Claude Code Sub-Agents: Optimizing Performance and Cost with Model Selection#

Published: August 23, 2025

Claude Code recently introduced a game-changing feature that addresses one of the most requested enhancements from the developer community: the ability to specify different Claude models for individual sub-agents. This feature, released in version 1.0.64, enables developers to optimize both performance and costs by strategically allocating different models to different tasks within their AI workflows.

The Evolution of Sub-Agent Architecture#

Before diving into the new feature, let’s understand the sub-agent architecture in Claude Code and why this enhancement matters.

What Are Sub-Agents?#

Sub-agents in Claude Code are specialized AI agents that can be invoked by the main agent to handle specific tasks. They allow developers to create modular, reusable components with focused responsibilities—similar to how microservices work in distributed systems.

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graph TD
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    subgraph "Claude Code Agent System"
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        MA[Main Agent<br/>Orchestrator]
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        SA1[Sub-Agent 1<br/>Code Review]
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        SA2[Sub-Agent 2<br/>Architecture]
6
        SA3[Sub-Agent 3<br/>Testing]
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        SA4[Sub-Agent 4<br/>Documentation]
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    end
9

10
    U[User Request] --> MA
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    MA --> SA1
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    MA --> SA2
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    MA --> SA3
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    MA --> SA4
15

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    SA1 --> MA
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    SA2 --> MA
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    SA3 --> MA
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    SA4 --> MA
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    MA --> R[Response]
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23
    style MA fill:#e3f2fd
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    style SA1 fill:#fff3e0
25
    style SA2 fill:#fff3e0
26
    style SA3 fill:#fff3e0
27
    style SA4 fill:#fff3e0

The Previous Limitation#

Prior to version 1.0.64, all sub-agents inherited the model from the main agent. This meant if you started Claude Code with --model opus, every sub-agent would also use Opus, regardless of task complexity. This one-size-fits-all approach had significant drawbacks:

Cost Inefficiency: Simple tasks consumed expensive Opus tokens unnecessarily
Resource Waste: Complex models were overkill for straightforward operations
Limited Optimization: No ability to leverage model-specific strengths

The New Model Parameter Feature#

The enhancement adds an optional model parameter to sub-agent configurations, allowing developers to specify which Claude model each sub-agent should use.

Syntax and Configuration#

The updated sub-agent YAML frontmatter now supports:

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---
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name: your-sub-agent-name
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description: Description of when this sub agent should be invoked
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tools: tool1, tool2, tool3  # Optional - inherits all tools if omitted
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model: sonnet  # NEW: Optional - inherits main agent's model if omitted
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---
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Your sub agent's system prompt goes here...

Supported Model Values#

The model parameter accepts:

Model aliases: sonnet, opus
Full model names: claude-sonnet-4-20250514, etc.
Default behavior: If omitted, inherits from main agent

Real-World Use Cases and Patterns#

Pattern 1: Task Complexity Routing#

Different tasks require different levels of reasoning. Here’s an optimal configuration:

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graph LR
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    subgraph "Task Complexity"
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        T1[Simple Tasks]
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        T2[Medium Tasks]
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        T3[Complex Tasks]
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    end
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    subgraph "Model Selection"
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        M1[Haiku/Sonnet]
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        M2[Sonnet]
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        M3[Opus]
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    end
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    subgraph "Examples"
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        E1[Code Formatting<br/>Syntax Checking]
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        E2[Code Review<br/>Bug Detection]
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        E3[Architecture Design<br/>Algorithm Creation]
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    end
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    T1 --> M1
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    T2 --> M2
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    T3 --> M3
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    M1 --> E1
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    M2 --> E2
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    M3 --> E3
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    style T1 fill:#c8e6c9
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    style T2 fill:#fff9c4
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    style T3 fill:#ffcdd2

Pattern 2: Cost-Optimized Development Workflow#

Here’s a practical example of a cost-optimized agent configuration:

