLM52 requestBody: required: true content: application/json: schema: $ref: '#/components/schemas/ChatCompletionRequest' examples: simple_text: summary: Single-turn text conversation value: model: glm-5.2 messages: - role: user content: Please introduce yourself multi_turn: summary: Multi-turn conversation (contextual understanding) value: model: glm-5.2 messages: - role: user content: What is Python? - role: assistant content: Python is a high-level programming language... - role: user content: What are its advantages? system_prompt: summary: Using a system prompt value: model: glm-5.2 messages: - role: system content: >- You are a professional Python programming assistant. Answer questions in concise language. - role: user content: How do I read a file? deep_thinking: summary: Enable deep thinking and adjust reasoning intensity value: model: glm-5.2 messages: - role: user content: >- A farmer needs to take a wolf, a sheep, and a cabbage across a river, but can only carry one at a time. How can he get them across safely? thinking: type: enabled reasoning_effort: max disable_thinking: summary: Disable deep thinking (answer directly) value: model: glm-5.2 messages: - role: user content: Summarize the theory of relativity in one sentence. thinking: type: disabled function_calling: summary: Tool calling (Function Calling) value: model: glm-5.2 messages: - role: user content: What's the weather like in Beijing today? tools: - type: function function: name: get_weather description: Query the real-time weather of a specified city parameters: type: object properties: city: type: string description: 'City name, for example: Beijing' required: - city tool_choice: auto web_search: summary: Enable the web search tool value: model: glm-5.2 messages: - role: user content: >- Help me find the latest artificial intelligence news from the past week tools: - type: web_search web_search: enable: true search_engine: search_pro count: 10 search_recency_filter: oneWeek json_mode: summary: JSON structured output value: model: glm-5.2 messages: - role: system content: >- Please output in JSON format, including the two fields name and age. - role: user content: John, 28 years old this year response_format: type: json_object streaming: summary: Streaming output (SSE) value: model: glm-5.2 messages: - role: user content: Write a short poem about spring stream: true responses: '200': description: Conversation generated successfully content: application/json: schema: $ref: '#/components/schemas/ChatCompletionResponse' '400': description: Invalid request parameters content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' example: error: code: 400 message: Invalid request parameters type: invalid_request_error '401': description: Unauthenticated, invalid or expired token content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' example: error: code: 401 message: Invalid or expired token type: authentication_error '402': description: Insufficient quota, top-up required content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' example: error: code: 402 message: Insufficient quota type: insufficient_quota_error fallback_suggestion: https://evolink.ai/dashboard/billing '403': description: Access denied content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' example: error: code: 403 message: Access denied for this model type: permission_error param: model '404': description: Resource not found content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' example: error: code: 404 message: Specified model not found type: not_found_error param: model fallback_suggestion: glm-5.2 '429': description: Rate limit exceeded content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' example: error: code: 429 message: Rate limit exceeded type: rate_limit_error fallback_suggestion: retry after 60 seconds '500': description: Internal server error content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' example: error: code: 500 message: Internal server error type: internal_server_error fallback_suggestion: try again later '502': description: Upstream service error content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' example: error: code: 502 message: Upstream AI service unavailable type: upstream_error fallback_suggestion: try different model '503': description: Service temporarily unavailable content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' example: error: code: 503 message: Service temporarily unavailable type: service_unavailable_error fallback_suggestion: retry after 30 seconds components: schemas: ChatCompletionRequest: type: object required: - model - messages properties: model: type: string description: >- The model code to call - `glm-5.2`: Latest flagship model, offering complex reasoning, ultra-long context, and exceptional inference speed enum: - glm-5.2 default: glm-5.2 example: glm-5.2 messages: type: array description: >- The list of conversation messages, containing the full context of the current conversation Supports four roles: `system`, `user`, `assistant`, `tool`. Messages of