What Makes Opus 4.6 Different
Claude Opus 4.6 represents a fundamental leap in AI capability. It's not just incrementally better — it introduces entirely new categories of what's possible with language models.
The Key Breakthroughs
1. Extended Thinking
Opus 4.6 can "think" before responding. When enabled, the model performs chain-of-thought reasoning internally before generating its answer. This dramatically improves performance on:
- Complex mathematical proofs
- Multi-step logical reasoning
- Code architecture decisions
- Nuanced analysis of ambiguous situations
Think of it as the difference between answering a question off the top of your head versus taking time to work through the problem on paper.
2. One Million Token Context Window
With a 1M token context window, Opus 4.6 can process approximately:
- ~750,000 words of text (roughly 10 full-length novels)
- ~30,000 lines of code in a single request
- Hundreds of pages of documentation simultaneously
This eliminates the need for complex chunking strategies and RAG pipelines in many use cases.
3. Agentic Excellence
Opus 4.6 excels at agentic tasks — workflows where the AI needs to:
- Plan multi-step approaches
- Use tools and APIs
- Handle errors and adapt
- Maintain context across long interactions
- Make decisions with incomplete information
4. World-Class Code Generation
Opus 4.6 achieves state-of-the-art results on coding benchmarks:
- SWE-bench Verified: 72.5% — the highest score by any model
- Terminal-bench: #1 for real-world agentic coding tasks
- TAU-bench: Top performance on complex, multi-step tool use
Capability Comparison
Feature | Opus 4.6 | Sonnet 4.6 | Haiku 4.5
---------------------|-----------|------------|----------
Max Context | 1M tokens | 200K | 200K
Extended Thinking | ✅ Yes | ✅ Yes | ❌ No
Max Output | 32K | 16K | 8K
Agentic Performance | Highest | High | Moderate
Code Quality | Best | Very Good | Good
Cost (per 1M input) | $15 | $3 | $0.80
When to Use Opus 4.6
Choose Opus 4.6 when you need:
- Maximum accuracy on complex tasks
- Extended reasoning for hard problems
- Long-context document analysis
- Building reliable autonomous agents
- Code generation for complex systems
Consider Sonnet 4.6 when you need:
- Good performance at lower cost
- Faster response times
- High-volume applications
Consider Haiku 4.5 when you need:
- Maximum speed
- Simple classification or extraction tasks
- Cost-sensitive high-volume processing
Understanding these trade-offs is crucial for building cost-effective AI applications.