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# Main agent (uses Opus for orchestration)
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claude-code --model opus
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# code-formatter.agent
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---
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name: code-formatter
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description: Formats and lints code according to project standards
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tools: filesystem
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model: sonnet  # Simple pattern matching, use cheaper model
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---
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You are a code formatting specialist. Apply consistent formatting...
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# code-reviewer.agent
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---
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name: code-reviewer
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description: Reviews code for bugs, security issues, and best practices
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tools: filesystem, search
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model: sonnet  # Moderate complexity, Sonnet is sufficient
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---
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You are an expert code reviewer. Analyze code for potential issues...
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# architect.agent
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---
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name: architect
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description: Designs system architecture and makes strategic decisions
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tools: filesystem, search, web
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model: opus  # Complex reasoning required, use most capable model
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---
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You are a senior system architect with expertise in distributed systems...
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# test-writer.agent
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---
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name: test-writer
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description: Writes comprehensive unit and integration tests
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tools: filesystem
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model: sonnet  # Template-based generation, Sonnet handles well
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---
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You specialize in writing thorough test suites...

Pattern 3: Specialized Model Strengths#

Different Claude models have different strengths. Leverage them strategically:

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mindmap
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  root((Model Selection Strategy))
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    Opus
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      Complex Reasoning
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        System Design
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        Algorithm Development
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        Strategic Planning
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      Creative Tasks
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        Novel Solutions
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        Architecture Innovation
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      Multi-step Logic
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        Debugging Complex Issues
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        Performance Optimization
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    Sonnet
15
      Balanced Tasks
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        Code Review
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        Documentation
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        Refactoring
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      Pattern Recognition
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        Bug Detection
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        Style Violations
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      Structured Generation
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        Test Writing
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        API Documentation
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    Haiku (Future)
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      Simple Tasks
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        Formatting
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        Linting
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        Syntax Checking
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      Quick Responses
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        Code Completion
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        Simple Queries

Implementation Examples#

Example 1: Full-Stack Development Team#

Create a team of specialized sub-agents for full-stack development:

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# frontend.agent
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---
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name: frontend-developer
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description: Handles React, TypeScript, and UI/UX implementation
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tools: filesystem, search
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model: sonnet
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---
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You are a frontend specialist focused on React and TypeScript...
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# backend.agent
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---
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name: backend-developer
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description: Develops APIs, handles database design, and server logic
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tools: filesystem, search, bash
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model: sonnet
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---
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You are a backend engineer specializing in Node.js and PostgreSQL...
18

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# devops.agent
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---
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name: devops-engineer
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description: Manages deployment, CI/CD, and infrastructure
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tools: filesystem, bash, web
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model: sonnet
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---
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You handle Docker, Kubernetes, and cloud infrastructure...
27

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# security.agent
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---
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name: security-analyst
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description: Performs security audits and implements security measures
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tools: filesystem, search, web
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model: opus  # Security requires deep analysis
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---
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You are a security expert who identifies vulnerabilities...

Example 2: Data Science Pipeline#

Optimize a data science workflow with appropriate models:

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# data-cleaner.agent
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---
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name: data-cleaner
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description: Cleans and preprocesses datasets
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tools: filesystem, python
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model: sonnet  # Routine data operations
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---
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You specialize in data cleaning and preprocessing...
9

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# ml-engineer.agent
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---
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name: ml-engineer
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description: Designs and trains machine learning models
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tools: filesystem, python, web
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model: opus  # Complex ML architecture decisions
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---
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You are an ML engineer who designs sophisticated models...
18

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# visualizer.agent
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---
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name: data-visualizer
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description: Creates charts and visualizations
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tools: filesystem, python
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model: sonnet  # Template-based visualization
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---
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You create compelling data visualizations...

