Code beginner · 3 min read

How to use Claude 3 Opus in python

Q: How to use Claude 3 Opus in python

Use the anthropic Python SDK to create a client with your API key and call client.messages.create specifying model="claude-3-opus-20240229" along with your messages and system prompt.

Direct answer

Use the anthropic Python SDK to create a client with your API key and call client.messages.create specifying model="claude-3-opus-20240229" along with your messages and system prompt.

Setup

Install

bash

pip install anthropic

Env vars

ANTHROPIC_API_KEY

Imports

python

import anthropic
import os

Examples

inTell me a joke about computers.

outWhy do programmers prefer dark mode? Because light attracts bugs!

inExplain the benefits of using AI in healthcare.

outAI in healthcare improves diagnostics accuracy, personalizes treatment plans, and enhances patient monitoring efficiency.

inSummarize the plot of 'The Great Gatsby'.

outThe Great Gatsby tells the story of Jay Gatsby's pursuit of wealth and love in 1920s America, highlighting themes of decadence and the American Dream.

Integration steps

Install the Anthropic Python SDK and set your API key in the environment variable ANTHROPIC_API_KEY.
Import the anthropic module and initialize the client with your API key from os.environ.
Prepare your conversation messages as a list of dictionaries with roles and content.
Call client.messages.create with model set to 'claude-3-opus-20240229', passing system prompt, messages, and max_tokens.
Extract the generated text from the response's content field.
Use or display the returned text as needed in your application.

Full code

python

import anthropic
import os

client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])

response = client.messages.create(
    model="claude-3-opus-20240229",
    max_tokens=500,
    system="You are a helpful assistant.",
    messages=[{"role": "user", "content": "Explain the benefits of AI in healthcare."}]
)

print("Claude 3 Opus response:")
print(response.content[0].text)

output

Claude 3 Opus response:
AI in healthcare improves diagnostics accuracy, personalizes treatment plans, and enhances patient monitoring efficiency.

API trace

Request

json

{"model": "claude-3-opus-20240229", "max_tokens": 500, "system": "You are a helpful assistant.", "messages": [{"role": "user", "content": "Explain the benefits of AI in healthcare."}]}

Response

json

{"id": "chatcmpl-xxx", "object": "chat.completion", "created": 1680000000, "model": "claude-3-opus-20240229", "choices": [{"index": 0, "message": {"role": "assistant", "content": ["AI in healthcare improves diagnostics accuracy, personalizes treatment plans, and enhances patient monitoring efficiency."]}, "finish_reason": "stop"}], "usage": {"prompt_tokens": 30, "completion_tokens": 40, "total_tokens": 70}}

Extractresponse.content[0].text

Variants

Streaming response ›

Use streaming to display partial results immediately for better user experience with long responses.

python

import anthropic
import os

client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])

stream = client.messages.create(
    model="claude-3-opus-20240229",
    max_tokens=500,
    system="You are a helpful assistant.",
    messages=[{"role": "user", "content": "Tell me a story about space exploration."}],
    stream=True
)

print("Streaming Claude 3 Opus response:")
for chunk in stream:
    print(chunk.content[0].text, end='', flush=True)

Async version ›

Use async calls to handle multiple concurrent requests efficiently in asynchronous Python applications.

python

import anthropic
import os
import asyncio

async def main():
    client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])
    response = await client.messages.acreate(
        model="claude-3-opus-20240229",
        max_tokens=500,
        system="You are a helpful assistant.",
        messages=[{"role": "user", "content": "What is quantum computing?"}]
    )
    print("Async Claude 3 Opus response:")
    print(response.content[0].text)

asyncio.run(main())

Alternative model: Claude 3 Sonnet ›

Use Claude 3 Sonnet for improved coding and reasoning tasks or when you want a more advanced model.

python

import anthropic
import os

client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])

response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=500,
    system="You are a helpful assistant.",
    messages=[{"role": "user", "content": "Summarize the latest AI trends."}]
)

print("Claude 3 Sonnet response:")
print(response.content[0].text)

Performance

Latency~1.2 seconds for typical 300-token completion

Cost~$0.003 per 500 tokens

Rate limitsTier 1: 300 requests per minute / 20,000 tokens per minute

Use concise prompts to reduce token usage.
Limit <code>max_tokens</code> to avoid unexpectedly long completions.
Reuse context efficiently by summarizing prior conversation.

Approach	Latency	Cost/call	Best for
Standard call	~1.2s	~$0.003/500 tokens	General purpose completions
Streaming	~0.5s initial + stream	~$0.003/500 tokens	Interactive UIs with long outputs
Async	~1.2s per call	~$0.003/500 tokens	Concurrent multiple requests
Claude 3 Sonnet	~1.5s	~$0.004/500 tokens	Complex reasoning and coding tasks

✓

Quick tip

Always set a clear system prompt with <code>system=</code> to guide Claude 3 Opus's behavior effectively.

⚠

Common mistake

Beginners often forget to use the <code>system=</code> parameter and instead try to include system instructions as a message role, which Claude does not support.

Verified 2026-04 · claude-3-opus-20240229, claude-3-5-sonnet-20241022

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