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The Anthropic Developer Platform – From First Call to Production

Lesson 3: Choosing the Right Model for Your Use Case

Lesson Objectives

By the end of this lesson, students should be able to:

  • Describe the trade-offs between Claude model tiers
  • Apply a use-case-driven model selection framework
  • Estimate the cost impact of model selection for a given request volume
  • Know when to change model selection mid-project

Lesson Content

The model tier framework.

Claude models are grouped into capability tiers (verify current models and naming at docs.anthropic.com/en/docs/about-claude/models):

Opus: Highest capability. Best for complex reasoning, nuanced writing, multi-step analysis, and tasks where quality is the primary constraint. Highest cost per token.

Sonnet: Balanced capability and cost. Suitable for most production applications – strong performance across a wide range of tasks without Opus pricing. The common default for production integrations.

Haiku: Fastest and cheapest. Strong for classification, extraction, simple summarization, routing, and tasks with well-defined low-complexity requirements. Poor choice for complex reasoning or nuanced generation.

The model selection decision framework.

For any application, answer four questions:

  1. Complexity: Does the task require sophisticated multi-step reasoning or nuanced judgment? -> Opus or Sonnet
  2. Latency sensitivity: Does the user experience require sub-second responses? -> Haiku or Sonnet
  3. Volume: How many requests per day/month? At what token cost does Opus become budget-prohibitive? -> Calculate and compare
  4. Quality threshold: What is the minimum acceptable output quality for the use case? -> Map to the cheapest tier that meets that threshold

Cost estimation.

Token pricing is published at anthropic.com/pricing (always verify current pricing – it changes). General approach:

  • Estimate average input token count per request (prompt + context)
  • Estimate average output token count per request (typical response length)
  • Multiply by expected daily/monthly request volume
  • Apply model-specific pricing per million tokens
  • Compare tiers against quality requirements

For most applications, Sonnet is the default starting point. Drop to Haiku for high-volume, low-complexity tasks. Move to Opus for tasks where quality failures have real cost.

When to change models.

Signals to upgrade the model:

  • Output quality is consistently failing to meet the use case threshold
  • Users are manually correcting AI outputs at a rate that suggests systematic capability limitation

Signals to downgrade:

  • Cost scaling beyond budget at current volume
  • Output quality substantially exceeds requirements (over-serving the use case)
  • Response latency is a user experience problem

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