GPT-4oClaude 3.5 Sonnet

Migrating from GPT-4o to Claude 3.5 Sonnet

8 min read

OpenAIAnthropicAPI MigrationCost Optimization

Claude 3.5 Sonnet outperforms GPT-4o on coding benchmarks at competitive cost. Migrate your production workloads in under an hour with this step-by-step guide.

Why Migrate to Claude 3.5 Sonnet?

Claude 3.5 Sonnet has emerged as the leading choice for production AI workloads, outperforming GPT-4o on several critical benchmarks.

Key advantages:

SWE-bench (coding): 49.0% vs 38.7% (GPT-4o)

Context window: 200K tokens vs 128K tokens

Generally lower effective cost due to higher task accuracy (fewer retries needed)

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Cost Comparison (per 1M tokens)

GPT-4o: $2.50 input, $10.00 output, 128K context

Claude 3.5 Sonnet: $3.00 input, $15.00 output, 200K context

For complex coding or analysis tasks requiring fewer retries, Claude often delivers better cost efficiency.

Step 1: Install Anthropic SDK

Python: pip install anthropic

Node.js: npm install @anthropic-ai/sdk

Step 2: Update Your API Call

BEFORE (OpenAI Python)

from openai import OpenAI
client = OpenAI(api_key="sk-...")
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Your prompt"}]
)
result = response.choices[0].message.content

AFTER (Anthropic Python)

import anthropic
client = anthropic.Anthropic(api_key="sk-ant-...")
response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=2048,
    system="You are a helpful assistant.",
    messages=[{"role": "user", "content": "Your prompt"}]
)
result = response.content[0].text

Key API Differences

System prompts are a separate "system" parameter, not inside the messages array

Response text is at response.content[0].text (not response.choices[0].message.content)

max_tokens is required in Anthropic (not optional like OpenAI)

Stop sequences use stop_sequences parameter (not stop)

Step 3: Update Streaming

BEFORE (OpenAI streaming)

stream = client.chat.completions.create(model="gpt-4o", messages=[...], stream=True)
for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="")

AFTER (Anthropic streaming)

with client.messages.stream(
    model="claude-3-5-sonnet-20241022",
    max_tokens=2048,
    messages=[...]
) as stream:
    for text in stream.text_stream:
        print(text, end="")

Tips for Better Results with Claude

Use XML tags to structure prompts: wrap sections in document, instructions, and output tags

Be explicit about output format: "Respond in JSON with keys: title, summary, tags"

Take advantage of the 200K context window for long documents

For coding tasks, ask Claude to think through the problem before writing code

Claude works best with detailed, explicit instructions rather than brief prompts

Get Your Free Claude Credits

New Anthropic Console accounts receive $5 in free API credits. Use the link in the sidebar to sign up and start testing your migration today.