different roles have different field structures; please select the corresponding role to view details. Must contain at least 1 message, and cannot consist solely of system or assistant messages. items: oneOf: - $ref: '#/components/schemas/SystemMessage' - $ref: '#/components/schemas/UserMessage' - $ref: '#/components/schemas/AssistantRequestMessage' - $ref: '#/components/schemas/ToolMessage' discriminator: propertyName: role mapping: system: $ref: '#/components/schemas/SystemMessage' user: $ref: '#/components/schemas/UserMessage' assistant: $ref: '#/components/schemas/AssistantRequestMessage' tool: $ref: '#/components/schemas/ToolMessage' minItems: 1 stream: type: boolean description: >- Whether to enable streaming output mode - `false`: The model generates the complete response and returns it all at once (default), suitable for short text and batch processing - `true`: Returns chunks in real time via Server-Sent Events (SSE), suitable for chat and long text; returns `data: [DONE]` when the stream ends default: false example: false thinking: type: object description: Controls whether to enable the chain of thought (Chain of Thought) properties: type: type: string description: |- Chain-of-thought toggle - `enabled`: Enable deep thinking (default) - `disabled`: Disable deep thinking; the model answers directly enum: - enabled - disabled default: enabled clear_thinking: type: boolean description: >- Whether to clear the `reasoning_content` from previous conversation turns - `true` (default): Ignores/removes `reasoning_content` from previous turns, using only non-reasoning content (user/assistant-visible text, tool calls and results, etc.) as context, which can reduce context length and cost - `false`: Retains `reasoning_content` from previous turns and provides it to the model along with the context (Preserved Thinking); in this case, the historical `reasoning_content` must be passed through in `messages` **completely, unmodified, and in the original order**—missing, truncating, rewriting, or reordering it will degrade results or prevent it from taking effect - Note: This parameter only affects historical thinking across turns; it does not change whether thinking is produced in the current turn default: true example: true reasoning_effort: type: string description: >- Controls the model's reasoning intensity (exclusive to GLM-5.2) **Notes**: - Only takes effect when `thinking` is enabled; defaults to `max` - Values from strongest to weakest: `max` > `xhigh` > `high` > `medium` > `low` > `minimal` > `none` **GLM-5.2 mapping rules** (for compatibility with other protocols): - `xhigh` → equivalent to `max` - `low` / `medium` → equivalent to `high` - `none` / `minimal` → skip thinking (no deep reasoning) enum: - max - xhigh - high - medium - low - minimal - none default: max example: max do_sample: type: boolean description: >- Whether to enable the sampling strategy - `true` (default): Uses `temperature` / `top_p` for random sampling, producing more varied output - `false`: Always selects the highest-probability token (greedy decoding), producing more deterministic output; in this case `temperature` and `top_p` are ignored For tasks requiring consistency and reproducibility (such as code generation and translation), setting this to `false` is recommended default: true example: true temperature: type: number format: float description: >- Sampling temperature, controlling the randomness and creativity of the output **Notes**: - Value range: `[0.0, 1.0]`, limited to two decimal places - Higher values (e.g. 0.8): more random and creative, suitable for creative writing - Lower values (e.g. 0.2): more stable and deterministic, suitable for factual Q&A and code generation - GLM-5.2 default value: `1.0` **Recommendation**: Do not adjust both `temperature` and `top_p` at the same time minimum: 0 maximum: 1 default: 1 example: 1 top_p: type: number format: float description: >- Nucleus Sampling parameter, an alternative to `temperature` sampling **Notes**: - Value range: `[0.01, 1.0]`, limited to two decimal places - The model only considers candidate tokens whose cumulative probability reaches `top_p`; for example, 0.1 means only the top 10% probability tokens are considered - Smaller values produce more focused and consistent output; larger values increase diversity - GLM-5.2 default value: `0.95` **Recommendation**: Do not adjust both `temperature` and `top_p` at the same time minimum: 0.01 maximum: 1 default: 0.95 example: 0.95 max_tokens: type: integer description: >- The maximum number of tokens limit for the model's output **Notes**: - GLM-5.2 supports up to **131,072 tokens** (128K) of output length; setting no less than `1024` is recommended - When `thinking` is enabled, chain-of-thought tokens are also counted toward this limit - If generation is truncated due to `length`, try increasing this value minimum: 1 maximum: 131072 example: 1024 tools: type: array description: >- The list of tools the model can call **Notes**: - Supports function calling (`function`), knowledge base retrieval (`retrieval`), web