curl --request POST \
  --url https://direct.evolink.ai/v1/messages \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "model": "glm-5.3",
  "max_tokens": 1024,
  "messages": [
    {
      "role": "user",
      "content": "Hello, world"
    }
  ]
}
'

{
  "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
    }
  }
}

Messages API

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: genuinely supported only by glm-5.3-flash; see the messages field for details

POST

messages

curl --request POST \
  --url https://direct.evolink.ai/v1/messages \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "model": "glm-5.3",
  "max_tokens": 1024,
  "messages": [
    {
      "role": "user",
      "content": "Hello, world"
    }
  ]
}
'

{
  "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
    }
  }
}

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.

Whether thinking can be turned off varies by model: only glm-5.2 supports thinking.type: "disabled"; glm-5.3 and glm-5.3-flash always think and return an error if disabled is sent. When migrating from glm-5.2, remove the hard-coded disabled before switching models.

Image input is supported only by glm-5.3-flash, and sending images to the other models does not raise an error. When image content blocks go to glm-5.3 or glm-5.2, the request returns 200 as usual, but the model cannot read the image and answers from the text alone — a plausible-looking reply that has nothing to do with the image, and one that is not consistent across requests. This kind of silent failure is hard to track down in production, so choose glm-5.3-flash when you need image understanding.

Authorizations

Authorization

string

header

required

##All interfaces require authentication using a Bearer Token##

Get an API Key:

Visit the API Key management page 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.

Body

application/json

model

enum<string>

default:glm-5.3

required

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-5.2`	Previous-generation flagship, complex reasoning and very long context; 1M context	Can be turned off (`thinking.type=disabled`)	Not supported

Available options:

glm-5.3,

glm-5.3-flash,

glm-5.2

Example:

"glm-5.3"

messages

object[]

required

The list of conversation messages, alternating between user and assistant turns

Note:

At least one message is required
The last message is normally role=user
Multi-turn context is supported and the model draws on the history

Image input: only glm-5.3-flash supports it, through a {"type":"image","source":{...}} block inside the content array.

Sending image content blocks to glm-5.3 or glm-5.2 does not return an error, but the model cannot read the image. The request returns 200 as usual, and the model answers from the text alone, producing a plausible-looking reply that has nothing to do with the image — and the result is not consistent across requests.

This kind of silent failure is hard to track down in production, so choose glm-5.3-flash when you need image understanding.

Minimum array length: 1

Show child attributes

max_tokens

integer

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

Required range: 1 <= x <= 131072

Example:

1024

system

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

Example:

"You are a helpful assistant."

temperature

number

Sampling temperature

Notes:

Higher values make output more varied, lower values more deterministic
Recommended range [0, 1]

Required range: 0 <= x <= 1

Example:

1

top_p

number

Nucleus sampling threshold

Notes:

Range [0, 1]
It is recommended not to adjust temperature and top_p at the same time

Required range: 0 <= x <= 1

Example:

0.9

top_k

integer

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

Required range: x >= 0

Example:

10

stop_sequences

string[]

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

Example:

["\n\n"]

stream

boolean

default:false

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)

Example:

false

thinking

object

Controls deep thinking

Note:

Every model in the GLM series is a reasoning model, and thinking is on by default when this field is omitted
When it is on, the response content array contains a type="thinking" reasoning block (billed as output tokens; signature may be an empty string)
Only the binary type switch takes effect: thinking budget or level parameters such as budget_tokens and effort have no effect (they are ignored)

Whether it can be turned off depends on the model:

glm-5.2: sending {"type":"disabled"} turns thinking off and cuts output tokens noticeably
glm-5.3 / glm-5.3-flash: always think, cannot be turned off. Sending disabled returns an error

The consequence: the glm-5.3 series cannot reduce thinking overhead on this endpoint. It cannot be turned off (disabled errors), and it cannot be turned down either (budget_tokens and effort both have no effect, and the top-level reasoning_effort is an OpenAI-protocol field that this endpoint ignores). Thinking content is billed as output tokens, so on this endpoint that cost is unavoidable.

To control thinking cost, switch to the Chat Completions API — there, reasoning_effort has three levels that genuinely take effect: low / high / max. glm-5.2 is not subject to this limit; it can turn thinking off directly on this endpoint.

Migrating from glm-5.2: if your code hard-codes thinking.type=disabled, you must remove that field before switching to glm-5.3, otherwise the request fails outright. And if you relied on turning thinking off to control cost, this endpoint offers no equivalent — plan to move to the Chat Completions API as part of the switch.

Show child attributes

tools

object[]

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

Show child attributes

tool_choice

object

Tool selection strategy

Show child attributes

metadata

object

Request metadata

Show child attributes

Response

Message object

Anthropic-style message response

string

The message's unique ID (format: msg_<uuid>)

type

enum<string>

Response object type

Available options:

message

role

enum<string>

Available options:

assistant

model

string

Model actually used

Example:

"glm-5.3"

content

object[]

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

Show child attributes

stop_reason

enum<string>

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

Available options:

end_turn,

max_tokens,

tool_use

usage

object

Token usage statistics (Anthropic specification)

Show child attributes

Image Series

Video Series

Audio Series

Text Series

Account Management

Task Management

File Management

GLM All-Model API - Messages Reference

Authorizations

Body

Response