Descripción general
Referencia de la API
Guías de integración
Errores
Precios y facturación
Anthropic Messages
Usa la API Anthropic Messages a través de CometAPI para acceder a modelos Claude con thinking extendido, almacenamiento en caché de prompts, uso de herramientas, búsqueda/obtención web, Streaming y control de esfuerzo.
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
}
}
}Resumen
CometAPI admite la API Anthropic Messages de forma nativa, dándote acceso directo a los modelos Claude con todas las funciones específicas de Anthropic. Usa este endpoint para capacidades exclusivas de Claude, como thinking extendido, almacenamiento en caché de prompts y control de esfuerzo.Inicio rápido
Usa el SDK oficial de Anthropic; solo configura la URL base a 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 como Authorization: Bearer para la autenticación. Los SDK oficiales de Anthropic usan x-api-key de forma predeterminada.Extended Thinking
Activa el razonamiento paso a paso de Claude con el parámetrothinking. La respuesta incluye bloques de contenido thinking que muestran el razonamiento interno de Claude antes de la respuesta final.
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 mínimo de 1,024. Los tokens de thinking cuentan para tu límite de max_tokens; configura max_tokens lo suficientemente alto como para incluir tanto el thinking como la respuesta.Almacenamiento en caché de prompts
Almacena en caché prompts de sistema grandes o prefijos de conversación para reducir la latencia y el costo en solicitudes posteriores. Agregacache_control a los bloques de contenido que deban almacenarse en caché:
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 de la respuesta:
cache_creation_input_tokens— tokens escritos en la caché (facturados a una tarifa más alta)cache_read_input_tokens— tokens leídos desde la caché (facturados a una tarifa reducida)
Streaming
Transmite respuestas usando Server-Sent Events (SSE) estableciendostream: true. Los eventos llegan en este orden:
message_start— contiene los metadatos del mensaje y el uso inicialcontent_block_start— marca el inicio de cada bloque de contenidocontent_block_delta— fragmentos de texto incrementales (text_delta)content_block_stop— marca el final de cada bloque de contenidomessage_delta—stop_reasonfinal yusagecompletomessage_stop— señala el final del stream
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="")
Control de esfuerzo
Controla cuánto esfuerzo pone Claude en generar una respuesta conoutput_config.effort:
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"
)
Herramientas del servidor
Claude admite herramientas del lado del servidor que se ejecutan en la infraestructura de Anthropic:- 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}
],
)
Ejemplo de respuesta
Una respuesta típica del endpoint Anthropic de CometAPI:{
"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
}
}
Diferencias clave con el endpoint compatible con OpenAI
| Característica | Anthropic Messages (/v1/messages) | Compatible con OpenAI (/v1/chat/completions) |
|---|---|---|
| Extended thinking | parámetro thinking con budget_tokens | No disponible |
| Prompt caching | cache_control en bloques de content | No disponible |
| Control de esfuerzo | output_config.effort | No disponible |
| Web fetch/search | Herramientas del servidor (web_fetch, web_search) | No disponible |
| Encabezado de autenticación | x-api-key o Bearer | Solo Bearer |
| Formato de respuesta | Formato Anthropic (bloques content) | Formato OpenAI (choices, message) |
| Modelos | Solo Claude | Multi-provider (GPT, Claude, Gemini, etc.) |
Autorizaciones
Your CometAPI key passed via the x-api-key header. Authorization: Bearer <key> is also supported.
Encabezados
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.
Cuerpo
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 Respuesta
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
}
}
}