search (`web_search`), and MCP (`mcp`) - Supports up to 128 functions items: oneOf: - $ref: '#/components/schemas/FunctionTool' - $ref: '#/components/schemas/RetrievalTool' - $ref: '#/components/schemas/WebSearchTool' - $ref: '#/components/schemas/McpTool' discriminator: propertyName: type mapping: function: $ref: '#/components/schemas/FunctionTool' retrieval: $ref: '#/components/schemas/RetrievalTool' web_search: $ref: '#/components/schemas/WebSearchTool' mcp: $ref: '#/components/schemas/McpTool' maxItems: 128 tool_choice: type: string description: >- Controls how the model selects which function to call **Notes**: Only takes effect when the tool type is `function`; defaults to and only supports `auto` (the model automatically decides whether to call a tool) enum: - auto default: auto example: auto stop: type: array description: >- The list of stop words **Notes**: - When the generated text encounters a specified string, generation stops immediately (the stop word itself is not included in the returned text) - Currently only a single stop word is supported, in the format `["stop_word1"]`, for example `["Human:"]` items: type: string maxItems: 4 example: - 'Human:' response_format: type: object description: >- Specifies the model's response output format; defaults to `text` **Notes**: - `{ "type": "json_object" }` enables JSON mode, and the model returns valid JSON-formatted data, suitable for scenarios such as structured data extraction - When using JSON mode, it is recommended to explicitly request JSON output in the `system` or `user` message required: - type properties: type: type: string description: |- Output format type - `text`: Plain text output (default) - `json_object`: JSON-formatted output enum: - text - json_object default: text request_id: type: string description: >- Unique request identifier **Notes**: - Passed by the client, 6-64 characters long; using UUID format is recommended to ensure uniqueness - If not provided, the platform will generate one automatically minLength: 6 maxLength: 64 example: req-7f3a2c1e8b9d4f0a user_id: type: string description: >- Unique identifier of the end user **Notes**: 6-128 characters long; using a unique identifier that does not contain sensitive information is recommended, which can help the platform monitor and detect abusive behavior minLength: 6 maxLength: 128 example: user-abc123456 ChatCompletionResponse: type: object properties: id: type: string description: Task `ID` example: chatcmpl-a6613b56-c61c-94ba-9a9f-43d4cdc7d77a object: type: string description: Response type enum: - chat.completion example: chat.completion request_id: type: string description: Request `ID` (returned when `request_id` is provided in the request) example: req-7f3a2c1e8b9d4f0a created: type: integer description: Request creation time, `Unix` timestamp (seconds) example: 1777021417 model: type: string description: Model name example: glm-5.2 choices: type: array description: The list of model responses items: $ref: '#/components/schemas/Choice' usage: $ref: '#/components/schemas/Usage' web_search: type: array description: >- Web search-related information, returned when the `web_search` tool is used and a search is triggered items: $ref: '#/components/schemas/WebSearchResult' content_filter: type: array description: Content safety-related information items: $ref: '#/components/schemas/ContentFilter' ErrorResponse: type: object properties: error: type: object properties: code: type: integer description: HTTP status error code message: type: string description: Error description message type: type: string description: Error type param: type: string description: Related parameter name fallback_suggestion: type: string description: Suggestion when an error occurs SystemMessage: title: System Message type: object required: - role - content properties: role: type: string enum: - system description: Role identifier, fixed as `system` content: type: string description: System prompt content, used to set the AI's role and behavior UserMessage: title: User Message type: object required: - role - content properties: role: type: string enum: - user description: Role identifier, fixed as `user` content: type: string description: User message content (plain text string) AssistantRequestMessage: title: Assistant Message type: object description: Assistant message, may contain tool calls required: - role properties: role: type: string enum: - assistant description: Role identifier, fixed as `assistant` content: type: - string - 'null' description: >- Assistant message content **Notes**: Used to pass historical assistant replies in multi-turn conversations; typically `null` when `tool_calls` is present reasoning_content: type: - string - 'null' description: >- Historical chain-of-thought content **Notes**: Required only when `thinking.clear_thinking=false` (Preserved Thinking); pass back the `reasoning_content` from the previous response as-is; by default (`clear_thinking=true`) no passback is needed tool_calls: type: array description: >- The list of tool calls Used to pass historical