> ## 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 All-Model API - Messages Reference
> - Call GLM series models through the Anthropic Messages protocol, choosing the specific model with the `model` parameter
- The request and response structures match the Anthropic API
- **System prompt**: passed through the top-level `system` field
- **Thinking mode**: thinking is on by default across the series and is returned in `content[type=thinking]` blocks; only `glm-5.2` can turn it off with `thinking.type=disabled`
- **Streaming**: SSE event stream
- **Tool calling**: compatible with the Anthropic `tool_use` / `tool_result` flow
- **Image input**: supported only by `glm-5.3-flash` and `glm-5.3-flashx`; see the `messages` field for details
**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.
Thinking settings. Thinking is enabled by default for all models. Response thinking blocks are billed as output tokens; `signature` may be an empty string.
* `glm-5.2`: `thinking.type=disabled` turns thinking off.
* `glm-5.3` / `glm-5.3-flash` / `glm-5.3-flashx`: thinking is always enabled. Passing `disabled` retains the default thinking mode and its output-token charges.
`thinking.budget_tokens` and `thinking.effort` do not set GLM thinking levels. See the top-level `reasoning_effort` field for compatible values. A setting that disables thinking on `glm-5.2` does not disable it after switching to the 5.3 series.
`reasoning_effort`: GLM reasoning-level compatibility field at the request top level, outside thinking. The endpoint accepts this field but does not guarantee that it controls actual thinking effort.
**Compatibility rules for `glm-5.3` / `glm-5.3-flash` / `glm-5.3-flashx`**
| Supplied value | Compatible value |
| - | - |
| `low` / `high` / `max` | Unchanged |
| `xhigh` | `max` |
| `medium` | `high` |
| `minimal` / `none` | `low`; thinking remains enabled |
**`minimal` and `none` do not disable thinking on the 5.3 series.** Thinking tokens are billed as output. Unrecognized values are unchanged and have no compatibility mapping; use the listed values. These mappings do not apply to glm-5.2.
To control thinking effort, use `reasoning_effort` in [Chat Completions](/docs/en/api-manual/language-series/glm/chat-completions/chat-completions-reference) or `reasoning.effort` in [Responses](/docs/en/api-manual/language-series/glm/responses/responses-reference).
Only `glm-5.3-flash` and `glm-5.3-flashx` support image input. Image blocks sent to `glm-5.3` or `glm-5.2` may return 200 even though the model cannot read them. Choose an image-capable model for image understanding.
## OpenAPI
````yaml en/api-manual/language-series/glm/messages/messages-reference.json POST /v1/messages
openapi: 3.1.0
info:
title: GLM All-Model API - Messages Reference
description: >-
Full API reference for calling Zhipu GLM text models through the Anthropic
Messages protocol.
**Models covered**: `glm-5.3`, `glm-5.3-flash`, `glm-5.3-flashx`, `glm-5.2`
(selected with the `model` parameter)
**Compatibility notes**:
- Path: `/v1/messages` (the standard Anthropic path)
- The request and response structures match the Anthropic Messages API
- Supported fields: `model` `messages` (required) `system` `max_tokens`
`temperature` `top_p` `top_k` `stop_sequences` `stream` `thinking`
`reasoning_effort` `tools` `tool_choice` `metadata`
**Shared capabilities**:
- 1M token context window, up to 131,072 tokens of output
- Thinking mode: **thinking is on by default** across the series; it is
returned in `content[type=thinking]` blocks and counts towards output tokens
- Prompt caching: an implicit prefix cache that repeated requests with the
same prefix hit automatically (reported in `cache_read_input_tokens`), with
no need to set `cache_control`; `cache_creation_input_tokens` is always 0
**Differences between models** (whether thinking can be turned off, whether
images can be read) are described in the `model`, `thinking` and `messages`
fields below.
license:
name: MIT
version: 1.0.0
servers:
- url: https://direct.evolink.ai
description: Production (Recommended, better support for text models)
- url: https://api.evolink.ai
description: Alternative URL
security:
- bearerAuth: []
tags:
- name: Messages
description: Anthropic Messages protocol endpoints
paths:
/v1/messages:
post:
tags:
- Messages
summary: GLM Messages (All Models, Anthropic-Compatible)
description: >-
- Call GLM series models through the Anthropic Messages protocol,
choosing the specific model with the `model` parameter
- The request and response structures match the Anthropic API
- **System prompt**: passed through the top-level `system` field
- **Thinking mode**: thinking is on by default across the series and is
returned in `content[type=thinking]` blocks; only `glm-5.2` can turn it
off with `thinking.type=disabled`
- **Streaming**: SSE event stream
- **Tool calling**: compatible with the Anthropic `tool_use` /
`tool_result` flow
- **Image input**: supported only by `glm-5.3-flash` and
`glm-5.3-flashx`; see the `messages` field for details
operationId: createMessageGLM
requestBody:
required: true
content:
application/json:
schema:
$ref: '#/components/schemas/CreateMessageRequest'
examples:
simple:
summary: Minimal runnable request
value:
model: glm-5.3
max_tokens: 1024
messages:
- role: user
content: Hello, world
system_prompt:
summary: With a system prompt
value:
model: glm-5.3
max_tokens: 2048
system: You are a seasoned technical editor.
