Originally published at claudeguide.io/claude-api-streaming-guide

Claude API Streaming: Complete Implementation Guide

Claude API streaming delivers response tokens as they're generated, rather than waiting for the complete response. Streaming reduces perceived latency from 5–30 seconds (waiting for full response) to near-instant first token appearance. For any user-facing application, streaming is the correct default. For batch processing pipelines, non-streaming is simpler and equally fast. This guide covers both patterns with complete Python and TypeScript implementations.

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Why streaming matters for UX

Non-streaming: user clicks Send → waits 8 seconds → full response appears.
Streaming: user clicks Send → first words appear in ~500ms → response builds in real time.

The total time-to-complete is identical. But perceived latency is dramatically lower with streaming. Studies on AI interfaces consistently show that streaming responses have higher user satisfaction and lower abandonment rates.

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Python streaming implementation

Basic streaming

import anthropic

client = anthropic.Anthropic()

with client.messages.stream(
    model="claude-sonnet-4-5",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Explain quantum entanglement simply."}],
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

# After the loop, get the final complete message
final_message = stream.get_final_message()
print(f"\nTotal tokens: {final_message.usage.input_tokens + final_message.usage.output_tokens}")

Async streaming (for FastAPI, async frameworks)

python
import asyncio
import anthropic

client = anthropic.AsyncAnthropic()

async def stream_response(user_message: str) -

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