Claude Messages API Application and Usage

Anthropic Claude is a very powerful AI conversational system that can generate fluent and natural responses in just a few seconds by simply entering a prompt. Claude Messages API is Anthropic’s official native API format. Unlike the OpenAI-compatible format (Chat Completion), it uses Anthropic’s own request and response structure, enabling better use of Claude’s unique capabilities, such as multimodal content input, tool calling, extended thinking, and other advanced features. This document mainly introduces the usage process of Claude Messages API operations. With it, we can use native interfaces consistent with Anthropic’s official ones to invoke Claude’s conversational capabilities.

Application Process

To use Claude Messages API, first go to the Ace Data Cloud Console to obtain your API Token and keep it for later use.

If you have not yet logged in or registered, you will be automatically redirected to the login page and invited to register and log in. After completion, you will automatically return to the current page. One API Token can invoke all platform services; there is no need to apply separately for each service. Your first application includes free credits for a free trial; when credits are insufficient, you can recharge your general balance in the Console.

📘 Full documentation: Claude Messages API →

Basic Usage

The request path for Claude Messages API is /v1/messages, consistent with the official Anthropic API. We need to provide at least three required parameters:

model: Select the Claude model to use. claude-opus-5-5 is available only through the Messages API, supports 1 million Token context, a maximum output of 128K Tokens, and always enables adaptive thinking. The latest flagship is claude-fable-5-1 (1 million Token context, maximum output of 128K Tokens); the original claude-fable-5 is still retained for compatibility. claude-sonnet-5-5 has joined the native Messages series interface, supports image input and adaptive thinking; its official reference prices for input, output, and cache reads are 2, 10, and 0.20 USD per million Tokens, respectively.
messages: An array of input messages. Each message contains role (role) and content (content), where role supports user and assistant.
max_tokens: The maximum number of output tokens, used to limit the length of a single response.

Common optional parameters:

system: System prompt, used to set the model’s behavior and role.
temperature: Generation randomness, between 0 and 1. The higher the value, the more divergent the response.
stream: Whether to use streaming responses. Setting it to true enables a word-by-word return effect.
stop_sequences: Custom stop sequences. The model stops generating when it encounters these texts.
top_p: Nucleus sampling parameter, used together with temperature to control generation randomness.
top_k: Sample only from the K options with the highest probabilities.
tools: Tool definitions, used to allow the model to call external functions.
tool_choice: Controls how the model uses the provided tools.
cache_control: Automatically creates a cache breakpoint at the last cacheable content block of the request; it can also be written on a specific content block.

cURL Example

curl -X POST 'https://api.acedata.cloud/v1/messages' \
  -H 'accept: application/json' \
  -H 'authorization: Bearer {token}' \
  -H 'content-type: application/json' \
  -d '{
    "model": "claude-fable-5-1",
    "max_tokens": 1024,
    "messages": [
      {
        "role": "user",
        "content": "Hello, Claude"
      }
    ]
  }'

Python Example

import requests

url = "https://api.acedata.cloud/v1/messages"

headers = {
    "accept": "application/json",
    "authorization": "Bearer {token}",
    "content-type": "application/json"
}

payload = {
    "model": "claude-fable-5-1",
    "max_tokens": 1024,
    "messages": [
        {"role": "user", "content": "Hello, Claude"}
    ]
}

response = requests.post(url, json=payload, headers=headers)
print(response.json())

After calling, the returned result is as follows:

{
  "id": "msg_013Zva2CMHLNnXjNJJKqJ2EF",
  "type": "message",
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "Hi! My name is Claude. How can I help you today?"
    }
  ],
  "model": "claude-opus-4-8",
  "stop_reason": "end_turn",
  "stop_sequence": null,
  "usage": {
    "input_tokens": 12,
    "output_tokens": 15
  }
}

