Choosing A Model ↗
yesEditorial Notes
The essential decision guide for anyone evaluating which Claude model to use. Anthropic organizes its models into tiers — Haiku for speed and cost efficiency, Sonnet for the best balance of capability and price, and Opus for maximum intelligence on complex tasks. For product leaders, the key insight is that model selection is a business decision, not just a technical one: choosing Haiku over Opus can reduce costs by 10-20x while still handling most routine tasks. Compare this tiered approach with OpenAI’s model lineup (GPT-4o, GPT-4o mini, o1) and Mistral’s range to understand how the industry structures the speed-cost-quality tradeoff.
Original Documentation
Selecting the optimal Claude model for your application involves balancing three key considerations: capabilities, speed, and cost. This guide helps you make an informed decision based on your specific requirements.
Establish key criteria#
When choosing a Claude model, consider first evaluating these factors:
- Capabilities: What specific features or capabilities will you need the model to have in order to meet your needs?
- Speed: How quickly does the model need to respond in your application? For Claude Opus 4.6, fast mode (research preview) can provide up to 2.5x higher output speed at premium pricing.
- Cost: What’s your budget for both development and production usage?
Knowing these answers in advance will make narrowing down and deciding which model to use much easier.
Choose the best model to start with#
There are two general approaches you can use to start testing which Claude model best works for your needs.
Option 1: Start with a fast, cost-effective model#
For many applications, starting with a faster, more cost-effective model like Claude Haiku 4.5 can be the optimal approach:
- Begin implementation with Claude Haiku 4.5
- Test your use case thoroughly
- Evaluate if performance meets your requirements
- Upgrade only if necessary for specific capability gaps
This approach allows for quick iteration, lower development costs, and is often sufficient for many common applications. This approach is best for:
- Initial prototyping and development
- Applications with tight latency requirements
- Cost-sensitive implementations
- High-volume, straightforward tasks
Option 2: Start with the most capable model#
For complex tasks where intelligence and advanced capabilities are paramount, you may want to start with the most capable model and then consider optimizing to more efficient models down the line:
- Implement with Claude Opus 4.6
- Optimize your prompts for these models
- Evaluate if performance meets your requirements
- Consider increasing efficiency by downgrading intelligence over time with greater workflow optimization
This approach is best for:
- Complex reasoning tasks
- Scientific or mathematical applications
- Tasks requiring nuanced understanding
- Applications where accuracy outweighs cost considerations
- Advanced coding
Model selection matrix#
| When you need… | Consider starting with… | Example use cases |
|---|---|---|
| The most intelligent model, and the world’s best model for coding, enterprise agents, and professional work. | Claude Opus 4.6 | Professional software engineering, advanced agents for office tasks, computer and browser use at scale, multi-hour research tasks, step-change vision applications |
| Frontier intelligence at scale, built for coding, agents, and enterprise workflows | Claude Sonnet 4.6 | Code generation, data analysis, content creation, visual understanding, agentic tool use |
| Near-frontier performance with lightning-fast speed and extended thinking at the most economical price point | Claude Haiku 4.5 | Real-time applications, high-volume intelligent processing, cost-sensitive deployments needing strong reasoning, sub-agent tasks |
Decide whether to upgrade or change models#
To determine if you need to upgrade or change models, you should:
- Create benchmark tests specific to your use case - having a good evaluation set is the most important step in the process
- Test with your actual prompts and data
- Compare performance across models for:
- Accuracy of responses
- Response quality
- Handling of edge cases
- Weigh performance and cost tradeoffs
Next steps#
See detailed specifications and pricing for the latest Claude models Explore the latest improvements in Claude 4.6 models Get started with your first API call