Role namesThe Text Completions API expects alternating \n\nHuman: and \n\nAssistant: turns, but the Messages API expects user and assistant roles. You may see documentation referring to either “human” or “user” turns. These refer to the same role, and will be “user” going forward. With Text Completions, the model’s generated text is returned in the completion values of the response: ... >>> response = anthropic.completions.create(...) >>> response.completion " Hi, I'm Claude" With Messages, the response is the content value, which is a list of content blocks:

Use the Text Completion API for single-turn text generation from a user supplied prompt. For example, you can use the Text Completion API to generate text for a blog post or to summarize text input from a user. For information about creating prompts for Anthropic Claude models, see Introduction to prompt design

For Text Completions, this is always "completion". ... curl https://api.anthropic.com/v1/complete \ -H 'Content-Type: application/json' \ -H "X-Api-Key: $ANTHROPIC_API_KEY" \ -d '{ "max_tokens_to_sample": 256, "model": "claude-opus-4-5-20251101", "prompt": "\\n\\nHuman: Hello, world!\\n\\nAssistant:" }'

API Keys · Usage Report · Cost Report · Completions · Create a Text Completion · Support & configuration · Rate limitsService tiersVersionsIP addressesSupported regionsOpenAI SDK compatibility · Console · Log in · API Reference · Completions · Copy page · completions().create(CompletionCreateParamsparams, RequestOptionsrequestOptions = RequestOptions.none()) : Completion ·

LiteLLM supports MCP tool calling with Anthropic in the OpenAI Responses API format. ... import os from litellm import completion os.environ["ANTHROPIC_API_KEY"] = "sk-ant-..." tools=[ { "type": "mcp", "server_label": "deepwiki", "server_url": "https://mcp.deepwiki.com/mcp", "require_approval": "never", }, ] response = completion( model="anthropic/claude-sonnet-4-20250514", messages=[{"role": "user", "content": "Who won the World Cup in 2022?"}], tools=tools )

Create a Text Completion · TypeScript · import Anthropic from '@anthropic-ai/sdk'; const client = new Anthropic({ apiKey: 'my-anthropic-api-key', }); const completion = await client.completions.create({ max_tokens_to_sample: 256, model: 'claude-opus-4-5-20251101', prompt: '\n\nHuman: Hello, world!\n\nAssistant:', }); console.log(completion.id); Response 200 ·

The Text Completions API expects alternating \n\nHuman: and \n\nAssistant: turns, but the Messages API expects user and assistant roles. You may see documentation referring to either “human” or “user” turns. These refer to the same role, and will be “user” going forward. With Text Completions, the model’s generated text is returned in the completion values of the response: ... >>> response = anthropic.messages.create(...) >>> response.content [{"type": "text", "text": "Hi, I'm Claude"}]

in the Anthropic Claude documentation. If you have existing Text Completion prompts that you want to migrate to the messages API, see Migrating from Text Completions

This endpoint is idempotent and can be used to poll for Message Batch completion. To access the results of a Message Batch, make a request to the…

curl https://api.anthropic.com/v1/messages \ --header "x-api-key: $ANTHROPIC_API_KEY" \ --header "anthropic-version: 2023-06-01" \ --header "content-type: application/json" \ --data \ '{ "model": "claude-sonnet-4-5", "max_tokens": 20000, "stream": true, "thinking": { "type": "enabled", "budget_tokens": 16000 }, "messages": [ { "role": "user", "content": "What is 27 * 453?"

June 26, 2024 - To learn more about Claude Sonnet and how it compares to ChatGPT, check out the articles below: ... The Text Completions API provides basic text completion functionality, while the Messages API offers more advanced features, such as the ability to have multi-turn conversations, incorporate ...

Text completions: Build advanced chat bots or text processors. Function Calling: Utilize tools for specific tasks and API calling.

This step enables prompts to be submitted to Anthropic's large language model (LLM), which will return the response that the model provides. Users can either add prompts directly or pull them from the Flow. Additionally, users can select the specific model to review the prompt.

In the inference call, fill the body field with a JSON object that conforms the type call you want to make, Anthropic Claude Text Completions API or Anthropic Claude Messages API.

Please follow these steps to complete the task: – Carefully read and analyze the ticket content. – Consider how the content relates to each of the available categories. – Choose the most appropriate category for the ticket.

[migration guide](https://docs.anthropic.com/en/api/migrating-from-text-completions-to-messages)