When migrating from from Text Completions to Messages, consider the following changes.

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Inputs and outputs

The largest change between Text Completions and the Messages is the way in which you specify model inputs and receive outputs from the model.

With Text Completions, inputs are raw strings:

Python
prompt = "\n\nHuman: Hello there\n\nAssistant: Hi, I'm Claude. How can I help?\n\nHuman: Can you explain Glycolysis to me?\n\nAssistant:"

With Messages, you specify a list of input messages instead of a raw prompt:

Each input message has a role and content.

Role names

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:

Python
>>> 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:

Python
>>> response = anthropic.messages.create(...)
>>> response.content
[{"type": "text", "text": "Hi, I'm Claude"}]

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Putting words in Claude’s mouth

With Text Completions, you can pre-fill part of Claude’s response:

Python
prompt = "\n\nHuman: Hello\n\nAssistant: Hello, my name is"

With Messages, you can achieve the same result by making the last input message have the assistant role:

Python
messages = [
  {"role": "human", "content": "Hello"},
  {"role": "assistant", "content": "Hello, my name is"},
]

When doing so, response content will continue from the last input message content:

JSON
{
  "role": "assistant",
  "content": [{"type": "text", "text": " Claude. How can I assist you today?" }],
  ...
}

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System prompt

With Text Completions, the system prompt is specified by adding text before the first \n\nHuman: turn:

Python
prompt = "Today is January 1, 2024.\n\nHuman: Hello, Claude\n\nAssistant:"

With Messages, you specify the system prompt with the system parameter:

Python
anthropic.Anthropic().messages.create(
    model="claude-3-opus-20240229",
    max_tokens=1024,
    system="Today is January 1, 2024.", # <-- system prompt
    messages=[
        {"role": "user", "content": "Hello, Claude"}
    ]
)

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Model names

The Messages API requires that you specify the full model version (e.g. claude-3-opus-20240229).

We previously supported specifying only the major version number (e.g. claude-2), which resulted in automatic upgrades to minor versions. However, we no longer recommend this integration pattern, and Messages do not support it.

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Stop reason

Text Completions always have a stop_reason of either:

  • "stop_sequence": The model either ended its turn naturally, or one of your custom stop sequences was generated.
  • "max_tokens": Either the model generated your specified max_tokens of content, or it reached its absolute maximum.

Messages have a stop_reason of one of the following values:

  • "end_turn": The conversational turn ended naturally.
  • "stop_sequence": One of your specified custom stop sequences was generated.
  • "max_tokens": (unchanged)

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Specifying max tokens

  • Text Completions: max_tokens_to_sample parameter. No validation, but capped values per-model.
  • Messages: max_tokens parameter. If passing a value higher than the model supports, returns a validation error.

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Streaming format

When using "stream": true in with Text Completions, the response included any of completion, ping, and error server-sent-events. See Text Completions streaming for details.

Messages can contain multiple content blocks of varying types, and so its streaming format is somewhat more complex. See Messages streaming for details.

Streaming MessagesMessages examples