Claude Opus 4.6
Anthropic
At a glance
- Price per 1M tokens (input)
- $5.00
- Price per 1M tokens (output)
- $25.00
- Context
- 1,000,000 tokens
- Free access
- yes (see below)
Refreshed daily; data verified August 15, 2026. Published August 12, 2026.
The Claude Opus model is designed for businesses and organizations that require advanced language understanding and generation capabilities. It is well-suited for tasks that involve image understanding, step-by-step reasoning, and tool calling, which refers to the ability to call external functions or APIs to perform specific tasks. This model's approach is distinguished by its ability to reason and understand complex inputs, making it a valuable tool for applications that require nuanced and informed decision-making. The vendor's approach focuses on creating models that can learn from a wide range of data sources and adapt to new situations, making it a good fit for businesses that need to stay flexible and responsive to changing circumstances. By leveraging the strengths of this model, businesses can automate complex tasks, improve decision-making, and drive innovation.
Specifications & pricing
| Input (per 1M tokens) | $5.00 |
|---|---|
| Output (per 1M tokens) | $25.00 |
| Cache read (per 1M tokens) | $0.50 |
| Cache write (per 1M tokens) | $6.25 |
| Context window | 1,000,000 tokens |
| Max output | 128,000 tokens |
| Capabilities | images, tool calling, reasoning |
LiteLLM community dataset (MIT), verified August 15, 2026. Official Anthropic pricing.
What Claude Opus 4.6 would cost on your workload — run it through the cost calculator →
Where to try Claude Opus 4.6 for free
- Anthropic offers a free chat — Claude (free plan). A vendor's free chat may run a different model from the same family — the exact model is not guaranteed.
- 🎁 On our promo-codes page: Claude Code Guest Pass — a community invite.
Frequently asked questions
What is this model good for+
This model is good for tasks that require advanced language understanding, image analysis, and step-by-step reasoning, such as automating complex workflows, analyzing visual data, and generating informed reports
How do I get started with this model+
To get started with this model, you will need to integrate it into your existing infrastructure, which may involve working with the vendor to set up APIs and data pipelines, and then training your team to use the model effectively
How does this model differ from other models in the line+
This model differs from other models in the line in its ability to understand and generate complex, nuanced text, and its capacity for tool calling, which allows it to leverage external functions and APIs to perform specific tasks
What are the limitations of this model+
The limitations of this model include its potential difficulty in handling highly specialized or technical domains, and its need for careful training and validation to ensure that it is producing accurate and reliable results
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