Anthropic has expanded Claude Sonnet 4’s context window to a full 1,000,000 tokens, a five-fold jump that shifts what teams can do in a single request—from whole-repo code reviews to end-to-end research synthesis. In practical terms, that means you can feed the model entire codebases (75,000+ lines) or dozens of papers at once and ask for structured analysis without manual chunking gymnastics. The upgrade is live in public beta on the Anthropic API and Amazon Bedrock; support on Google Cloud’s Vertex AI is “coming soon.”
Why this matters: bigger context changes workflows, not just numbers. When prompts can carry requirements, source files, logs, and prior discussion all together, you get fewer lost references and more coherent plans. It also smooths multi-agent and tool-calling patterns where a planner, executor, and reviewer share one evolving, grounded workspace—without constant re-fetching or re-summarizing. Press coverage framed the jump as removing a major pain point: breaking big problems into fragile fragments.
What you can do today
• Audit whole repos: Ask for dependency maps, risky functions, and minimally invasive refactors across tens of thousands of lines—then request diffs.• Digest literature packs: Load a folder of PDFs and prompt for a matrix of methods, datasets, and limitations, plus follow-up questions the papers don’t answer.
• Conduct long-form investigations: Keep logs, configs, and transcripts in the same conversation so the model can track hypotheses over hours or days.
Where to run it
• Anthropic API: public beta with 1M-token support.• Amazon Bedrock: available now in public preview.
• Google Vertex AI: listed as “coming soon.”
How to get the most from 1M tokens
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Keep retrieval in the loop. A giant window isn’t a silver bullet; relevant-first context still beats raw volume. Anthropic’s own research shows better retrieval reduces failure cases dramatically. Use hybrid search (BM25 + embeddings) and reranking to stage only what matters.
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Structure the canvas. With big inputs, schema matters: headings, file paths, and short summaries up top make it easier for the model to anchor its reasoning and cite sources accurately.
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Plan for latency and cost. Longer prompts mean more compute. Batch where you can, and use summaries or “table of contents” stubs for less-critical sections before expanding on demand. (Early reports note the upgrade targets real enterprise needs like analyzing entire codebases and datasets.)