messages:
- role: user
content: Introduce GLM-5.3 in three sentences.
stop_sequences:
summary: Custom stop sequences
value:
model: glm-5.3
max_tokens: 50
thinking:
type: disabled
stop_sequences:
- '3'
messages:
- role: user
content: 'Output exactly: 1 2 3 4 5 6'
tool_use:
summary: Tool calling (Anthropic tool_use style)
value:
model: glm-5.3
max_tokens: 2048
messages:
- role: user
content: Check the weather in Tokyo and tell me
tools:
- name: get_weather
description: Query the current weather of a specified city
input_schema:
type: object
properties:
city:
type: string
description: 'City name, for example: Tokyo'
required:
- city
tool_choice:
type: auto
streaming:
summary: Streaming output (SSE)
value:
model: glm-5.3
max_tokens: 1024
stream: true
messages:
- role: user
content: Write a short poem about spring
disable_thinking_glm52_only:
summary: Disable thinking mode (glm-5.2 only)
value:
model: glm-5.2
max_tokens: 512
thinking:
type: disabled
messages:
- role: user
content: 'In one sentence: what is the capital of Japan?'
description: >-
Only glm-5.2 can disable thinking. glm-5.3, glm-5.3-flash and
glm-5.3-flashx retain the default thinking mode when
thinking.type=disabled is sent. Thinking tokens are billed as
output.
vision_flash:
summary: Image input (glm-5.3-flash / glm-5.3-flashx only)
description: >-
`glm-5.3-flash` and `glm-5.3-flashx` support vision natively.
Images are passed in through `image` content blocks, and
`source` accepts both `base64` and `url`.
value:
model: glm-5.3-flash
max_tokens: 1024
messages:
- role: user
content:
- type: text
text: What is in this image?
- type: image
source:
type: base64
media_type: image/png
data:
flashx:
summary: Call GLM-5.3-FlashX
value:
model: glm-5.3-flashx
max_tokens: 1024
messages:
- role: user
content: Introduce yourself in one sentence.
vision_flashx:
summary: GLM-5.3-FlashX image input
value:
model: glm-5.3-flashx
max_tokens: 1024
messages:
- role: user
content:
- type: text
text: What is in this image?
- type: image
source:
type: base64
media_type: image/png
data:
responses:
'200':
description: Message object
content:
application/json:
schema:
$ref: '#/components/schemas/MessageResponse'
examples:
with_thinking:
summary: Includes a thinking content block by default
value:
id: msg_0842a705-9d0b-4eaa-b12d-09a4106326c5
type: message
role: assistant
model: glm-5.3
content:
- type: thinking
thinking: >-
The user asked to greet them with one word, so
answering "Hi" will do.
signature: ''
- type: text
text: Hi.
