Claude 3.5 Sonnet Optimized for RAG: 500K Context Window and Extended Thinking

Announcement

Anthropic has released an updated Claude 3.5 Sonnet with features specifically designed for RAG applications, including a 500K token context window and enhanced citation capabilities.

Key Features

Extended Context Window

Context window expanded to 500K tokens (approximately 1.5 million characters):

What this enables:

• Entire codebases in context (~150K lines of code)
• Full research papers with references
• Complete legal documents
• Month-long conversation histories

Pricing:

• Input: $3.00 per million tokens
• Output: $15.00 per million tokens
• Same as 200K window version (no premium for extra capacity)

RAG-Specific Improvements

Improved Citation Accuracy

Claude 3.5 now includes exact passage citations:

Query: "What is the refund policy?"

Response: "According to our refund policy [1], customers can request
a full refund within 30 days of purchase [2]."

Sources:
[1] Customer Service Policy, Section 4.2, Page 12
[2] Terms of Service, Article 8, Last updated: 2025-10-15

Citation accuracy improved from 78% to 94% in internal benchmarks.

Contextual Hallucination Detection

New analyze_faithfulness parameter:

DEVELOPERpython
response = anthropic.messages.create(
    model="claude-3-5-sonnet-20251101",
    messages=[{"role": "user", "content": prompt}],
    analyze_faithfulness=True  # New parameter
)

# Returns faithfulness score
print(response.faithfulness_score)  # 0.0-1.0

Helps identify when the model generates information not in the provided context.

Multi-Document Reasoning

Better at synthesizing information across many documents:

• Tested on MultiDoc benchmark
• 15% improvement in cross-document Q&A
• Handles up to 100 retrieved chunks effectively

Performance Benchmarks

RAG-Specific Tests

Tested on RAG-Truth benchmark (faithfulness to source):

Long Context Performance

Needle-in-haystack test (finding specific information in long context):

• 100K tokens: 99.2% accuracy
• 200K tokens: 98.7% accuracy
• 350K tokens: 97.1% accuracy
• 500K tokens: 95.3% accuracy

Performance degrades gracefully even at maximum window.

Extended Thinking Mode

New experimental feature for complex RAG queries:

DEVELOPERpython
response = anthropic.messages.create(
    model="claude-3-5-sonnet-20251101",
    messages=[{"role": "user", "content": complex_query}],
    extended_thinking=True,  # Enables chain-of-thought
    max_tokens=4096
)

# Model shows reasoning process
print(response.thinking)  # Internal reasoning steps
print(response.answer)     # Final answer

Improves multi-hop question accuracy by 23% but increases latency by 2-3x.

Enterprise Features

Batch Processing

Process large RAG workloads at 50% discount:

DEVELOPERpython
# Submit batch job
batch = anthropic.batches.create(
    requests=[
        {"model": "claude-3-5-sonnet-20251101", "messages": msgs1},
        {"model": "claude-3-5-sonnet-20251101", "messages": msgs2},
        # ... up to 10,000 requests
    ]
)

# Check status
status = anthropic.batches.retrieve(batch.id)

# Retrieve results (available in 24 hours)
results = anthropic.batches.results(batch.id)

Cached Context

Reduce costs for repeated context:

DEVELOPERpython
# First request: full cost
response1 = anthropic.messages.create(
    model="claude-3-5-sonnet-20251101",
    messages=[...],
    system="Large system prompt...",  # 10K tokens
    enable_caching=True
)

# Subsequent requests: 90% discount on cached content
response2 = anthropic.messages.create(
    model="claude-3-5-sonnet-20251101",
    messages=[...],
    system="Large system prompt...",  # Same 10K tokens, cached
    enable_caching=True
)

Cache persists for 5 minutes. Ideal for RAG where context stays constant across queries.

Use Cases

Claude 3.5 Sonnet RAG excels at:

Legal Research

• Analyze full case files
• Cross-reference precedents
• Generate briefs with citations

Scientific Research

• Review multiple papers simultaneously
• Extract findings across studies
• Generate literature reviews

Technical Documentation

• Answer questions across large codebases
• Provide accurate code references
• Explain complex system interactions

Customer Support

• Comprehensive knowledge base access
• Accurate policy citations
• Multi-turn conversations with context

Migration Guide

Upgrading from Claude 3 Opus:

DEVELOPERpython
# Old
response = anthropic.messages.create(
    model="claude-3-opus-20240229",
    max_tokens=1024,
    messages=messages
)

# New
response = anthropic.messages.create(
    model="claude-3-5-sonnet-20251101",  # Updated model ID
    max_tokens=1024,
    messages=messages,
    analyze_faithfulness=True,  # Optional: enable faithfulness scoring
    enable_caching=True  # Optional: cache system prompts
)

Limitations

Latency

• 500K context: 5-10s response time
• Extended thinking: 10-30s response time
• Not suitable for real-time applications

Costs

• 500K context costs $1.50 input per request
• Large context = expensive at scale
• Use caching and batching to mitigate

Context Processing

• Model reads full context each time
• No incremental updates
• Consider chunking for very long documents

Best Practices

1. Use caching: Enable for repeated contexts (RAG system prompts)
2. Batch when possible: 50% cost savings for offline workloads
3. Enable faithfulness: Track hallucination risk
4. Optimize prompts: Shorter prompts = lower costs
5. Test context limits: Accuracy degrades above 400K tokens

Availability

• Available now via Anthropic API
• Coming to AWS Bedrock (November)
• Coming to Google Cloud Vertex AI (December)
• Not yet available in Claude web interface

Conclusion

Claude 3.5 Sonnet's RAG-specific optimizations make it an excellent choice for enterprise retrieval applications where accuracy and attribution are critical. The combination of large context window, citation capabilities, and cost controls positions it as a strong contender for production RAG systems.