Claude 3 Released: Anthropic's Most Capable AI Model Family

Today, Anthropic released Claude 3 - their most capable AI model family yet. With three models (Opus, Sonnet, and Haiku), Anthropic is directly challenging OpenAI’s dominance. Having tested it extensively, I can confirm: this is a significant moment in AI.

The Claude 3 Family

Anthropic released three models targeting different use cases:

Model	Context	Strengths	Pricing (Input/Output per 1M tokens)
Opus	200K	Most intelligent, complex tasks	$15 / $75
Sonnet	200K	Balanced performance/cost	$3 / $15
Haiku	200K	Fastest, most affordable	$0.25 / $1.25

Key Capabilities

1. Vision (Multimodal)

All three Claude 3 models can understand images:

import anthropic

client = anthropic.Anthropic()

# Claude 3 can analyze images
message = client.messages.create(
    model="claude-3-opus-20240229",
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "source": {
                        "type": "base64",
                        "media_type": "image/png",
                        "data": image_data,  # base64 encoded
                    },
                },
                {
                    "type": "text",
                    "text": "Analyze this architecture diagram. What are the potential bottlenecks?"
                }
            ],
        }
    ],
)

print(message.content[0].text)

2. 200K Context Window

All models support 200K tokens - roughly 150,000 words:

# Process entire codebases or long documents
def analyze_codebase(files: list[str]) -> str:
    """Analyze an entire codebase with Claude 3"""

    combined_code = "\n\n".join([
        f"# File: {f['name']}\n{f['content']}"
        for f in files
    ])

    message = client.messages.create(
        model="claude-3-opus-20240229",
        max_tokens=4096,
        messages=[
            {
                "role": "user",
                "content": f"""Analyze this codebase and provide:
1. Architecture overview
2. Code quality assessment
3. Security concerns
4. Performance recommendations

Codebase:
{combined_code}"""
            }
        ],
    )

    return message.content[0].text

3. Improved Accuracy and Reasoning

Claude 3 Opus achieves benchmark-leading performance:

# Claude 3 Opus excels at complex reasoning
def solve_complex_problem(problem: str) -> str:
    message = client.messages.create(
        model="claude-3-opus-20240229",
        max_tokens=8192,
        messages=[
            {
                "role": "user",
                "content": f"""Think through this problem step by step:

{problem}

Show your reasoning process clearly."""
            }
        ],
    )

    return message.content[0].text

# Example: Mathematical reasoning
result = solve_complex_problem("""
A company has three warehouses. Warehouse A can ship 100 units/day at $5/unit,
Warehouse B can ship 150 units/day at $4/unit, and Warehouse C can ship 80 units/day at $6/unit.
Customer X needs 200 units and Customer Y needs 130 units.
What's the optimal shipping strategy to minimize cost?
""")

4. Reduced Hallucinations

Claude 3 is more likely to say “I don’t know” rather than fabricate:

# Claude 3 has better calibration on uncertainty
message = client.messages.create(
    model="claude-3-sonnet-20240229",
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": "What happened at the fictional company XYZ Corp's board meeting on January 15, 2024?"
        }
    ],
)

# Claude 3 will acknowledge it doesn't have this information
# rather than making up a plausible-sounding response

Model Selection Guide

When to Use Opus

# Complex analysis, research, coding tasks
OPUS_USE_CASES = [
    "Complex code generation and review",
    "Research synthesis across many sources",
    "Strategic analysis and recommendations",
    "Advanced mathematical reasoning",
    "Nuanced content creation",
]

# Example: Code review with deep analysis
def deep_code_review(code: str, context: str) -> str:
    return client.messages.create(
        model="claude-3-opus-20240229",
        max_tokens=4096,
        messages=[{
            "role": "user",
            "content": f"""Perform a comprehensive code review:

Context: {context}

Code:
{code}

Analyze:
1. Correctness and edge cases
2. Performance implications
3. Security vulnerabilities
4. Maintainability concerns
5. Suggested refactoring"""
        }]
    ).content[0].text

When to Use Sonnet

# Balanced workloads - good performance at reasonable cost
SONNET_USE_CASES = [
    "Customer support automation",
    "Content summarization",
    "Data extraction and transformation",
    "Standard code generation",
    "Interactive applications",
]

# Example: Customer support
def handle_support_query(query: str, history: list) -> str:
    messages = [{"role": m["role"], "content": m["content"]} for m in history]
    messages.append({"role": "user", "content": query})

    return client.messages.create(
        model="claude-3-sonnet-20240229",
        max_tokens=1024,
        system="You are a helpful customer support agent. Be concise and helpful.",
        messages=messages
    ).content[0].text

When to Use Haiku

# High-volume, low-latency tasks
HAIKU_USE_CASES = [
    "Real-time classification",
    "Simple Q&A",
    "Data labeling",
    "Quick summarization",
    "Input validation",
]

# Example: Real-time classification
def classify_intent(text: str) -> str:
    return client.messages.create(
        model="claude-3-haiku-20240307",
        max_tokens=50,
        messages=[{
            "role": "user",
            "content": f"""Classify this customer message intent:
"{text}"