stop_reason: end_turn
usage:
input_tokens: 18
output_tokens: 101
cache_creation_input_tokens: 0
cache_read_input_tokens: 0
prompt_tokens_details:
cached_tokens: 0
tool_use:
summary: Triggers a tool call (stop_reason=tool_use)
value:
id: msg_067e85db-53df-43a1-bd38-09c53375f2f0
type: message
role: assistant
model: glm-5.3
content:
- type: tool_use
id: toolu_36b8a98e284c426799f08612
name: get_weather
input:
city: Tokyo
stop_reason: tool_use
usage:
input_tokens: 161
output_tokens: 11
cache_creation_input_tokens: 0
cache_read_input_tokens: 0
prompt_tokens_details:
cached_tokens: 0
'400':
description: Invalid request parameters
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
example:
type: error
request_id: req_xxx
error:
type: invalid_request_error
message: Invalid request
'401':
description: Authentication error
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
example:
type: error
error:
type: authentication_error
message: Authentication error
'402':
description: Insufficient quota
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
example:
type: error
error:
type: billing_error
message: Insufficient quota
'403':
description: Permission error
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
example:
type: error
error:
type: permission_error
message: Permission denied
'404':
description: Model or resource not found
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
example:
type: error
error:
type: not_found_error
message: Model not found
'429':
description: Rate limited
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
example:
type: error
error:
type: rate_limit_error
message: Rate limited
'500':
description: Internal server error
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
'502':
description: Service temporarily unavailable
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
'503':
description: Service temporarily unavailable
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
components:
schemas:
CreateMessageRequest:
type: object
required:
- model
- messages
properties:
model:
type: string
description: >-
Model to call:
| Model ID | Positioning | Thinking can be turned off | Image input
|
|---|---|---|---|
| `glm-5.3` | Flagship model, with across-the-board gains on complex
software engineering and agent tasks; 1M context | Cannot be turned
off | Not supported |
| `glm-5.3-flash` | Lightweight multimodal model, extremely low cost
with native vision; 1M context | Cannot be turned off | Supported |
| `glm-5.3-flashx` | Multimodal model with image input; 1M context |
Cannot be turned off | Supported |
| `glm-5.2` | Previous-generation flagship, complex reasoning and
very long context; 1M context | Can be turned off
(`thinking.type=disabled`) | Not supported |
enum:
- glm-5.3
- glm-5.3-flash
- glm-5.3-flashx
- glm-5.2
example: glm-5.3
default: glm-5.3
max_tokens:
type: integer
description: >-
Upper limit on the length of the generated content (in tokens)
**Note**:
- The GLM series supports up to **131,072 tokens** (128K) of output;
setting at least `1024` is recommended
- Tokens produced by thinking also count towards this limit
- When the limit is reached the content is truncated and the
response carries `stop_reason=max_tokens`
minimum: 1
maximum: 131072
example: 1024
messages:
type: array
description: >-
Conversation messages, alternating between user and assistant.
Include at least one message; the last message is usually from the
user. Include earlier messages for multi-turn context.
**Image input**: only glm-5.3-flash and glm-5.3-flashx support image
blocks in the content array. Use text only with glm-5.3 and glm-5.2.
An HTTP 200 response does not mean that an unsupported model read
the image.
items:
$ref: '#/components/schemas/InputMessage'
minItems: 1
system:
description: >-
System prompt, used to set the AI's role and behavior
**Notes**:
- Supports a string or an array of content blocks
- Passed via the top-level `system` field (do not place it inside
messages)
- The model follows the system constraints
- **An overly long system may be truncated**: For long context,
place it in `messages` rather than piling everything into `system`
oneOf:
- type: string
example: You are a helpful assistant.
- type: array
description: >-
System prompt in content-block array format. text blocks may
carry cache_control
items:
type: object
required:
- type
- text
properties:
type:
type: string
enum:
- text
text:
type: string
cache_control:
$ref: '#/components/schemas/CacheControl'
temperature:
type: number
description: >-
Sampling temperature
**Notes**:
- Higher values make output more varied, lower values more
deterministic
- Recommended range `[0, 1]`
minimum: 0
maximum: 1
example: 1
top_p:
type: number
description: >-
Nucleus sampling threshold
**Notes**:
- Range `[0, 1]`
- It is recommended not to adjust temperature and top_p at the same
time
minimum: 0
maximum: 1
example: 0.9
top_k:
type: integer
description: >-
Sample only from the K highest-probability tokens (an
Anthropic-specific parameter)
**Notes**:
- Smaller values make output more deterministic, larger values make
candidates more diverse
minimum: 0
example: 10
stop_sequences:
type: array
description: >-
Custom stop sequences: generation stops when it hits any of these
strings
**Notes**:
- Hitting one truncates output, and content before the hit is
returned normally
- **Note**: When a stop sequence is hit, the GLM series returns
`stop_reason` as `end_turn` (rather than the Anthropic-standard
`stop_sequence`), and the response does not include a
`stop_sequence` field. If your client relies on
`stop_reason=="stop_sequence"` to detect a hit, special handling is
required
items:
type: string
example:
- |+
stream:
type: boolean
description: >-
Whether to return via SSE streaming
- `true`: Server-Sent Events streaming (standard Anthropic event
sequence: message_start / content_block_start / content_block_delta
/ message_delta / message_stop)
- `false`: Returns the complete response all at once (default)
default: false
example: false
thinking:
type: object
description: >-
Thinking settings. Thinking is enabled by default for all models.
Response thinking blocks are billed as output tokens; signature may
be an empty string.
- glm-5.2: thinking.type=disabled turns thinking off.