Description of returned result fields:

id: Unique identifier of this message.
type: Always message.
role: Always assistant.
content: An array of response content. Each element contains type (such as text) and the corresponding content.
model: The name of the model processing the request.
stop_reason: The reason for stopping. Stable values include end_turn, max_tokens, stop_sequence, tool_use, pause_turn (you can return the current assistant content unchanged to continue), refusal, and model_context_window_exceeded.
stop_sequence: If stopped due to a custom stop sequence, displays the matching stop sequence text.
stop_details: When stop_reason is refusal, it may contain the refusal category and description.
usage: Token usage statistics. input_tokens is uncached input; cache_creation_input_tokens and cache_read_input_tokens are cache writes and reads respectively; output_tokens is the total number of output tokens. If output_tokens_details.thinking_tokens is returned, this value is a subset of output_tokens; do not add it again when calculating totals or costs. This detail may be null or omitted when there is no authoritative count.
usage.cache_creation: Optional cache write TTL details, containing ephemeral_5m_input_tokens and ephemeral_1h_input_tokens. When the object exists, the sum of the two equals cache_creation_input_tokens; a field being null or omitted means that the current response has no available TTL breakdown and must not be interpreted as 0.
usage.cost: Non-streaming responses may contain a credit consumption object recorded by Ace Data Cloud, where amount is the actual consumption for this request, currency is the unit of measurement, and list_amount is the amount before discount (if any). The official base price for Fable 5.1 cache reads is $0.25 per million Tokens, while the base prices for 5-minute and 1-hour cache writes are $12.50 and $20 per million Tokens, respectively; actual platform prices are converted according to plan discounts.

System Prompt

Claude Messages API supports setting a system prompt through the system field, used to define the model’s behavior, role, and context.

Python Example

import requests

url = "https://api.acedata.cloud/v1/messages"

headers = {
    "accept": "application/json",
    "authorization": "Bearer {token}",
    "content-type": "application/json"
}

payload = {
    "model": "claude-sonnet-4-20250514",
    "max_tokens": 1024,
    "system": "You are a professional Chinese translation assistant. Please translate the English input from the user into Chinese.",
    "messages": [
        {"role": "user", "content": "The quick brown fox jumps over the lazy dog."}
    ]
}

response = requests.post(url, json=payload, headers=headers)
print(response.json())

By setting the system prompt, you can precisely control Claude’s role and behavior.

Streaming Responses

This interface also supports streaming responses. Set the stream parameter to true to receive results progressively, which is highly suitable for implementing character-by-character display on web pages.

Python Example

import requests

url = "https://api.acedata.cloud/v1/messages"

headers = {
    "accept": "application/json",
    "authorization": "Bearer {token}",
    "content-type": "application/json"
}

payload = {
    "model": "claude-sonnet-4-20250514",
    "max_tokens": 1024,
    "stream": True,
    "messages": [
        {"role": "user", "content": "Hello, Claude"}
    ]
}

response = requests.post(url, json=payload, headers=headers, stream=True)
for line in response.iter_lines():
    if line:
        print(line.decode("utf-8"))

Streaming responses are returned in Server-Sent Events (SSE) format, with each line prefixed by event: and data:. Streaming event types include:

message_start: The message starts, containing the basic message information and model name.
content_block_start: A content block starts.
content_block_delta: An incremental update to a content block, containing newly generated text fragments.
content_block_stop: A content block ends.
message_delta: An incremental update at the message level, containing stop_reason and the final usage information. The authoritative value of output_tokens_details.thinking_tokens should only be read from the last message_delta.usage; do not accumulate it across events.
message_stop: The message ends.

The output is as follows:

event: message_start
data: {"type":"message_start","message":{"id":"msg_01XFDUDYJgAACzvnptvVoYEL","type":"message","role":"assistant","content":[],"model":"claude-sonnet-4-20250514","stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":12,"output_tokens":0}}}

event: content_block_start
data: {"type":"content_block_start","index":0,"content_block":{"type":"text","text":""}}

event: content_block_delta
data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"Hi"}}

event: content_block_delta
data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"! My name is"}}

event: content_block_delta
data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":" Claude. How can I help you today?"}}

event: content_block_stop
data: {"type":"content_block_stop","index":0}

event: message_delta
data: {"type":"message_delta","delta":{"stop_reason":"end_turn","stop_sequence":null},"usage":{"output_tokens":15}}

event: message_stop
data: {"type":"message_stop"}

As you can see, the content_block_delta events in the streaming response contain progressively generated text content. By concatenating all text_delta values, you can obtain the complete reply.