Categories: billing, technical_support, general_inquiry, complaint, feedback
Respond with only the category name."""
        }]
    ).content[0].text

# Haiku is fast enough for real-time applications
# Can handle thousands of classifications per minute

Comparison with GPT-4

Capability	Claude 3 Opus	GPT-4 Turbo
Context Window	200K	128K
Vision	Yes	Yes
Reasoning	Excellent	Excellent
Code Generation	Excellent	Excellent
Following Instructions	Superior	Very Good
Refusing Harmful Requests	Superior	Good
Pricing (Input/1M)	$15	$10
Pricing (Output/1M)	$75	$30

Migration from Claude 2

API Changes

# Claude 2 style (old)
response = anthropic.completions.create(
    model="claude-2",
    prompt=f"\n\nHuman: Hello\n\nAssistant:",
    max_tokens_to_sample=1024
)

# Claude 3 style (new - Messages API)
response = client.messages.create(
    model="claude-3-sonnet-20240229",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello"}
    ]
)

# Accessing the response
# Old: response.completion
# New: response.content[0].text

System Prompts

# Claude 3 properly supports system prompts
message = client.messages.create(
    model="claude-3-opus-20240229",
    max_tokens=1024,
    system="You are an expert Python developer. Provide clear, production-ready code.",
    messages=[
        {"role": "user", "content": "Write a retry decorator with exponential backoff"}
    ],
)

Building a Multi-Model Strategy

from enum import Enum
from dataclasses import dataclass

class ModelTier(Enum):
    FAST = "claude-3-haiku-20240307"
    BALANCED = "claude-3-sonnet-20240229"
    POWERFUL = "claude-3-opus-20240229"

@dataclass
class TaskRouter:
    """Route tasks to appropriate Claude 3 model"""

    def route(self, task_type: str, token_estimate: int) -> str:
        # High-volume, simple tasks -> Haiku
        if task_type in ["classification", "extraction", "validation"]:
            return ModelTier.FAST.value

        # Standard tasks -> Sonnet
        if task_type in ["summarization", "qa", "support"]:
            return ModelTier.BALANCED.value

        # Complex tasks -> Opus
        if task_type in ["analysis", "research", "complex_code"]:
            return ModelTier.POWERFUL.value

        # Default to Sonnet for unknown tasks
        return ModelTier.BALANCED.value

# Usage
router = TaskRouter()
model = router.route("classification", 500)  # Returns Haiku
model = router.route("analysis", 5000)       # Returns Opus

Practical Example: Document Analysis Pipeline

import anthropic
from typing import List, Dict

client = anthropic.Anthropic()

def analyze_document_pipeline(document: str) -> Dict:
    """
    Multi-stage document analysis using appropriate models
    """

    # Stage 1: Quick classification with Haiku
    doc_type = client.messages.create(
        model="claude-3-haiku-20240307",
        max_tokens=50,
        messages=[{
            "role": "user",
            "content": f"Classify this document type (contract/report/email/memo/other): {document[:1000]}"
        }]
    ).content[0].text.strip()

    # Stage 2: Extract key information with Sonnet
    extraction = client.messages.create(
        model="claude-3-sonnet-20240229",
        max_tokens=2048,
        messages=[{
            "role": "user",
            "content": f"""Extract key information from this {doc_type}:

{document}

Return as JSON with: dates, parties, key_terms, action_items"""
        }]
    ).content[0].text

    # Stage 3: Deep analysis with Opus (if needed)
    if doc_type == "contract":
        analysis = client.messages.create(
            model="claude-3-opus-20240229",
            max_tokens=4096,
            messages=[{
                "role": "user",
                "content": f"""Analyze this contract for:
1. Potential risks
2. Unusual clauses
3. Missing standard provisions
4. Negotiation recommendations

Contract:
{document}"""
            }]
        ).content[0].text
    else:
        analysis = None

    return {
        "document_type": doc_type,
        "extracted_info": extraction,
        "deep_analysis": analysis
    }

What This Means for the Industry

Claude 3’s release marks a pivotal moment:

1. Competition is healthy - GPT-4 now has a serious competitor
2. Specialization is key - Three models for different needs
3. Context length matters - 200K tokens enables new use cases
4. Safety can coexist with capability - Claude 3 is both capable AND safer

Getting Started

# Install the SDK
# pip install anthropic

import anthropic

# Initialize with your API key
client = anthropic.Anthropic()  # Uses ANTHROPIC_API_KEY env var

# Your first Claude 3 message
message = client.messages.create(
    model="claude-3-sonnet-20240229",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello, Claude 3! What makes you different from your predecessors?"}
    ]
)

print(message.content[0].text)

Conclusion

Claude 3 is not just an incremental update - it’s a significant leap forward. Opus competes with (and in some cases exceeds) GPT-4, while Haiku provides an incredibly fast option for high-volume tasks. The 200K context window and vision capabilities open new possibilities.

I’ll be exploring Claude 3 capabilities in depth over the coming weeks. This is an exciting time to be building with AI.

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References:

• Claude 3 Announcement
• Claude 3 Technical Report
• Anthropic API Documentation
• Claude 3 Model Card