Claude 3.5 Sonnet: Anthropic's New Benchmark in AI

Anthropic just released Claude 3.5 Sonnet, and the benchmarks are impressive. This model represents a significant leap in capability while maintaining the safety-focused approach Anthropic is known for. For those of us building AI applications on Azure and beyond, this opens new possibilities.

What Makes Claude 3.5 Sonnet Special

Performance That Competes

Claude 3.5 Sonnet outperforms Claude 3 Opus on most benchmarks while being significantly faster and cheaper. It’s positioned as a “goldilocks” model - powerful enough for complex tasks, efficient enough for production use.

Key benchmark highlights:

• Graduate-level reasoning (GPQA): 59.4%
• Undergraduate knowledge (MMLU): 88.7%
• Code generation (HumanEval): 92.0%
• Math problem solving (MATH): 71.1%

Speed Improvements

Claude 3.5 Sonnet runs at roughly 2x the speed of Claude 3 Opus. For real-time applications, this matters enormously.

Getting Started with the API

import anthropic

client = anthropic.Anthropic(
    api_key="your-api-key"
)

message = client.messages.create(
    model="claude-3-5-sonnet-20240620",
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": "Explain the CAP theorem and how it applies to Azure Cosmos DB's consistency levels."
        }
    ]
)

print(message.content[0].text)

Vision Capabilities

Claude 3.5 Sonnet excels at visual understanding:

import anthropic
import base64

def analyze_architecture_diagram(image_path):
    client = anthropic.Anthropic()

    with open(image_path, "rb") as image_file:
        image_data = base64.standard_b64encode(image_file.read()).decode("utf-8")

    message = client.messages.create(
        model="claude-3-5-sonnet-20240620",
        max_tokens=2048,
        messages=[
            {
                "role": "user",
                "content": [
                    {
                        "type": "image",
                        "source": {
                            "type": "base64",
                            "media_type": "image/png",
                            "data": image_data,
                        },
                    },
                    {
                        "type": "text",
                        "text": "Analyze this Azure architecture diagram. Identify potential improvements for scalability and cost optimization."
                    }
                ],
            }
        ],
    )

    return message.content[0].text

# Analyze your architecture
feedback = analyze_architecture_diagram("azure-architecture.png")
print(feedback)

Code Generation Quality

One area where Claude 3.5 Sonnet shines is code generation. Here’s an example of generating Azure Functions:

prompt = """
Create an Azure Function in Python that:
1. Triggers on Blob Storage uploads
2. Reads CSV files and validates the schema
3. Writes valid records to Cosmos DB
4. Logs invalid records to a separate container

Include proper error handling and logging.
"""

message = client.messages.create(
    model="claude-3-5-sonnet-20240620",
    max_tokens=4096,
    messages=[{"role": "user", "content": prompt}]
)

The generated code is typically production-ready with proper exception handling, type hints, and Azure SDK best practices.

Comparison with Previous Models

Capability	Claude 3 Haiku	Claude 3 Sonnet	Claude 3.5 Sonnet	Claude 3 Opus
Speed	Fastest	Fast	Fast	Slower
Reasoning	Basic	Good	Excellent	Excellent
Code	Good	Good	Excellent	Excellent
Vision	Good	Good	Excellent	Excellent
Cost	Lowest	Medium	Medium	Highest

Practical Use Cases

1. Data Pipeline Documentation

def document_pipeline(pipeline_code):
    prompt = f"""
    Analyze this data pipeline code and generate:
    1. A high-level overview
    2. Data flow diagram in Mermaid syntax
    3. Potential failure points and mitigations
    4. Performance optimization suggestions

    Code:
    {pipeline_code}
    """

    message = client.messages.create(
        model="claude-3-5-sonnet-20240620",
        max_tokens=4096,
        messages=[{"role": "user", "content": prompt}]
    )

    return message.content[0].text

2. SQL Query Optimization

def optimize_query(sql_query, table_schemas):
    prompt = f"""
    Optimize this SQL query for Azure Synapse Analytics:

    Query:
    {sql_query}

    Table Schemas:
    {table_schemas}

    Consider:
    - Distribution strategies
    - Indexing opportunities
    - Query plan improvements
    - Cost reduction
    """

    return client.messages.create(
        model="claude-3-5-sonnet-20240620",
        max_tokens=2048,
        messages=[{"role": "user", "content": prompt}]
    ).content[0].text

3. Error Analysis

def analyze_error(error_log, context):
    prompt = f"""
    Analyze this error from our Azure data pipeline:

    Error:
    {error_log}

    Context:
    {context}

    Provide:
    1. Root cause analysis
    2. Immediate fix
    3. Long-term prevention strategy
    """

    return client.messages.create(
        model="claude-3-5-sonnet-20240620",
        max_tokens=2048,
        messages=[{"role": "user", "content": prompt}]
    ).content[0].text

Safety and Alignment

Anthropic’s Constitutional AI approach means Claude 3.5 Sonnet:

• Refuses harmful requests clearly
• Provides balanced perspectives
• Acknowledges uncertainty appropriately
• Avoids generating misleading information

For enterprise use, this translates to more predictable behavior and fewer edge cases to handle.

What This Means for Azure Developers

Claude 3.5 Sonnet is available through Amazon Bedrock, and Anthropic has announced plans for Azure availability. In the meantime, you can:

1. Use the direct Anthropic API
2. Build abstraction layers that support multiple providers
3. Prepare your pipelines for model flexibility

My Take

Claude 3.5 Sonnet hits a sweet spot. It’s capable enough to handle complex reasoning and code generation, fast enough for interactive applications, and priced reasonably for production use.

For data engineering tasks specifically - documentation, query optimization, error analysis - it performs excellently. The improved vision capabilities also open up interesting possibilities for processing diagrams, charts, and visual data.

Start experimenting. The model is available now, and the API is straightforward. The AI landscape keeps advancing, and staying current with these capabilities is essential.