Anthropic 消息
通过 CometAPI 使用 Anthropic Messages API，访问支持扩展思考、Prompt 缓存、工具使用、网页搜索/抓取、流式输出以及 effort control 的 Claude 模型。
import anthropic
client = anthropic.Anthropic(
base_url="https://api.cometapi.com",
api_key="<COMETAPI_KEY>",
)
message = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
system="You are a helpful assistant.",
messages=[
{"role": "user", "content": "Hello, world"}
],
)
print(message.content[0].text){
"id": "<string>",
"type": "message",
"role": "assistant",
"content": [
{
"type": "text",
"text": "<string>",
"thinking": "<string>",
"signature": "<string>",
"id": "<string>",
"name": "<string>",
"input": {}
}
],
"model": "<string>",
"stop_reason": "end_turn",
"stop_sequence": "<string>",
"usage": {
"input_tokens": 123,
"output_tokens": 123,
"cache_creation_input_tokens": 123,
"cache_read_input_tokens": 123,
"cache_creation": {
"ephemeral_5m_input_tokens": 123,
"ephemeral_1h_input_tokens": 123
}
}
}概述
CometAPI 原生支持 Anthropic Messages API，让你可以直接访问具备全部 Anthropic 专属功能的 Claude 模型。对于扩展思考、Prompt 缓存和 effort control 等 Claude 独有能力，请使用此端点。快速开始
使用官方 Anthropic SDK——只需将 base URL 设置为 CometAPI：import anthropic
client = anthropic.Anthropic(
base_url="https://api.cometapi.com",
api_key="<COMETAPI_KEY>",
)
message = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
messages=[{"role": "user", "content": "Hello!"}],
)
print(message.content[0].text)
x-api-key 和 Authorization: Bearer 请求头进行身份验证。官方 Anthropic SDK 默认使用 x-api-key。扩展思考
使用thinking 参数启用 Claude 的分步推理。响应中会包含 thinking 内容块，在最终答案前展示 Claude 的内部推理过程。
message = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=16000,
thinking={
"type": "enabled",
"budget_tokens": 10000,
},
messages=[
{"role": "user", "content": "Prove that there are infinitely many primes."}
],
)
for block in message.content:
if block.type == "thinking":
print(f"Thinking: {block.thinking[:200]}...")
elif block.type == "text":
print(f"Answer: {block.text}")
budget_tokens 的最小值为 1,024。Thinking 所消耗的 Token 会计入你的 max_tokens 限制，因此请将 max_tokens 设置得足够高，以同时容纳 thinking 和响应内容。Prompt 缓存
缓存较大的 system prompt 或对话前缀，以降低后续请求的延迟和成本。为需要缓存的内容块添加cache_control：
message = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
system=[
{
"type": "text",
"text": "You are an expert code reviewer. [Long detailed instructions...]",
"cache_control": {"type": "ephemeral"},
}
],
messages=[{"role": "user", "content": "Review this code..."}],
)
usage 字段中返回：
cache_creation_input_tokens— 写入缓存的 tokens（按更高费率计费）cache_read_input_tokens— 从缓存读取的 tokens（按较低费率计费）
流式输出（Streaming）
通过设置stream: true，使用 Server-Sent Events (SSE) 进行流式响应。事件会按以下顺序到达：
message_start— 包含消息元数据和初始 usagecontent_block_start— 标记每个内容块的开始content_block_delta— 增量文本片段（text_delta）content_block_stop— 标记每个内容块的结束message_delta— 最终的stop_reason和完整的usagemessage_stop— 表示流结束
with client.messages.stream(
model="claude-sonnet-4-6",
max_tokens=256,
messages=[{"role": "user", "content": "Hello"}],
) as stream:
for text in stream.text_stream:
print(text, end="")
努力程度控制
使用output_config.effort 控制 Claude 在生成响应时投入多少计算努力：
message = client.messages.create(
model="claude-opus-4-6",
max_tokens=4096,
messages=[
{"role": "user", "content": "Summarize this briefly."}
],
output_config={"effort": "low"}, # "low", "medium", or "high"
)
服务器工具
Claude 支持运行在 Anthropic 基础设施上的服务端工具：- Web Fetch
- Web Search
message = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
messages=[
{"role": "user", "content": "Analyze the content at https://arxiv.org/abs/1512.03385"}
],
tools=[
{"type": "web_fetch_20250910", "name": "web_fetch", "max_uses": 5}
],
)
message = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
messages=[
{"role": "user", "content": "What are the latest developments in AI?"}
],
tools=[
{"type": "web_search_20250305", "name": "web_search", "max_uses": 5}
],
)
响应示例
来自 CometAPI 的 Anthropic 端点的典型响应：{
"id": "msg_bdrk_01UjHdmSztrL7QYYm7CKBDFB",
"type": "message",
"role": "assistant",
"content": [
{
"type": "text",
"text": "Hello!"