JavaScript Example

const options = {
  method: "POST",
  headers: {
    accept: "application/json",
    authorization: "Bearer {token}",
    "content-type": "application/json",
  },
  body: JSON.stringify({
    model: "claude-sonnet-4-20250514",
    max_tokens: 1024,
    stream: true,
    messages: [{ role: "user", content: "Hello, Claude" }],
  }),
};

const response = await fetch("https://api.acedata.cloud/v1/messages", options);
const reader = response.body.getReader();
const decoder = new TextDecoder();

while (true) {
  const { done, value } = await reader.read();
  if (done) break;
  console.log(decoder.decode(value));
}

Multi-turn Conversations

If you want to integrate multi-turn conversation functionality, you need to alternately arrange messages with the user and assistant roles in the messages array, and pass in the previous conversation history together.

Python Example

import requests

url = "https://api.acedata.cloud/v1/messages"

headers = {
    "accept": "application/json",
    "authorization": "Bearer {token}",
    "content-type": "application/json"
}

payload = {
    "model": "claude-sonnet-4-20250514",
    "max_tokens": 1024,
    "messages": [
        {"role": "user", "content": "Hello, my name is Alice."},
        {"role": "assistant", "content": "Hello Alice! Nice to meet you. How can I help you today?"},
        {"role": "user", "content": "What is my name?"}
    ]
}

response = requests.post(url, json=payload, headers=headers)
print(response.json())

The response is as follows:

{
  "id": "msg_01Y1wfQmd89g968TVbFu57Yc",
  "type": "message",
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "Your name is Alice, as you just told me!"
    }
  ],
  "model": "claude-sonnet-4-20250514",
  "stop_reason": "end_turn",
  "stop_sequence": null,
  "usage": {
    "input_tokens": 40,
    "output_tokens": 14
  }
}

By passing the complete conversation history in messages, Claude can provide accurate answers based on the context.

Deep Thinking Models

Claude’s thinking and thinking summary are two different concepts: the model can perform internal reasoning, but the API does not return the original chain of thought. When the reasoning process needs to be displayed, the API returns a processed summary. Current models are recommended to use adaptive thinking, and the overall reasoning effort is controlled through output_config.effort:

import requests

url = "https://api.acedata.cloud/v1/messages"
headers = {
    "accept": "application/json",
    "authorization": "Bearer {token}",
    "content-type": "application/json"
}
payload = {
    "model": "claude-opus-5",
    "max_tokens": 16000,
    "thinking": {
        "type": "adaptive",
        "display": "summarized"
    },
    "output_config": {
        "effort": "high"
    },
    "messages": [
        {"role": "user", "content": "What is the sine of 30 degrees?"}
    ]
}

response = requests.post(url, json=payload, headers=headers)
print(response.json())

The thinking block in the response is as follows:

{
  "type": "thinking",
  "thinking": "The problem asks for a standard trigonometric value...",
  "signature": "opaque-signature"
}