tool call information in multi-turn conversations; when this field is provided, `content` is typically empty items: type: object required: - id - type properties: id: type: string description: Tool call ID type: type: string enum: - function - web_search - retrieval description: Tool type function: type: object description: Function call information, not empty when `type` is `function` required: - name - arguments properties: name: type: string description: Function name arguments: type: string description: Function arguments (JSON-formatted string) ToolMessage: title: Tool Message type: object required: - role - content properties: role: type: string enum: - tool description: Role identifier, fixed as `tool` content: type: string description: The content of the tool call's returned result tool_call_id: type: string description: >- Indicates the tool call `ID` this message corresponds to (matching the `id` returned in the assistant message's `tool_calls`) FunctionTool: title: Function Tool type: object required: - type - function additionalProperties: false properties: type: type: string enum: - function default: function description: Tool type, fixed as `function` function: type: object required: - name - description - parameters properties: name: type: string description: >- The name of the function to call **Notes**: Must consist of the characters `a-z`, `A-Z`, `0-9`, or contain underscores and dashes; maximum length 64 characters minLength: 1 maxLength: 64 pattern: ^[a-zA-Z0-9_-]+$ description: type: string description: >- A description of the function's capability, for the model to choose when and how to call the function parameters: type: object description: >- The function's input parameters, described as a JSON Schema object RetrievalTool: title: Retrieval Tool (Knowledge Base Retrieval) type: object required: - type - retrieval additionalProperties: false properties: type: type: string enum: - retrieval default: retrieval description: Tool type, fixed as `retrieval` retrieval: type: object required: - knowledge_id properties: knowledge_id: type: string description: Knowledge base `ID`, created or obtained from the platform prompt_template: type: string description: >- The prompt template used to request the model, a custom template containing the placeholders `{{ knowledge }}` and `{{ question }}`; the default template is used when not provided WebSearchTool: title: Web Search Tool (Web Search) type: object required: - type - web_search additionalProperties: false properties: type: type: string enum: - web_search default: web_search description: Tool type, fixed as `web_search` web_search: type: object required: - search_engine properties: enable: type: boolean description: Whether to enable the search feature; set to `true` to enable default: false search_engine: type: string description: Search engine type, defaults to `search_std` enum: - search_std - search_pro - search_pro_sogou - search_pro_quark search_query: type: string description: Custom keyword that forcibly triggers a search search_intent: type: string description: >- Whether to perform search intent recognition; performed by default - `true`: Perform search intent recognition, and run the search once a search intent is detected - `false`: Skip intent recognition and run the search directly count: type: integer description: >- The number of results to return, ranging `1-50`, defaults to `10` (supported by `search_std` / `search_pro` / `search_pro_sogou`) minimum: 1 maximum: 50 default: 10 search_domain_filter: type: string description: >- Domain allowlist that limits the search results (e.g. `www.example.com`) search_recency_filter: type: string description: >- Limits the time range of the search results, defaults to `noLimit` enum: - oneDay - oneWeek - oneMonth - oneYear - noLimit default: noLimit content_size: type: string description: >- Controls the word count of web page summaries, defaults to `medium` - `medium`: Returns summary information, meeting basic reasoning needs - `high`: Maximizes context with more detailed information enum: - medium - high default: medium result_sequence: type: string description: >- The position where search results are returned (before or after the model's reply), defaults to `after` enum: - before - after default: after search_result: type: boolean description: >- Whether to return detailed information about the search sources, defaults to `false` default: false require_search: type: boolean description: >- Whether to require that the answer be returned based on search results, defaults to `false` default: false search_prompt: type: string description: >- The `Prompt` used to customize the processing of search results; the default template is used when not provided McpTool: title: MCP Tool type: object required: - type - mcp additionalProperties: false properties: type: type: string enum: - mcp default: mcp description: Tool type, fixed as `mcp` mcp: type: object required: - server_label properties: server_label: type: string description: >- MCP server label; when connecting to a built-in