You call Claude Opus 4.6 via the Anthropic Messages API, by sending a POST request with your API key, the model field set to the Opus 4.6 model ID, and a message list (or prompt content) describing the task. The Messages API is Anthropic’s primary interface for building applications, and it supports standard patterns you’ll likely want in production: multi-turn messages, tool calling, and streaming. Start from Anthropic’s model docs to confirm the model and capabilities, then use the Messages API endpoint and payload structure described in the platform documentation. The relevant official pages are: What’s new in Claude 4.6 and the broader API docs (including streaming): Streaming responses.
A minimal “hello world” call looks like this (replace the API key; adjust max_tokens to your needs). This example shows the core shape most teams use: specify the model, set an output token cap, and provide user messages.
curl https://api.anthropic.com/v1/messages \
-H "x-api-key: $ANTHROPIC_API_KEY" \
-H "anthropic-version: 2023-06-01" \
-H "content-type: application/json" \
-d '{
"model": "claude-opus-4-6",
"max_tokens": 800,
"messages": [
{ "role": "user", "content": "Explain what this function does and suggest one safe refactor." }
]
}'
For production usage, you’ll typically add: (1) a system instruction for output format (“Return Markdown with sections…”), (2) tool definitions if you want tool calling, and (3) a validator on the client side to enforce structured output (JSON schema, required headings, diff-only output, etc.). If you need real-time UX, you’ll set streaming (covered in Anthropic’s streaming docs): Streaming responses.
If your use case involves internal documentation or large knowledge bases, don’t shove everything into messages. Instead, integrate retrieval: store content in Milvus or Zilliz Cloud, retrieve the top-k chunks for a query, and pass only those chunks to Claude as a ## Context section. Then enforce a strict system rule: “Answer using only Context; if missing, say you don’t know.” This keeps costs predictable (shorter prompts), improves accuracy (grounded answers), and makes behavior debuggable (you can trace which retrieved chunks drove the answer). With Opus 4.6, the API call is straightforward; the quality comes from the retrieval + validation system you wrap around it.