display: "summarized" returns a readable thinking summary; it is not the raw chain of thought.
display: "omitted" returns thinking: "", while still retaining the opaque signature to support subsequent conversations.
display: "updates" is Anthropic’s beta mode and requires anthropic-beta: thinking-display-updates-2026-08-18. The official definition is to hide the reasoning body and display brief progress updates between tool calls. The platform passes through this parameter and beta request header as-is, and will not forcibly rewrite it as summarized; the actual output content depends on the support of the selected model.
thinking, output_config, effort, sampling parameters, and newly added extension fields are passed through as-is. The platform does not maintain hardcoded value enumerations or combination restrictions by model, nor does it automatically rewrite thinking modes; whether these parameters are accepted is determined by the selected model, and parameter errors returned by the model are passed back to the client.
The default display value for Fable 5.1, Fable 5, Opus 5, Sonnet 5, Opus 4.8, and Opus 4.7 is omitted; Opus 4.6, Sonnet 4.6, and earlier models that support thinking use summarized by default.
Display affects only returned content and streaming latency; it does not disable reasoning or reduce billing for thinking tokens.
Whether thinking is enabled by default and the display default value are two independent matters. Opus 5 and Sonnet 5 enable adaptive thinking by default; for Opus 5, omitting thinking is equivalent to adaptive, and omitting output_config.effort is equivalent to high. Opus 4.8, 4.7, and 4.6 require explicit enablement.
Thinking and the final body jointly use the max_tokens output budget. When the budget is too small, thinking may consume most of the allowance, causing the body to be empty or truncated; please increase max_tokens, or use low / medium effort to control reasoning investment.
For models that allow thinking to be disabled, you can pass thinking: {"type":"disabled"}. New modes such as between_tools and effort combinations depend on the selected model; the platform does not reject them in advance based on old model rules.
Fixed thinking budgets can be passed through budget_tokens; new models typically use thinking.type=adaptive and output_config.effort. Whether fixed budgets or disabling thinking are supported depends on the current capabilities of the selected model.
During multi-turn conversations and tool calls, the complete thinking block and signature returned by the assistant should be passed back as-is; do not modify or generate the signature yourself.
Parameter pass-through does not mean that every model supports every feature. Cross-protocol calls still require conversion, and the support scope and errors for content such as redacted_thinking are determined by the model service that actually processes the request.

In streaming requests, summarized produces thinking_delta; omitted does not produce thinking_delta, and only retains the thinking block lifecycle and signature_delta.

Vision Models

Claude supports multimodal input and can process text and images simultaneously. In the Messages API, vision capabilities can be used by setting content to an array format and passing image content blocks.

Using Base64-Encoded Images

import base64
import requests

url = "https://api.acedata.cloud/v1/messages"

headers = {
    "accept": "application/json",
    "authorization": "Bearer {token}",
    "content-type": "application/json"
}

# 读取并编码图片
with open("image.png", "rb") as f:
    image_data = base64.standard_b64encode(f.read()).decode("utf-8")

payload = {
    "model": "claude-sonnet-4-20250514",
    "max_tokens": 1024,
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "source": {
                        "type": "base64",
                        "media_type": "image/png",
                        "data": image_data
                    }
                },
                {
                    "type": "text",
                    "text": "What's in this image?"
                }
            ]
        }
    ]
}

response = requests.post(url, json=payload, headers=headers)
print(response.json())

Using URL Images

import requests

url = "https://api.acedata.cloud/v1/messages"

headers = {
    "accept": "application/json",
    "authorization": "Bearer {token}",
    "content-type": "application/json"
}

payload = {
    "model": "claude-sonnet-4-20250514",
    "max_tokens": 1024,
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "source": {
                        "type": "url",
                        "url": "https://cdn.acedata.cloud/ueugot.png"
                    }
                },
                {
                    "type": "text",
                    "text": "What's in this image?"
                }
            ]
        }
    ]
}

response = requests.post(url, json=payload, headers=headers)
print(response.json())

cURL Example

curl -X POST 'https://api.acedata.cloud/v1/messages' \
  -H 'accept: application/json' \
  -H 'authorization: Bearer {token}' \
  -H 'content-type: application/json' \
  -d '{
    "model": "claude-sonnet-4-20250514",
    "max_tokens": 1024,
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "image",
            "source": {
              "type": "url",
              "url": "https://cdn.acedata.cloud/ueugot.png"
            }
          },
          {
            "type": "text",
            "text": "What'\''s in this image?"
          }
        ]
      }
    ]
  }'

Supported image formats include: image/jpeg, image/png, image/gif, and image/webp.

Documents and PDFs

PDFs use document content blocks and support two stable sources: Base64 and URL. Base64 sources must use application/pdf:

import base64

with open("report.pdf", "rb") as f:
    pdf_data = base64.standard_b64encode(f.read()).decode("utf-8")

payload = {
    "model": "claude-fable-5-1",
    "max_tokens": 1024,
    "messages": [{
        "role": "user",
        "content": [
            {
                "type": "document",
                "source": {
                    "type": "base64",
                    "media_type": "application/pdf",
                    "data": pdf_data
                },
                "title": "Quarterly report"
            },
            {"type": "text", "text": "Summarize this PDF."}
        ]
    }]
}

URL sources are written as {"type":"url","url":"https://example.com/report.pdf"}. document also supports text/plain and content sources composed of text/image blocks; optional fields include title, context, and citations. The file_id source of the Files API belongs to an independent beta feature and is not part of the stable contract of this interface.

