Model Selection Skill

Choose the right model for custom agent tasks based on complexity, cost, and performance requirements.

Interactive Model Selection

Use AskUserQuestion to understand requirements and recommend the optimal model:

Question 1: Primary Priority (MCP: CLI best practices - tradeoff selection)

question: "What is your primary priority for this agent?" header: "Priority" options:

• label: "Cost Efficiency (Recommended)" description: "Minimize API costs, high-volume operations"
• label: "Balanced Performance" description: "Good quality at reasonable cost for most tasks"
• label: "Maximum Quality" description: "Best results regardless of cost, complex reasoning"
• label: "Lowest Latency" description: "Real-time responses, user-facing interactions"

Question 2: Task Complexity (MCP: Agent SDK model selection)

question: "How complex is the task this agent will perform?" header: "Complexity" options:

• label: "Simple" description: "Transformations, extraction, formatting, classification"
• label: "Moderate" description: "Code generation, analysis, planning, most tasks"
• label: "Complex" description: "Architecture decisions, multi-step reasoning, critical code"
• label: "Variable" description: "Mix of simple and complex tasks in one agent"

Use these responses to apply the decision tree and recommend the appropriate model.

Purpose

Guide selection of appropriate Claude model (Haiku, Sonnet, Opus) for custom agent tasks to optimize cost, speed, and quality.

When to Use

• Designing a new custom agent

• Optimizing existing agent performance

• Balancing cost vs quality

• Meeting specific latency requirements

Model Overview

Model Speed Cost Quality Use Case

Haiku Fastest Lowest Good Simple tasks, high volume

Sonnet Fast Medium Very Good Most tasks, balanced

Opus Slowest Highest Best Complex reasoning

Selection Decision Tree

START │ ├── Is task simple transformation? │ └── YES → Haiku │ ├── Is cost the primary concern? │ └── YES → Haiku (if adequate) or Sonnet │ ├── Is quality critical (no room for error)? │ └── YES → Opus │ ├── Does task require complex reasoning? │ └── YES → Opus │ ├── Is latency critical (real-time)? │ └── YES → Haiku │ └── DEFAULT → Sonnet (best balance)

Model Selection by Task Type

Haiku Tasks

Best for:

• Text transformations (uppercase, formatting)

• Simple classification

• Data extraction

• High-volume operations

• Real-time processing

• Pattern matching

Haiku examples

model="claude-3-5-haiku-20241022"

Echo agent - simple transformation

Calculator - straightforward math

Stream processor - high volume, low complexity

Sonnet Tasks

Best for:

• Code generation

• Code review

• Planning and analysis

• Most custom agents

• Balanced performance

Sonnet examples

model="claude-sonnet-4-20250514"

QA agent - codebase analysis

Builder agent - code implementation

General-purpose agents

Opus Tasks

Best for:

• Strategic planning

• Complex architectural decisions

• Critical code review

• Multi-step reasoning

• Novel problem solving

Opus examples

model="claude-opus-4-20250514"

Planner agent - strategic decisions

Reviewer agent - critical validation

Architect agent - system design

Cost Considerations

Relative Costs

Model Input Tokens Output Tokens Relative Cost

Haiku Low Low 1x

Sonnet Medium Medium ~10x

Opus High High ~30x

Cost Optimization Strategies

• Start with Haiku: Test if simpler model is adequate

• Use Haiku for preprocessing: Filter/classify before main task

• Reserve Opus for critical paths: Only where quality is paramount

• Monitor costs: Track ResultMessage.total_cost_usd

Cost tracking

async for message in client.receive_response(): if isinstance(message, ResultMessage): print(f"Query cost: ${message.total_cost_usd:.6f}")

Speed Considerations

Latency Profiles

Model First Token Total Time Throughput

Haiku ~500ms Fast Highest

Sonnet ~1s Medium Good

Opus ~2s Slower Lower

Speed Optimization

• Real-time needs Haiku: Sub-second response

• Interactive needs Sonnet: Acceptable latency

• Batch allows Opus: Latency less critical

Quality Considerations

Capability Differences

Capability Haiku Sonnet Opus

Simple reasoning ✓ ✓ ✓

Code generation Limited Good Excellent

Complex planning Poor Good Excellent

Multi-step reasoning Limited Good Excellent

Novel problems Poor Adequate Excellent

Quality Requirements

• Haiku: Acceptable for well-defined, simple tasks

• Sonnet: Good for most development tasks

• Opus: Required for critical decisions

Multi-Model Patterns

Tiered Processing

Tier 1: Haiku for classification

classification = await classify_task(task, model="haiku")

Tier 2: Route to appropriate model

if classification == "simple": result = await process(task, model="haiku") elif classification == "complex": result = await process(task, model="opus") else: result = await process(task, model="sonnet")

Multi-Agent with Different Models

Planner: Opus for strategic decisions

planner_options = ClaudeAgentOptions( model="claude-opus-4-20250514" )

Builder: Sonnet for implementation

builder_options = ClaudeAgentOptions( model="claude-sonnet-4-20250514" )

Reviewer: Opus for critical review

reviewer_options = ClaudeAgentOptions( model="claude-opus-4-20250514" )

Output Format

When recommending model selection:

Model Selection

Task: [description] Recommended Model: [Haiku/Sonnet/Opus]

Decision Factors

Rationale

[Why this model is appropriate]

Alternatives

• If cost is concern: [alternative]
• If quality is critical: [alternative]

Configuration

options = ClaudeAgentOptions( model="[model-id]", ... )

Selection Checklist

• Task complexity assessed

• Cost constraints identified

• Quality requirements defined

• Latency requirements considered

• Model selected with rationale

• Alternatives documented

Key Insights

"Choose wisely: Claude Haiku for simple, fast tasks. Claude Sonnet for balanced performance. Claude Opus for complex reasoning."

Model selection directly impacts:

• User experience (latency)

• Operational cost (tokens)

• Output quality (accuracy)

Cross-References

• @core-four-custom.md - Model in Core Four

• @custom-agent-design skill - Agent design workflow

• @agent-deployment-forms.md - Deployment considerations

Version History

• v1.0.0 (2025-12-26): Initial release

Last Updated

Date: 2025-12-26 Model: claude-opus-4-5-20251101