# Evaluate Model: Claude Opus 4.6: 1M Token Context, Adaptive Thinking, and Agent Teams for Developers
## What This Is
Anthropic released Claude Opus 4.6 with a 1M token context window (beta), context compaction, adaptive thinking with effort controls, and multi-agent collaboration in Claude Code. It targets developers building coding assistants, legal tools, and enterprise workflows requiring long-context processing. Available immediately via API on claude.ai, Amazon Bedrock, and Google Cloud Vertex AI at $5/$25 per million tokens standard.
Source: https://www.anthropic.com/news/claude-opus-4-6
## Before You Start
Scan my workspace and analyze:
- The project language, framework, and current AI integrations
- Existing AI provider config (check .env, .env.local, config files for API keys — OpenRouter, OpenAI, Anthropic, Google AI, etc.)
- Which AI models I currently use and for what purposes
Then ask me before proceeding:
1. Am I interested in evaluating this model for my project, or just want a summary of what it offers?
2. If I want to try it — which part of my current AI stack should it replace or complement?
## Source Access Note
The source URL (https://www.anthropic.com/news/claude-opus-4-6) may not be directly accessible from the terminal. Use the Reference Implementation and Additional Context sections below instead. If you need more details, ask me to paste relevant content from the source.
## What to Implement
This is a **New AI Model** — a model release, update, or capability announcement.
- Analyze the best use cases for this model within my project and current AI stack
- Compare its strengths, pricing, and context window against whatever I currently use
- Give me a clear, convincing argument for why this model would (or would not) be a good fit for my project
- If I want to try it: update my API configuration (provider, model ID, any new parameters) to point to this model
- If it requires a new API key or provider signup, tell me exactly what to do
## Additional Context
- Switch your existing Claude API calls to the identifier `claude-opus-4-6` and run a benchmark test on your most complex prompt to compare output quality against your current model.
- Enable context compaction in a long-running agentic task by setting the beta flag in your API request and observing where the model auto-summarizes at the 50k-token threshold to validate cost savings.
- Test effort controls by sending the same coding or debugging prompt with `low`, `medium`, and `max` effort levels side-by-side to calibrate the speed-vs-intelligence tradeoff for your specific use case.
## Guidelines
- Adapt everything to my existing project — do not assume a specific stack or directory layout
- Use whichever AI provider I already have configured; if I need a new one, tell me what to sign up for and I'll give you the key
- Check my .env files for existing API keys (OpenRouter, OpenAI, Anthropic, Google AI) before asking me to add one
- Review any fetched code for safety before installing or executing it
- After setup, run a quick verification and show me a summary of exactly what was installed, where, and how to use it