LLM Quickstart

Sunra provides four LLM API endpoints for text generation and embeddings. They use the same authentication and base URL (https://api-llm.sunra.ai), so you can pick the format that fits your stack. Before diving in, grab an API key from your dashboard. Use canonical model IDs in requests. Sunra selects a provider automatically when provider is omitted; see Provider routing when you need to choose or prioritize providers.

Chat Completions — `/v1/chat/completions`

The Chat Completions endpoint follows the OpenAI Chat Completions format. It accepts a list of messages with roles (system, user, assistant) and returns a completion. Use this endpoint when you want drop-in compatibility with OpenAI SDKs and tooling. Key features: streaming, function calling, vision (images, audio, video, files), reasoning, structured outputs (JSON schema / grammar), logprobs.

curl -X POST https://api-llm.sunra.ai/v1/chat/completions \
  -H "Authorization: Bearer <SUNRA_KEY>" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "google/gemini-2.5-flash",
    "messages": [
      { "role": "system", "content": "You are a helpful assistant." },
      { "role": "user", "content": "What is the capital of France?" }
    ]
  }'

Anthropic Messages — `/v1/messages`

The Anthropic Messages endpoint follows the Anthropic Messages API format. It uses user / assistant message roles with rich content blocks and a separate system parameter. Use this endpoint when you want native access to Anthropic Claude models and features like extended thinking, prompt caching, citations, and built-in tools (web search, code execution). Key features: streaming, extended thinking, prompt caching, tool use (custom + built-in), PDF/document input, citations, structured outputs.

curl -X POST https://api-llm.sunra.ai/v1/messages \
  -H "Authorization: Bearer <SUNRA_KEY>" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "anthropic/claude-sonnet-4-6",
    "max_tokens": 1024,
    "messages": [
      { "role": "user", "content": "Hello, how are you?" }
    ]
  }'

Responses — `/v1/responses`

The Responses endpoint follows the OpenAI Responses API format. It accepts flexible input items (messages, function calls, reasoning) and returns structured output items. Use this endpoint when you need the latest OpenAI Responses features like built-in web search, file search, code interpreter, computer use, MCP tool integration, or image generation. Key features: streaming, function calling, web search, file search, code interpreter, computer use, MCP tools, image generation, reasoning, structured outputs.

curl -X POST https://api-llm.sunra.ai/v1/responses \
  -H "Authorization: Bearer <SUNRA_KEY>" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "google/gemini-2.5-flash",
    "input": [
      { "type": "message", "role": "user", "content": "Hello, how are you?" }
    ]
  }'

Embeddings — `/v1/embeddings`

The Embeddings endpoint follows the OpenAI Embeddings format. It creates vectors from text and supported media inputs for retrieval, semantic search, clustering, classification, and RAG.

curl -X POST https://api-llm.sunra.ai/v1/embeddings \
  -H "Authorization: Bearer <SUNRA_KEY>" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "google/gemini-embedding-2",
    "input": "Sunra provides one API for AI models."
  }'

Choosing the right endpoint

	Chat Completions	Anthropic Messages	Responses	Embeddings
Format	OpenAI Chat	Anthropic Messages	OpenAI Responses	OpenAI Embeddings
Best for	OpenAI SDK compatibility	Claude-native features	Latest OpenAI features	Search and RAG vectors
Streaming	SSE	SSE	SSE	No
Function calling	Yes	Yes (custom + built-in)	Yes	No
Reasoning	Yes	Extended thinking	Yes	No
Structured output	JSON schema, grammar	JSON schema	JSON schema	Vectors
Built-in tools	—	Web search, code execution	Web search, file search, code interpreter, computer use, MCP	—

All four endpoints share the same authentication. Pass your API key as a Bearer token in the Authorization header.

Overview

Platform

Multimodal

LLM

Client Libraries

Chat Completions — `/v1/chat/completions`

Anthropic Messages — `/v1/messages`

Responses — `/v1/responses`

Embeddings — `/v1/embeddings`

Choosing the right endpoint

​Chat Completions — /v1/chat/completions

​Anthropic Messages — /v1/messages

​Responses — /v1/responses

​Embeddings — /v1/embeddings

​Choosing the right endpoint

Chat Completions — `/v1/chat/completions`

Anthropic Messages — `/v1/messages`

Responses — `/v1/responses`

Embeddings — `/v1/embeddings`

Choosing the right endpoint