Models & pricingGuides

Choosing the right model

Choosing a Claude model means balancing capabilities, speed, and cost. This guide covers the questions to ask, two ways to pick a starting model, and how to test the choice.

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: Several Claude 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 Fable 5.1 and 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.

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:

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 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:

Implement with Claude Opus 5.
Optimize your prompts for this model.
Evaluate if performance meets your requirements.
Consider increasing efficiency by lowering effort or downgrading models over time with greater workflow optimization.
If your evals at xhigh or max effort still fall short on demanding reasoning or long-horizon agentic work, move to Claude Fable 5.1.

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 built for complex agentic coding and enterprise work, with deep reasoning, long-horizon tasks, and test-time compute scaling.

Claude Fable 5.1 (claude-fable-5-1) is Anthropic's most capable widely released model. It extends Claude Fable 5 with stronger long-running agentic coding, knowledge work, and research at the same input and output prices, with cache reads at a quarter of the cost. Claude Mythos 5.1 (claude-mythos-5-1) offers the same capabilities to Project Glasswing participants only. Both models use always-on adaptive thinking. See What's new in Claude Fable 5.1 for details.

Claude Fable 5 and Claude Mythos 5 are also available. See Introducing Claude Fable 5 and Claude Mythos 5 for details. For context windows, output limits, and prices, see the model comparison table.

Model selection matrix

Most workloads start with Claude Opus 5.

When you need...	Consider starting with...	Example use cases
The highest available capability	Claude Fable 5.1	Agent sessions that run for hours, multistep deep research, analysis carried through to a finished document, spreadsheet, or deck
Complex agentic coding and enterprise work	Claude Opus 5	Multihour autonomous coding agents, large-scale refactoring, complex systems engineering, vision-heavy workflows, computer use
Speed and capability for everyday coding, agent, and enterprise workloads	Claude Sonnet 5	Code generation, data analysis, content creation, visual understanding, agentic tool use
The lowest latency and price, with extended thinking	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.

Combine models

Multi-model strategies pair a lower-cost model with a frontier model so that most tokens are billed at the lower rate. The two common patterns are an executor that escalates hard decisions to an advisor, and an orchestrator that delegates bulk work to lower-cost workers. See Optimizing for cost and intelligence for both strategies, measured examples, and implementation options.