Claude's Guide to Picking the Right AI Model for Your Needs

Claude, a leading AI assistant developed by Anthropic, has released a comprehensive guide to help users decide which of its four AI models—Haiku, Sonnet, Opus, and Fable—to use for their specific tasks. This guide is particularly useful for developers and businesses looking to optimize their AI workflows. The guide provides detailed advice on when to switch models and how to optimize costs by adjusting effort levels.

Understanding the Claude Model Lineup

Claude currently offers four generally available models: Claude Haiku 5.5, Claude Sonnet 5.5, Claude Opus 5.5, and Claude Fable 5.2. Each model is designed for different types of tasks, ranging from simple classification to complex coding and scientific research. Claude Mythos 5.2, the most capable model, is available through verification programs for cybersecurity and life sciences research.

Claude Haiku 5.5 is the smallest and most cost-effective model, ideal for high-volume, simple tasks such as classification, routing, and extraction. Claude Sonnet 5.5 is a step up, suitable for well-scoped everyday work like bug fixes or feature builds against a spec. Claude Opus 5.5 is the default model for most tasks, offering a balance between cost and capability. Claude Fable 5.2 is the most powerful model, designed for the hardest technical projects in coding, finance, and the sciences.

When to Use Each Model

The guide recommends starting most work with Opus 5.5 at its default effort level, medium. Opus 5.5 is cost-effective, costing half as much per input and output token as Fable 5.2, and is ideal for coding and knowledge work. For well-scoped everyday tasks like bug fixes or feature builds, Sonnet 5.5 is recommended. Haiku 5.5 is best for high-volume, simple tasks such as classification, routing, and extraction. Fable 5.2 is reserved for the hardest technical projects in coding, finance, and the sciences.

The guide also advises moving up to Fable 5.2 when Opus 5.5 at high effort falls short, or for long unattended runs. Conversely, moving down to Sonnet 5.5 or Haiku 5.5 can be more cost-effective for simpler tasks. The guide emphasizes the importance of adjusting effort levels before switching models to optimize costs and performance.

Optimizing Costs and Performance

Before switching models, the guide advises trying to adjust the effort level. Higher effort levels can help the model think longer, call more tools, or check its own work, but they also add latency and cost. The guide suggests testing the next model up at a lower effort level if the current model at high effort falls short. For example, on the FrontierCode 2.0 benchmark, Fable 5.2 at high effort scored about the same as Opus 5.5 at max effort for roughly half the cost per task.

The guide also provides practical tips for optimizing costs and performance. For instance, it suggests using self-verification methods to reduce the need for higher effort levels. Automatically checking outcomes and re-running failures at a high effort level can be more cost-effective. Additionally, the guide notes that faster models like Fable 5.2 generate output up to 30% faster than their predecessors, which can be crucial for tasks requiring quick responses.

Practical Tips for Users

The guide offers several practical tips for users to optimize their AI workflows. For example, it suggests pricing the hardest tasks in your set as well as the typical ones, because the hardest tasks carry most of the spend. On one 20-problem search benchmark, two problems carried 43% of the spend. This highlights the importance of carefully selecting the right model and effort level for each task.

The guide also emphasizes the trade-offs between capability and latency. Generally, bigger models cost more per token and are more capable, but the cost vs. capability trade-off isn't universally true at the task level. A higher price per token doesn't always mean a higher cost per task or a better result. For example, on Anthropic's runs of Terminal-Bench 3.0, the previous Fable, 5.1, at max scored 58.0%, while Opus 5.5 at high scored 58.9% with half the tokens.

What You Can Do Today

If you're a Claude user, you can start by reviewing the guide on the Claude blog to understand which model best suits your needs. Open the Claude platform and experiment with different models and effort levels to see how they perform on your specific tasks. For example, try using Opus 5.5 for your next coding project and adjust the effort level to see the difference in performance and cost. Additionally, consider using self-verification methods to reduce costs and improve efficiency.

By following the guide, you can optimize your AI workflows, reduce costs, and improve performance. Whether you're a developer, a business, or an individual user, understanding the different Claude models and their use cases can help you make the most of your AI tools.