How to use Claude Opus 4.6 1M context

Anthropic just released Claude Opus 4.6, a large context AI model supporting up to 1 million tokens. This video dives into its capabilities, benchmarks, and how it performs in a real production React Native app built with Expo.

I test Opus 4.6 by adding a prompt history feature to my AI tattoo app, Inkigo, using Expo SQLite for local storage. I show how Opus 4.6 plans and executes code changes, handles large codebases, and the costs involved with using the model.

What's inside

• Anthropic just released Claude Opus 4.6
• Benchmark comparisons with GPT 5.2 and others
• Massive 1 million token context support explained
• Testing Opus 4.6 in a real React Native app (Inkigo)
• Using Expo SQLite for prompt history storage
• Integrating voice input with Aqua Voice for prompts
• Observing Opus 4.6’s code generation and planning
• Cost considerations when using Opus 4.6

Anthropic just released Claude Opus 4.6

Claude Opus 4.6 is Anthropic’s latest AI model, touted as the smartest and most reliable yet. It improves on Opus 4.5 by planning more carefully, sustaining agent tasks longer, and handling large codebases better. This makes it attractive for enterprises and engineers working with complex projects.

The model is designed to be more reliable in multi-step reasoning and coding tasks, especially in large-scale applications. I focus on putting this model to the test in a real-world React Native project.

Benchmark comparisons with GPT 5.2 and others

The official Anthropic blog post includes graphs showing Opus 4.6 outperforming GPT 5.2 in knowledge work, agentic search, coding, and reasoning tasks. It scores highest in deep multi-step agentic search and long context reasoning.

These benchmarks highlight Opus 4.6’s strength in handling complex, multi-step problems and maintaining context over very long inputs, which is crucial for coding and enterprise applications.

Massive 1 million token context support explained

One of the biggest improvements in Opus 4.6 is support for a 1 million token context window. This means the model can remember and reason over extremely large amounts of text or code.

In a benchmark example, Opus 4.6 correctly retrieves user preferences (like favorite fruit) from a quarter million tokens of context about 76% of the time. This is a huge leap for applications needing deep context retention, such as large codebases or extensive documents.

Testing Opus 4.6 in a real React Native app (Inkigo)

I test Opus 4.6 by adding a prompt history feature to Inkigo, my AI tattoo app built with React Native and Expo. Inkigo is a production app generating over $200 monthly recurring revenue.

The goal is to let users save and reuse previous prompts, solving the problem of losing prompt text after generating tattoo ideas. This practical test shows how Opus 4.6 handles planning and coding tasks in a large, real-world codebase.

Using Expo SQLite for prompt history storage

Inkigo already uses Expo SQLite for storing app settings. I extend this to save prompt history locally without API calls.

I show how Opus 4.6 generates code to create a bottom sheet UI displaying saved prompts, allowing users to select or delete them. The approach uses Expo SQLite KV storage, following existing app patterns for consistency.

Integrating voice input with Aqua Voice for prompts

To speed up prompt entry, I use Aqua Voice, a speech-to-text software, to dictate prompts instead of typing. This integration helps demonstrate a smoother workflow when interacting with the AI model.

Aqua Voice is recommended with a free one-month trial link in the description, making it easy to try voice input in your own projects.

Observing Opus 4.6’s code generation and planning

Opus 4.6 quickly generates a detailed plan to implement prompt history, including UI changes and data storage. It writes code consistent with the existing app architecture, which is important for maintainability in large projects.

The model handles tricky UI features like swipe-to-delete with mixed success, showing some limitations. Still, it produces a mostly working implementation that I test live on my iPhone.

Cost considerations when using Opus 4.6

Using the 1 million token context model is expensive. I burn about $2 in API credits testing the feature, with most cost coming from the large token context usage (around 55,000 tokens per request).

This cost is a tradeoff for the model’s advanced capabilities and long context support. Use it carefully and monitor usage to avoid unexpected expenses.

Resources

InkigoAI tattoo app

See a real production React Native app using Claude Opus 4.6 and Expo SQLite for prompt history.

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