Prompt Caching

Top-level cache_control automatically places the cache breakpoint on the final cacheable block:

payload = {
    "model": "claude-fable-5-1",
    "max_tokens": 1024,
    "cache_control": {"type": "ephemeral", "ttl": "5m"},
    "system": "You are an expert on this reference material.",
    "messages": [{"role": "user", "content": "Summarize the key points."}]
}

When precise control over placement is needed, you can also write the same cache_control on text, image, document, tool_use, tool_result content blocks, or tool definitions. ttl supports 5m (default) and 1h; please use usage.cache_creation_input_tokens and usage.cache_read_input_tokens to determine cache writes and hits. When the response provides usage.cache_creation, ephemeral_5m_input_tokens + ephemeral_1h_input_tokens = cache_creation_input_tokens. If cache_creation is null or omitted, it indicates that there is only a total cache write volume and no authoritative TTL breakdown; in this case, do not treat either bucket as a known 0, and billing and totals should still be based on the aggregate fields. Example return result:

{
  "id": "msg_01NCrxpZmV17bhQJJRQEFEb9",
  "type": "message",
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "This image shows an API request configuration interface for what appears to be an AI chat completion service. The interface includes parameters for model selection, messages, stream mode, and max tokens settings."
    }
  ],
  "model": "claude-sonnet-4-20250514",
  "stop_reason": "end_turn",
  "stop_sequence": null,
  "usage": {
    "input_tokens": 1570,
    "output_tokens": 52
  }
}

Tool Use

The Claude Messages API natively supports tool use, allowing the model to call your predefined tools/functions when needed.

Python Example

import requests

url = "https://api.acedata.cloud/v1/messages"

headers = {
    "accept": "application/json",
    "authorization": "Bearer {token}",
    "content-type": "application/json"
}

payload = {
    "model": "claude-sonnet-4-20250514",
    "max_tokens": 1024,
    "tools": [
        {
            "name": "get_weather",
            "description": "Get the current weather in a given location",
            "input_schema": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA"
                    }
                },
                "required": ["location"]
            }
        }
    ],
    "messages": [
        {"role": "user", "content": "What's the weather like in San Francisco?"}
    ]
}

response = requests.post(url, json=payload, headers=headers)
print(response.json())

When the model decides to call a tool, content in the returned result will include a content block of type tool_use:

{
  "id": "msg_01Aq9w938a90dw8q",
  "type": "message",
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "Let me check the weather in San Francisco for you."
    },
    {
      "type": "tool_use",
      "id": "toolu_01A09q90qw90lq917835lgs",
      "name": "get_weather",
      "input": {
        "location": "San Francisco, CA"
      }
    }
  ],
  "model": "claude-sonnet-4-20250514",
  "stop_reason": "tool_use",
  "stop_sequence": null,
  "usage": {
    "input_tokens": 120,
    "output_tokens": 68
  }
}

Note that stop_reason is tool_use, indicating that the model needs to call a tool. After receiving this result, you need to execute the tool function and return the result to the model in the form of tool_result:

payload = {
    "model": "claude-sonnet-4-20250514",
    "max_tokens": 1024,
    "tools": [
        {
            "name": "get_weather",
            "description": "Get the current weather in a given location",
            "input_schema": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA"
                    }
                },
                "required": ["location"]
            }
        }
    ],
    "messages": [
        {"role": "user", "content": "What's the weather like in San Francisco?"},
        {
            "role": "assistant",
            "content": [
                {"type": "text", "text": "Let me check the weather in San Francisco for you."},
                {"type": "tool_use", "id": "toolu_01A09q90qw90lq917835lgs", "name": "get_weather", "input": {"location": "San Francisco, CA"}}
            ]
        },
        {
            "role": "user",
            "content": [
                {
                    "type": "tool_result",
                    "tool_use_id": "toolu_01A09q90qw90lq917835lgs",
                    "content": "Sunny, 72°F"
                }
            ]
        }
    ]
}

response = requests.post(url, json=payload, headers=headers)
print(response.json())

The model will generate the final natural language response based on the result returned by the tool.