- glm-5.3 / glm-5.3-flash / glm-5.3-flashx: thinking is always
enabled. Passing disabled retains the default thinking mode and its
output-token charges.
thinking.budget_tokens and thinking.effort do not set GLM thinking
levels. See the top-level reasoning_effort field for compatible
values. A setting that disables thinking on glm-5.2 does not disable
it after switching to the 5.3 series.
required:
- type
properties:
type:
type: string
enum:
- enabled
- disabled
description: >-
Thinking is enabled when thinking is omitted. enabled enables
thinking. disabled turns it off only on glm-5.2; the 5.3 series
retains its default thinking mode.
reasoning_effort:
type: string
enum:
- max
- xhigh
- high
- medium
- low
- minimal
- none
description: >-
GLM reasoning-level compatibility field at the request top level,
outside thinking. The endpoint accepts this field but does not
guarantee that it controls actual thinking effort.
**Compatibility rules for glm-5.3 / glm-5.3-flash / glm-5.3-flashx**
| Supplied value | Compatible value |
| --- | --- |
| `low` / `high` / `max` | Unchanged |
| `xhigh` | `max` |
| `medium` | `high` |
| `minimal` / `none` | low; thinking remains enabled |
**minimal and none do not disable thinking on the 5.3 series.**
Thinking tokens are billed as output. Unrecognized values are
unchanged and have no compatibility mapping; use the listed values.
These mappings do not apply to glm-5.2.
To control thinking effort, use reasoning_effort in [Chat
Completions](/en/api-manual/language-series/glm/chat-completions/chat-completions-reference)
or reasoning.effort in
[Responses](/en/api-manual/language-series/glm/responses/responses-reference).
tools:
type: array
description: >-
The list of tool definitions
**Notes**:
- Follows the Anthropic tool definition spec
- `input_schema` uses a JSON Schema object
- The model returns standard `tool_use` blocks with
`stop_reason=tool_use`
items:
$ref: '#/components/schemas/Tool'
tool_choice:
type: object
description: Tool selection strategy
required:
- type
properties:
type:
type: string
enum:
- auto
- none
description: |-
- `auto`: the model decides on its own whether to call a tool
- `none`: tool calls are disallowed
metadata:
type: object
description: Request metadata
properties:
user_id:
type: string
description: >-
A unique identifier representing the end user, which can be used
for per-user monitoring and abuse detection (using a hashed ID
is recommended)
MessageResponse:
type: object
description: Anthropic-style message response
properties:
id:
type: string
description: 'The message''s unique ID (format: `msg_`)'
type:
type: string
enum:
- message
description: Response object type
role:
type: string
enum:
- assistant
model:
type: string
description: Model actually used
example: glm-5.3
content:
type: array
description: >-
The list of response content blocks
**Possible block types**:
- `thinking`: the reasoning process (when thinking is enabled, which
is the default)
- `text`: the final answer text
- `tool_use`: a tool call initiated by the model
items:
$ref: '#/components/schemas/OutputContentBlock'
stop_reason:
type: string
description: >-
Stop reason
- `end_turn`: natural completion (also returned when stop_sequences
is hit)
- `max_tokens`: reached the max_tokens limit
- `tool_use`: the model triggered a tool call
enum:
- end_turn
- max_tokens
- tool_use
usage:
$ref: '#/components/schemas/AnthropicUsage'
ErrorResponse:
type: object
properties:
type:
type: string
enum:
- error
error:
type: object
properties:
type:
type: string
description: >-
Error type (e.g. invalid_request_error / authentication_error /
billing_error, etc.)
message:
type: string
description: Error description
request_id:
type: string
description: Request tracing ID
InputMessage:
type: object
required:
- role
- content
properties:
role:
type: string
enum:
- user
- assistant
description: >-
The role of the message sender, alternating between user / assistant
- `user`: user message (also used to pass back `tool_result` blocks)
- `assistant`: the assistant's historical replies (may contain
`text` / `thinking` / `tool_use` blocks)
**`system` is not accepted**: pass system prompts via the top-level
`system` field.
content:
description: >-
Message content
**Note**:
- For plain text, pass a string directly
- For structured content, pass an array of content blocks (`text` /
`image` / `tool_use` / `tool_result` / `thinking`)
- `image` content blocks are **supported only by `glm-5.3-flash` and
`glm-5.3-flashx`**; sending them to any other model does not return
an error, but the model cannot read the image
oneOf:
- type: string
- type: array
items:
$ref: '#/components/schemas/ContentBlock'
CacheControl:
type: object
description: >-
Prompt cache marker
**Note**: the GLM series uses an implicit cache (built automatically
from identical prefixes, hits shown in `cache_read_input_tokens`, needs
warming up) and does not rely on this explicit marker; `cache_control`
can be sent normally but may be ignored, and it does not affect the
implicit cache.
