How to Use Claude Sonnet 5.5 API for Coding

Claude Sonnet 5.5 arrived on September 28, 2026 as a faster, more efficient model for well-scoped coding work. It runs more than 30% faster than Sonnet 5 and typically uses fewer tokens, which lowers the cost of routine tasks.

Developers who already use Claude for bug fixes, code reviews, or document generation can switch with a single model ID change. This guide walks through API setup, a first coding call, effort-level control, and practical tips drawn only from Anthropic’s official materials.

What Claude Sonnet 5.5 Offers for Coding

Sonnet 5.5 scores 70.6% on Terminal-Bench 4.0 compared with Sonnet 5’s 10.3%. Early testers noted stronger judgment on system design and fewer unnecessary tool calls.

It accepts text and images and returns text. The context window is 1 million tokens and the maximum output is 128,000 tokens. Pricing stays at $2 per million input tokens and $10 per million output tokens, matching Sonnet 5, while cache reads drop to $0.10 per million.

For most everyday coding, it sits between Haiku 5.5 (high volume, lowest cost) and Opus 5.5 (complex judgment). If you already run high-volume classification with Haiku, keep that workflow and route mid-complexity edits here.

Set Up API Access

Create or log into an account at the Anthropic Console. Generate an API key and store it as an environment variable named ANTHROPIC_API_KEY. Never commit the key to source control.

Install the official Python SDK:

pip install -U anthropic

The same key works for the TypeScript SDK and raw HTTP requests. Anthropic’s docs recommend the Python or TypeScript libraries for most applications because they handle retries and streaming.

Make Your First Coding Call

The model ID is claude-sonnet-5-5. A minimal request that asks the model to explain a small function looks like this:

import anthropic

client = anthropic.Anthropic()

message = client.messages.create(
    model="claude-sonnet-5-5",
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": "Explain what this Python function does and suggest one improvement: def add(a, b): return a + b"
        }
    ]
)

print(message.content[0].text)

The response arrives as a list of content blocks. For plain text, extract the first text block. Adaptive thinking is on by default; the model decides how much reasoning to perform before answering.

You can also send the same request with curl:

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-sonnet-5-5",
    "max_tokens": 1024,
    "messages": [{"role": "user", "content": "Explain this function..."}]
  }'

Control Effort for Cost and Quality

Sonnet 5.5 supports an effort parameter that trades speed and token use against thoroughness. In the Claude Platform the default is high. Lower values produce faster, cheaper answers that still beat Sonnet 5 on many coding benchmarks.

Pass the setting inside output_config:

message = client.messages.create(
    model="claude-sonnet-5-5",
    max_tokens=2048,
    output_config={"effort": "medium"},
    messages=[{"role": "user", "content": "Refactor this function to remove the N+1 query."}]
)

Valid levels include low, medium, high, and higher. For routine bug fixes or documentation, medium or low often suffices. Raise the level only when the task involves multi-file architecture or subtle edge cases.

Temperature, top_p, and top_k are no longer accepted; the API returns a 400 error if you send them.

Tips for Stronger Coding Results

Give the model the exact files or snippets it needs rather than a vague description of the problem. Include the expected behavior and any constraints (language version, style guide, test command).

Ask it to verify its own output when possible. For example, “After you write the patch, list the tests you would run.” Early testers at Unity reported that the majority of Sonnet 5.5 changes passed runtime checks.

Cache long system prompts or shared code context. Cache reads cost only $0.10 per million tokens, so repeated calls against the same large codebase become inexpensive.

For image-based tasks such as reviewing a UI screenshot or a diagram, attach the image in the content array. The model handles both text and images in the same request.

If you need even lower cost for high-volume subtasks, route those to Claude Haiku 5.5. See our guide on how to use Claude Haiku 5.5 for high-volume tasks.

Watch the Official Introduction

Conclusion

Claude Sonnet 5.5 gives developers a practical middle ground: faster responses and lower token use than Sonnet 5 while remaining capable enough for most daily coding work. Start with the model ID claude-sonnet-5-5, set effort to medium for routine tasks, and raise it only when the problem demands deeper reasoning.

Verify results against your own test suite before merging. The combination of clear prompts, effort control, and prompt caching keeps both latency and cost predictable.

Sources

• Anthropic, “Introducing Claude Sonnet 5.5,” September 28, 2026.
• Claude Platform Docs, “Claude Sonnet 5.5,” overview page.
• Official YouTube announcement from the Claude channel.