MCP server on the platform, fill in the corresponding mcp code, and there is no need to fill in `server_url` server_url: type: string description: MCP server address transport_type: type: string description: Transport type enum: - sse - streamable-http default: streamable-http allowed_tools: type: array description: The set of tools allowed to be called items: type: string headers: type: object description: The authentication information required by the MCP server Choice: type: object properties: index: type: integer description: Result index example: 0 message: $ref: '#/components/schemas/AssistantMessage' finish_reason: type: string description: >- The reason inference terminated - `stop`: Natural completion or a stop word was triggered - `tool_calls`: The model triggered a function (tool call) - `length`: Reached the token length limit - `sensitive`: Content was blocked by safety review (please assess and decide whether to retract public content) - `network_error`: Model inference error - `model_context_window_exceeded`: Exceeded the model's context window enum: - stop - tool_calls - length - sensitive - network_error - model_context_window_exceeded example: stop Usage: type: object description: Token usage statistics returned when the call ends properties: prompt_tokens: type: integer description: The number of tokens in the user input example: 24 completion_tokens: type: integer description: >- The number of output tokens (including the chain-of-thought `reasoning_tokens` portion) example: 346 total_tokens: type: integer description: Total token count = prompt_tokens + completion_tokens example: 370 prompt_tokens_details: type: object description: Detailed breakdown of input tokens properties: cached_tokens: type: integer description: The number of tokens that hit the cache example: 0 completion_tokens_details: type: object description: Detailed breakdown of output tokens properties: reasoning_tokens: type: integer description: >- The number of tokens produced by the chain of thought (deep thinking), counted toward `completion_tokens` example: 321 WebSearchResult: type: object description: A single web search result properties: icon: type: string description: The icon of the source website title: type: string description: The title of the search result link: type: string description: The web page link of the search result media: type: string description: The media source name of the search result web page publish_date: type: string description: The website's publish time content: type: string description: The cited text content of the search result web page refer: type: string description: Superscript number ContentFilter: type: object description: Content safety information properties: role: type: string description: |- The stage where safety takes effect - `assistant`: Model inference - `user`: User input - `history`: Historical context enum: - assistant - user - history level: type: integer description: >- Severity level `0-3`, where `0` indicates the most severe and `3` indicates minor minimum: 0 maximum: 3 AssistantMessage: type: object properties: role: type: string description: The current conversation role, defaults to `assistant` enum: - assistant example: assistant content: type: - string - 'null' description: >- Conversation text content **Notes**: May be `null` when calling tools (`tool_calls`); otherwise returns the model's reply content example: >- Hello! I'm GLM-5.2, and I can help you with a variety of tasks such as conversation, reasoning, writing, and code. reasoning_content: type: string description: >- Chain-of-thought content **Notes**: Returned when `thinking` is enabled, recording the model's reasoning process example: Let me first analyze this problem... tool_calls: type: array description: >- Generated tool call information (returned when the model decides to call a tool) items: type: object properties: id: type: string description: The unique identifier of the tool call type: type: string description: Tool call type enum: - function - mcp function: type: object description: >- Function call information (containing the generated function name and JSON-formatted arguments) properties: name: type: string description: The generated function name arguments: type: string description: >- The JSON-formatted string of function call arguments; please validate the arguments before calling the function mcp: type: object description: MCP tool call parameters (returned when `type=mcp`) properties: id: type: string description: The unique identifier of the MCP tool call type: type: string description: MCP call type enum: - mcp_list_tools - mcp_call server_label: type: string description: MCP server label error: type: string description: Error message securitySchemes: bearerAuth: type: http scheme: bearer description: >- ##All interfaces require authentication using a Bearer Token## **Get an API Key**: Visit the [API Key management page](https://evolink.ai/dashboard/keys) to obtain your API Key **Add it to the request header when using**: ``` Authorization: Bearer YOUR_API_KEY ``` ````