}
],
"model": "claude-sonnet-4-6",
"stop_reason": "end_turn",
"stop_sequence": null,
"usage": {
"input_tokens": 19,
"cache_creation_input_tokens": 0,
"cache_read_input_tokens": 0,
"cache_creation": {
"ephemeral_5m_input_tokens": 0,
"ephemeral_1h_input_tokens": 0
},
"output_tokens": 4
}
}
与 OpenAI-Compatible 端点的关键区别
| 功能 | Anthropic Messages (/v1/messages) | OpenAI-Compatible (/v1/chat/completions) |
|---|---|---|
| 扩展思考 | 带有 budget_tokens 的 thinking 参数 | 不可用 |
| Prompt 缓存 | 内容块上的 cache_control | 不可用 |
| 努力程度控制 | output_config.effort | 不可用 |
| Web 抓取/搜索 | 服务器工具（web_fetch, web_search） | 不可用 |
| 认证请求头 | x-api-key 或 Bearer | 仅 Bearer |
| 响应格式 | Anthropic 格式（content 块） | OpenAI 格式（choices, message） |
| 模型 | 仅 Claude | 多提供商（GPT、Claude、Gemini 等） |
授权
Your CometAPI key passed via the x-api-key header. Authorization: Bearer <key> is also supported.
请求头
The Anthropic API version to use. Defaults to 2023-06-01.
"2023-06-01"
Comma-separated list of beta features to enable. Examples: max-tokens-3-5-sonnet-2024-07-15, pdfs-2024-09-25, output-128k-2025-02-19.
请求体
The Claude model to use. See the Models page for current Claude model IDs.
"claude-sonnet-4-6"
The conversation messages. Must alternate between user and assistant roles. Each message's content can be a string or an array of content blocks (text, image, document, tool_use, tool_result). There is a limit of 100,000 messages per request.
Show child attributes
Show child attributes
The maximum number of tokens to generate. The model may stop before reaching this limit. When using thinking, the thinking tokens count towards this limit.
x >= 11024
System prompt providing context and instructions to Claude. Can be a plain string or an array of content blocks (useful for prompt caching).
Controls randomness in the response. Range: 0.0–1.0. Use lower values for analytical tasks and higher values for creative tasks. Defaults to 1.0.
0 <= x <= 1Nucleus sampling threshold. Only tokens with cumulative probability up to this value are considered. Range: 0.0–1.0. Use either temperature or top_p, not both.
0 <= x <= 1Only sample from the top K most probable tokens. Recommended for advanced use cases only.
x >= 0If true, stream the response incrementally using Server-Sent Events (SSE). Events include message_start, content_block_start, content_block_delta, content_block_stop, message_delta, and message_stop.
Custom strings that cause the model to stop generating when encountered. The stop sequence is not included in the response.
Enable extended thinking — Claude's step-by-step reasoning process. When enabled, the response includes thinking content blocks before the answer. Requires a minimum budget_tokens of 1,024.
Show child attributes
Show child attributes
Tools the model may use. Supports client-defined functions, web search (web_search_20250305), web fetch (web_fetch_20250910), code execution (code_execution_20250522), and more.
Show child attributes
Show child attributes
Controls how the model uses tools.
Show child attributes
Show child attributes
Request metadata for tracking and analytics.
Show child attributes
Show child attributes
Configuration for output behavior.
Show child attributes
Show child attributes
The service tier to use. auto tries priority capacity first, standard_only uses only standard capacity.
auto, standard_only 响应
Successful response. When stream is true, the response is a stream of SSE events.
Unique identifier for this message (e.g., msg_01XFDUDYJgAACzvnptvVoYEL).
Always message.
message Always assistant.
assistant The response content blocks. May include text, thinking, tool_use, and other block types.
Show child attributes
Show child attributes
The specific model version that generated this response (e.g., claude-sonnet-4-6).
Why the model stopped generating.
end_turn, max_tokens, stop_sequence, tool_use, pause_turn The stop sequence that caused the model to stop, if applicable.
Token usage statistics.
Show child attributes
Show child attributes
import anthropic
client = anthropic.Anthropic(
base_url="https://api.cometapi.com",
api_key="<COMETAPI_KEY>",
)
message = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
system="You are a helpful assistant.",
messages=[
{"role": "user", "content": "Hello, world"}
],
)
print(message.content[0].text){
"id": "<string>",
"type": "message",
"role": "assistant",
"content": [
{
"type": "text",
"text": "<string>",
"thinking": "<string>",
"signature": "<string>",
"id": "<string>",
"name": "<string>",
"input": {}
}
],
"model": "<string>",
"stop_reason": "end_turn",
"stop_sequence": "<string>",
"usage": {
"input_tokens": 123,
"output_tokens": 123,
"cache_creation_input_tokens": 123,
"cache_read_input_tokens": 123,
"cache_creation": {
"ephemeral_5m_input_tokens": 123,
"ephemeral_1h_input_tokens": 123
}
}
}