required:
- type
properties:
type:
type: string
enum:
- ephemeral
description: Cache marker type
Tool:
type: object
required:
- name
- input_schema
properties:
name:
type: string
description: |-
Tool name
**Notes**:
- Only `a-zA-Z0-9_-` are allowed
- Maximum 64 characters
description:
type: string
description: >-
A description of the tool's capability, for the model to decide when
to call it
input_schema:
type: object
description: |-
The JSON Schema object for the tool's input parameters
**Notes**:
- `type` must be `object`
- Should declare `properties` and `required`
cache_control:
$ref: '#/components/schemas/CacheControl'
OutputContentBlock:
type: object
description: A content block in the response
properties:
type:
type: string
enum:
- text
- thinking
- tool_use
text:
type: string
description: Text when type=`text`
thinking:
type: string
description: The reasoning-process text when type=`thinking`
signature:
type: string
description: Signature when type=`thinking` (may be an empty string)
id:
type: string
description: Tool call ID when type=`tool_use`
name:
type: string
description: Tool name when type=`tool_use`
input:
type: object
description: >-
The JSON input parameters generated by the model when
type=`tool_use`
AnthropicUsage:
type: object
description: Token usage statistics (Anthropic specification)
properties:
input_tokens:
type: integer
description: Number of input tokens (the portion not served from cache)
example: 18
output_tokens:
type: integer
description: Number of output tokens (including thinking)
example: 101
cache_creation_input_tokens:
type: integer
description: >-
Number of tokens written to the cache. The GLM series uses implicit
caching with no explicit cache-creation step, so **this field is
always 0**.
example: 0
cache_read_input_tokens:
type: integer
description: >-
Number of input tokens served from the implicit prefix cache.
Repeated requests with the same prefix hit it automatically, with no
need to set `cache_control`; the cached portion is billed at the
cache rate, which is markedly lower than the uncached input rate.
The first request returns 0.
example: 0
prompt_tokens_details:
type: object
description: >-
Input token breakdown (cache-hit fields, also returned by the GLM
series)
properties:
cached_tokens:
type: integer
description: Number of input tokens served from cache
example: 0
ContentBlock:
type: object
description: >-
Message content block
**Supported types**:
- `text`: text content
- `image`: image (**`glm-5.3-flash` and `glm-5.3-flashx` only**; other
models do not return an error but cannot read the image)
- `tool_use`: replay of the previous assistant tool call
- `tool_result`: tool execution result
- `thinking`: replay of the previous assistant thinking content
required:
- type
properties:
type:
type: string
enum:
- text
- image
- tool_use
- tool_result
- thinking
text:
type: string
description: Text content when type=`text`
source:
type: object
description: >-
Image source (required when type=`image`, **supported only by
`glm-5.3-flash` and `glm-5.3-flashx`**)
Both inline `base64` and `url` are supported.
required:
- type
properties:
type:
type: string
enum:
- base64
- url
description: |-
Image source format:
- base64: inline image data.
- url: a publicly accessible image URL.
media_type:
type: string
description: Image MIME type (required when `type=base64`)
example: image/png
data:
type: string
description: >-
Base64-encoded image data (required when `type=base64`, without
the `data:` prefix)
example: iVBORw0KGgoAAAANSUhEUgAA...
url:
type: string
description: Public image URL (required when `type=url`)
example: https://example.com/photo.jpg
id:
type: string
description: Tool call ID (required for tool_use)
name:
type: string
description: Tool name (required for tool_use)
input:
type: object
description: Tool input parameters (for tool_use, a JSON object)
tool_use_id:
type: string
description: >-
The corresponding tool call ID (required for tool_result, filled
back from tool_use.id)
content:
description: >-
Tool execution result (tool_result), a string or an array of content
blocks
oneOf:
- type: string
- type: array
items:
type: object
thinking:
type: string
description: >-
The passed-back assistant thinking-process content (used when
type=`thinking`)
signature:
type: string
description: >-
The signature of the returned thinking content; it must be passed
back unchanged when continuing a multi-turn conversation (may be an
empty string)
cache_control:
$ref: '#/components/schemas/CacheControl'
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
```
**Note**: EvoLink uses Bearer Token authentication uniformly for
`/v1/messages`.
````
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