Differences from the Chat Completion API

Ace Data Cloud provides two Claude API formats. The main differences between them are as follows: For the Messages API, usage.input_tokens only represents uncached input, while cache_read_input_tokens and cache_creation_input_tokens are independently billed buckets; all three are calculated separately according to their respective prices.

Feature	Messages API (`/v1/messages`)	Chat Completion API (`/v1/chat/completions`)
Format	Anthropic native format	OpenAI-compatible format
System prompt	Separate `system` field	Passed through `role: "system"` in `messages`
Response structure	`content` array (supports multiple types)	`choices` array (contains `message`)
Streaming format	SSE events (multiple event types)	SSE `data` lines
Extended thinking	Native `thinking` and `output_config.effort`	Model default strategy and compatibility parameters
Tool use	Native `tools` + `input_schema`	OpenAI-compatible `tools` format
Token statistics	`output_tokens_details.thinking_tokens`	`completion_tokens_details.reasoning_tokens`

If your system has already integrated with an OpenAI-format API, you can use the Chat Completion API for a seamless switch. If you need to use all of Claude’s native capabilities, the Messages API is recommended.

Error Handling

Error responses from public APIs use the Ace Data Cloud platform envelope: error.code is a stable error code, error.message is a description, and trace_id is used for request troubleshooting. Common HTTP statuses include:

400: Request parameters or protocol content is invalid.
401: The authorization token is invalid, missing, or expired.
403: Access is forbidden, the balance is insufficient, or the quota is restricted.
404: The API or model does not exist.
413: The request body is too large.
429: Too many requests.
500 / 503 / 504: Service error, temporarily unavailable, or processing timeout.

Error Response Example

{
  "error": {
    "code": "api_error",
    "message": "fetch failed"
  },
  "trace_id": "2cf86e86-22a4-46e1-ac2f-032c0f2a4e89"
}

This error structure is the runtime contract of Ace Data Cloud and is not equivalent to Anthropic’s official error envelope; please handle it according to the HTTP status and error.code.

Conclusion

Through this document, you have learned how to use the Claude Messages API to invoke Claude’s conversation capabilities in Anthropic’s native format. The Messages API supports rich features such as basic conversations, system prompts, streaming responses, multi-turn conversations, extended thinking, vision understanding, PDFs, prompt caching, and tool use. If you have any questions, please feel free to contact our technical support team.

Getting Started

Core Concepts

Integrations

AI Chat

AI Image

AI Video

AI Audio

Web & Data

CAPTCHA

Identity

Coding

More guides

Claude Messages API Application and Usage

Application Process

Basic Usage

cURL Example

Python Example

System Prompt

Python Example

Streaming Responses

Python Example

JavaScript Example

Multi-turn Conversations

Python Example

Deep Thinking Models

Vision Models

Using Base64-Encoded Images

Using URL Images

cURL Example

Documents and PDFs

Prompt Caching

Tool Use

Python Example

Differences from the Chat Completion API

Error Handling

Error Response Example

Conclusion

​Application Process

​Basic Usage

​cURL Example

​Python Example

​System Prompt

​Python Example

​Streaming Responses

​Python Example

​JavaScript Example

​Multi-turn Conversations

​Python Example

​Deep Thinking Models

​Vision Models

​Using Base64-Encoded Images

​Using URL Images

​cURL Example

​Documents and PDFs

​Prompt Caching

​Tool Use

​Python Example

​Differences from the Chat Completion API

​Error Handling

​Error Response Example

​Conclusion

Application Process

Basic Usage

cURL Example

Python Example

System Prompt

Python Example

Streaming Responses

Python Example

JavaScript Example

Multi-turn Conversations

Python Example

Deep Thinking Models

Vision Models

Using Base64-Encoded Images

Using URL Images

cURL Example

Documents and PDFs

Prompt Caching

Tool Use

Python Example

Differences from the Chat Completion API

Error Handling

Error Response Example

Conclusion