What is the Claude Sonnet 4.6 million token context window? The Claude Sonnet 4.6 million token context window is a beta feature released February 17, 2026, that allows commercial real estate investors to process approximately 750,000 words, or 1,500 pages, of deal documents in a single AI analysis session at just $3 per million input tokens. This capability transforms how CRE professionals handle document heavy workflows like underwriting, due diligence, and portfolio analysis. For foundational context on AI in commercial real estate investing, see our complete guide on AI commercial real estate.

Key Takeaways

• Claude Sonnet 4.6's 1 million token context window holds approximately 750,000 words, enough to process a complete offering memorandum, full rent roll, three years of financials, and a property condition report in one session
• The 1M context window is now available at the Sonnet pricing tier ($3/$15 per million tokens), making large scale document analysis 40% cheaper than the Opus tier that previously held this capability exclusively
• CRE due diligence workflows that required splitting documents across multiple AI conversations can now run in a single session, preserving cross document analysis capabilities that identify hidden risks
• Anthropic's context compaction feature (beta) automatically summarizes earlier parts of long conversations, enabling effectively infinite analysis sessions without manual context management
• Early testing shows Sonnet 4.6 maintains strong retrieval accuracy across the full 1 million token range, with Anthropic reporting significant improvements in long context reliability over previous Sonnet models

What 1 Million Tokens Actually Means for CRE Documents

Context window size determines how much information an AI model can consider simultaneously. One million tokens translates to roughly 750,000 words of text. To put that in concrete CRE terms, here is what fits inside a single Sonnet 4.6 analysis session:

• Offering memorandum: A typical 60 to 100 page OM consumes approximately 30,000 to 50,000 tokens
• Rent roll (200 units): A detailed unit by unit rent roll with lease terms consumes approximately 15,000 to 25,000 tokens
• Trailing 12 month operating statement: Detailed monthly financials consume approximately 8,000 to 15,000 tokens
• Property condition report: A full PCA with photos removed consumes approximately 40,000 to 80,000 tokens
• Environmental site assessment (Phase I): A standard ESA consumes approximately 25,000 to 40,000 tokens
• Market research report: A JLL or CBRE submarket report consumes approximately 20,000 to 40,000 tokens
• Appraisal report: A narrative appraisal consumes approximately 50,000 to 80,000 tokens

Combined, a typical due diligence document package for a multifamily acquisition totals 188,000 to 330,000 tokens, well within the 1 million token limit. This means investors can load the entire document package and conduct comprehensive cross document analysis without ever hitting a context boundary. According to Anthropic, Sonnet 4.6 brings this capability to the Sonnet tier for the first time, with the same context window size that was previously available only through Opus class models.

Why Cross Document Analysis Matters for CRE

The real value of a large context window is not just processing big documents. It is the ability to analyze relationships between documents that creates actionable intelligence for CRE investors. When all documents live in the same context, the AI can perform analyses that are impossible when documents are split across separate conversations:

Revenue Verification

Ask Sonnet 4.6 to compare the rent roll's in place rents against the trailing 12 month income statement. Does the annualized rent from the rent roll match the actual collected revenue? If there is a discrepancy, the model identifies it immediately and calculates the variance. This single cross reference, which takes an analyst 30 to 60 minutes manually, completes in seconds and catches revenue inflation that might otherwise go undetected until deep in the due diligence process.

CapEx Assumption Validation

Load the property condition report alongside the seller's proforma. Ask the model to compare the PCA's recommended capital expenditures against the proforma's reserve assumptions. Are the seller's CapEx reserves sufficient to cover the PCA's identified items? If the PCA identifies $2.5 million in deferred maintenance over five years but the proforma only reserves $800,000, that gap directly impacts your underwriting. The model quantifies the difference and recalculates NOI projections with adjusted reserves. For more on how AI supports the due diligence process, see our guide on AI real estate due diligence.

Market Reality Check

Combine the OM's rent growth projections with the market research report's submarket forecasts. Does the seller's assumed 4% annual rent growth align with the market report's projection of 2.5% growth for the submarket? The model flags optimistic assumptions by benchmarking deal specific projections against independent market data, giving investors a quantified assessment of proforma realism before they commit hours to detailed underwriting.

Environmental and Legal Risk Correlation

Cross reference the Phase I environmental assessment with the property condition report and any available survey documents. If the ESA identifies potential contamination concerns near underground storage tanks and the PCA notes related infrastructure issues, the model connects these findings and assesses the combined risk profile. This pattern recognition across specialized reports is exactly where AI adds value that exceeds what most analysts can deliver under time pressure.

Practical Prompting Strategies for Large Document Analysis

Loading documents into the context window is only the first step. The quality of your analysis depends on how you structure your prompts. Here are proven strategies for CRE document analysis with Sonnet 4.6:

The Layered Analysis Approach

Rather than asking one broad question, structure your analysis in layers. Start with a document inventory prompt that asks the model to identify what documents are present and summarize each one in two to three sentences. Then follow with specific analytical queries that reference multiple documents. This approach ensures the model has properly parsed each document before you ask it to draw cross document conclusions.

The Investment Thesis Test

Provide your investment thesis alongside the document package and ask the model to evaluate whether the documents support or contradict your thesis. For example: "My thesis is that this 180 unit property in Austin is a value add opportunity where I can achieve 25% rent premiums through unit renovations over a 3 year hold. Based on all uploaded documents, evaluate whether this thesis is supported, and identify the top 5 risks." This structured prompt focuses the model's analysis on what matters for your specific decision. Investors building systematic AI multifamily underwriting workflows find this thesis testing approach particularly effective.

