Claude Sonnet 5
Anthropic
At a glance
- Price per 1M tokens (input)
- $2.00
- Price per 1M tokens (output)
- $10.00
- Context
- 1,000,000 tokens
- Free access
- yes (see below)
Refreshed daily; data verified August 15, 2026. Published August 9, 2026.
Claude Sonnet 5 is designed for businesses and organizations that require advanced image understanding and step-by-step reasoning capabilities. This model is well-suited for tasks such as image analysis, object detection, and image-based decision making. The vendor's approach to developing this model focuses on creating a robust and flexible architecture that can be applied to a wide range of applications. Tool calling, which refers to the ability of the model to call external functions or tools to perform specific tasks, is a key feature that sets this model apart from others in its class. By leveraging this capability, users can extend the model's functionality and create custom workflows that meet their specific needs.
Specifications & pricing
| Input (per 1M tokens) | $2.00 |
|---|---|
| Output (per 1M tokens) | $10.00 |
| Cache read (per 1M tokens) | $0.20 |
| Cache write (per 1M tokens) | $2.50 |
| 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 Sonnet 5 would cost on your workload — run it through the cost calculator →
Where to try Claude Sonnet 5 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 image understanding and step-by-step reasoning, such as image analysis and object detection
How do I get started with this model+
To get started with this model, users should familiarize themselves with the model's capabilities and limitations, and then design a workflow that leverages its strengths, such as tool calling and image understanding
How does this model differ from other models in the line+
This model differs from other models in the line in its focus on image understanding and step-by-step reasoning, as well as its ability to perform tool calling, which allows users to create custom workflows and extend the model's functionality
What are the limitations of this model+
The limitations of this model include its potential difficulty in handling complex or ambiguous images, and its reliance on high-quality training data to produce accurate results
Comparisons
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