Choosing the right model



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 to meet your needs?
• Speed: How quickly does the model need to respond in your application? Claude Opus 5 and Claude Opus 4.8 support fast mode (research preview), which delivers up to 2.5x higher output speed at premium pricing.
• Cost: What's your budget for both development and production usage?
• Effort: Recent Opus and Sonnet models support an effort parameter that trades intelligence for latency and cost within a single model. Tuning effort is often a better lever than switching models. On Claude Opus 5, start with the default (high) and adjust up or down based on your evals. On Claude Opus 4.8 and Claude Opus 4.7, the xhigh effort level, between high and max, is the best setting for most coding and agentic use cases.

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 efficiency-first

For many applications, starting with a faster, more cost-effective model like Claude Haiku 4.5 can be the optimal approach:

1. Begin implementation with Claude Haiku 4.5.
2. Test your use case thoroughly.
3. Evaluate if performance meets your requirements.
4. 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 capability-first

For complex tasks where intelligence and advanced capabilities are paramount, you may want to start capability-first: implement with the strongest starting point for your task, then optimize to more efficient models down the line:

1. Implement with Claude Opus 5.
2. Optimize your prompts for this model.
3. Evaluate if performance meets your requirements.
4. Consider increasing efficiency by lowering effort or downgrading models 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 and high-autonomy agentic work

Claude Opus 5 (claude-opus-5) is a step-change improvement over Claude Opus 4.8, strong on deep reasoning, agentic and long-horizon tasks, and test-time compute scaling. Claude Opus 5 supports a 1M token context window by default and up to 128k output tokens, and is priced at $5 per million input tokens and $25 per million output tokens.

Claude Fable 5 (claude-fable-5) is Anthropic's most capable widely released model, delivering next-generation intelligence for long-running agents. Claude Mythos 5 (claude-mythos-5) is available through Project Glasswing. Both models support a 1M token context window by default, up to 128k output tokens, and always-on adaptive thinking. See Introducing Claude Fable 5 and Claude Mythos 5 for launch details.

Claude Fable 5 and Claude Mythos 5 are priced at $10 per million input tokens and $50 per million output tokens.



Model selection matrix



Decide whether to upgrade or change models

To determine if you need to upgrade or change models, you should:

1. Create benchmark tests specific to your use case - having a good evaluation set is the most important step in the process.
2. Test with your actual prompts and data.
3. Compare performance across models for:
   • Accuracy of responses
   • Response quality
   • Handling of edge cases
4. Weigh performance and cost tradeoffs.



Next steps