The Red Flag Scan

Ask the model to perform a comprehensive red flag scan across all documents. Effective prompt language includes: "Review all uploaded documents and identify any inconsistencies between documents, any assumptions that appear unrealistic based on the market data provided, any deferred maintenance items that are not reflected in the proforma, and any lease terms or tenant concentrations that create unusual risk." This broad scan leverages the model's ability to hold all documents in context simultaneously, a capability that is only possible with the 1 million token window.

Context Compaction: Enabling Infinite Analysis Sessions

Sonnet 4.6 introduces context compaction in beta, a feature that automatically summarizes earlier parts of a conversation when the context window approaches its limit. For CRE investors, this means analysis sessions are no longer bounded by the context window. You can load a document package, conduct initial analysis, add additional documents, ask follow up questions, and continue refining your analysis without worrying about hitting a wall.

The compaction mechanism preserves key facts, numbers, and conclusions from earlier in the conversation while compressing the detailed text. This is particularly valuable during due diligence, where new documents often arrive over days or weeks. Instead of starting fresh each time a new report becomes available, investors can continue their existing analysis session, adding context incrementally. The model retains its understanding of previously analyzed documents even after compaction, maintaining analytical continuity across extended due diligence timelines.

Cost Analysis: Large Context at Sonnet Pricing

The economics of large context document analysis improve dramatically at the Sonnet 4.6 pricing tier. Here is a realistic cost breakdown for common CRE analysis scenarios:

• Standard deal screening (OM plus rent roll, approximately 60,000 input tokens, 5,000 output tokens): $0.26 per deal
• Full underwriting analysis (complete document package, approximately 300,000 input tokens, 20,000 output tokens): $1.20 per deal
• Comprehensive due diligence review (all DD documents, approximately 500,000 input tokens, 30,000 output tokens): $1.95 per deal
• Portfolio analysis (10 property operating statements, approximately 150,000 input tokens, 15,000 output tokens): $0.68 per session

For a CRE firm screening 40 deals monthly and conducting full underwriting on 10, the monthly AI cost with Sonnet 4.6 is approximately $22.40. The same workflow with Opus 4.6 would cost approximately $37.50. Both figures represent extraordinary value relative to the analyst hours saved, but Sonnet 4.6 makes the decision to adopt AI for every deal an obvious one. For more on AI tools and their costs, see our comprehensive guide on AI tools for real estate investors.

Limitations and Best Practices

While the 1 million token context window is transformative, CRE investors should be aware of several practical considerations:

• Premium pricing above 200K tokens: When input exceeds 200,000 tokens, all tokens are priced at a premium rate. For Sonnet 4.6, this means $6 per million input tokens instead of $3. Plan your document loading strategy accordingly, prioritizing the most analytically important documents
• Retrieval accuracy varies by position: While Sonnet 4.6 shows improved long context reliability, information buried deep in the middle of a very long context may occasionally receive less attention than information at the beginning or end. Place your most critical documents first in the context
• Text extraction quality matters: The model analyzes whatever text you provide. Poor quality OCR from scanned documents, garbled table formatting, or missing sections in the source documents will produce lower quality analysis. Clean your document text before uploading for best results
• Always verify critical numbers: Cross document analysis is powerful but not infallible. Verify key financial figures, especially IRR calculations, waterfall distributions, and debt sizing numbers, against your own models before making investment decisions

If you are ready to implement large context AI document analysis into your CRE workflow, The AI Consulting Network specializes in building customized pipelines that match your deal flow and investment criteria. Connect with Avi Hacker, J.D. for a tailored implementation plan.

Frequently Asked Questions

Q: How many pages of CRE documents can Sonnet 4.6 process at once?

A: Approximately 1,500 pages of text, which translates to 750,000 words or 1 million tokens. A typical multifamily due diligence package (OM, rent roll, financials, PCA, ESA, and market report) totals 200,000 to 330,000 tokens, fitting comfortably within the limit with room for detailed follow up questions.

Q: Is the 1 million token context window available to free Claude users?

A: The 1 million token context window is currently in beta and available through the API and Claude Code on a pay as you go basis. Free and Pro tier users on claude.ai have access to Sonnet 4.6 as their default model, but the extended context window availability may vary by plan tier.

Q: Does document analysis quality degrade with very large context?

A: Sonnet 4.6 shows significantly improved long context reliability compared to previous Sonnet models. However, placing the most critical documents first in the context and using specific targeted questions rather than open ended prompts produces the best results. Anthropic's context compaction feature also helps maintain quality during extended analysis sessions.

Q: Can I upload PDF documents directly to Claude for analysis?

A: Yes, Claude supports PDF uploads through both claude.ai and the API. For best results, ensure documents are text based PDFs rather than scanned images. If working with scanned documents, use OCR processing first to convert images to text before uploading.

Q: How does the 1 million token context compare to ChatGPT's context window?

A: Claude Sonnet 4.6's 1 million token context window is among the largest available from any frontier AI model. ChatGPT's context window varies by model but is currently smaller for comparable reasoning quality. For CRE investors processing large document packages, this context advantage is one of Claude's most meaningful differentiators. For a detailed platform comparison, see our guide on Claude Opus 4.6 vs ChatGPT for CRE.