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Claude Platform's latest update offers a groundbreaking 1M context window for both Opus and Sonnet models, eliminating long-context premiums and setting standard pricing across the board for enhanced efficiency in data handling.
The Claude Platform has announced the general availability of a full 1M context window for both Opus 4.6 and Sonnet 4.6 models, with standard pricing now applying across the entire window. This update brings several key improvements that will significantly benefit practitioners working with large datasets and complex tasks.
One Price, Full Context Window:
Full Rate Limits at Every Context Length:
6x More Media Per Request:
No Beta Header Required:
A million-token context window is more than just a large number; it's about maintaining accuracy and recall across vast amounts of data. Opus 4.6 scores 78.3% on MRCR v2 (Multi-Reference Comprehension and Reasoning), the highest among frontier models at this context length.
This improvement means you can load entire codebases, thousands of pages of contracts, or long-running agent traces-complete with tool calls, observations, and intermediate reasoning-and use them directly. The need for lossy summarization and frequent context clearing is minimized, ensuring that more of the conversation remains intact.

Dr. Alex Wissner-Gross, Co-Founder, highlights how Claude Opus 4.6’s 1M context window and expanded media limits are revolutionizing scientific research:
"Scientific discovery requires reasoning across research literature, mathematical frameworks, databases, and simulation code simultaneously. Claude Opus 4.6’s 1M context and expanded media limits let our agentic systems synthesize hundreds of papers, proofs, and codebases in a single pass, helping us dramatically accelerate fundamental and applied physics research."
For developers using Claude Code:
Pricing Structure:
Rate Limits:
Media Handling:
The general availability of a full 1M context window for Opus 4.6 and Sonnet 4.6 on the Claude Platform marks a significant step forward in handling large datasets and complex tasks. With no long-context premium, consistent rate limits, and expanded media support, practitioners can now leverage these models more effectively across a wide range of applications.
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About the author
Kai built ML infrastructure at a Bay Area startup before developing an obsession with transformer architectures and inference optimisation that eventually pulled him out of product work entirely. A stint at a compute research lab sharpened his instinct for what actually matters in a model release versus what is marketing. He writes from the inside — from the perspective of someone who has debugged the systems he is describing at three in the morning. He is allergic to hype and instinctively drawn to the unglamorous plumbing questions that everyone else skips over.
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