Cost-Benefit Analysis#

Let’s analyze the potential cost savings with strategic model selection:

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graph TD
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    subgraph "Before: All Opus"
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        B1[100% Opus Usage]
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        B2[High Cost]
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        B3[~$15/million tokens]
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    end
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    subgraph "After: Mixed Models"
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        A1[20% Opus<br/>80% Sonnet]
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        A2[Reduced Cost]
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        A3[~$6/million tokens]
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    end
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    subgraph "Savings"
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        S1[60% Cost Reduction]
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        S2[No Performance Loss]
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        S3[Better Resource Allocation]
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    end
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    B1 --> A1
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    B2 --> A2
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    B3 --> A3
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    A1 --> S1
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    A2 --> S2
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    A3 --> S3
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    style B2 fill:#ffcdd2
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    style A2 fill:#c8e6c9
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    style S1 fill:#4caf50,color:#fff

Token Usage Patterns#

Based on typical development workflows:

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pie title "Typical Task Distribution in Development"
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    "Simple Tasks (40%)" : 40
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    "Moderate Tasks (45%)" : 45
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    "Complex Tasks (15%)" : 15

By mapping these to appropriate models:

Simple Tasks → Sonnet/Haiku: 40% of tokens
Moderate Tasks → Sonnet: 45% of tokens
Complex Tasks → Opus: 15% of tokens

Result: 85% of tokens can use more cost-effective models without sacrificing quality.

Best Practices and Guidelines#

1. Task Classification Framework#

Before assigning models, classify your tasks:

Task Category	Characteristics	Recommended Model	Examples
Simple	Pattern matching, templates	Sonnet/Haiku	Formatting, linting
Moderate	Rule-based analysis	Sonnet	Code review, testing
Complex	Creative problem-solving	Opus	Architecture, algorithms
Critical	High-stakes decisions	Opus	Security, optimization

2. Progressive Enhancement Strategy#

Start with the simplest model and upgrade only when needed:

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graph LR
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    Start[Task Analysis] --> Q1{Simple<br/>Pattern?}
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    Q1 -->|Yes| M1[Use Sonnet]
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    Q1 -->|No| Q2{Requires<br/>Creativity?}
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    Q2 -->|No| M2[Use Sonnet]
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    Q2 -->|Yes| Q3{High<br/>Stakes?}
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    Q3 -->|No| M3[Consider Sonnet]
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    Q3 -->|Yes| M4[Use Opus]
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    style M1 fill:#c8e6c9
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    style M2 fill:#c8e6c9
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    style M3 fill:#fff9c4
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    style M4 fill:#ffcdd2

3. Monitoring and Optimization#

Track performance metrics to optimize model selection:

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sub_agents:
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  code_reviewer:
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    model: sonnet
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    success_rate: 94%
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    avg_tokens: 2500
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    cost_per_task: $0.075
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  architect:
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    model: opus
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    success_rate: 98%
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    avg_tokens: 5000
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    cost_per_task: $0.75

Migration Guide#

For existing Claude Code users, here’s how to migrate:

Step 1: Audit Current Sub-Agents#

List all your existing sub-agents and their typical tasks:

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# Find all agent files
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find . -name "*.agent" -type f
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# Review each agent's complexity
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cat each-agent.agent

Step 2: Classify by Complexity#

Create a classification matrix:

Agent Name	Current Model	Task Complexity	Recommended Model
formatter	Inherited (Opus)	Simple	Sonnet
reviewer	Inherited (Opus)	Moderate	Sonnet
architect	Inherited (Opus)	Complex	Opus

Step 3: Update Agent Configurations#

Add the model parameter to each agent file:

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# Example update
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sed -i '/^---$/a model: sonnet' formatter.agent

Step 4: Test and Validate#

Run your workflows with the new configuration:

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# Test with verbose output to monitor model usage
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claude-code --model opus --verbose

Future Implications#

This feature opens doors for several future enhancements:

Dynamic Model Selection#

Future versions might support dynamic model selection based on:

Task complexity analysis
Token budget constraints
Response time requirements
Error rates and retry logic

Model Cascading#

Implement fallback strategies:

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model:
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  primary: sonnet
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  fallback: opus  # Use if Sonnet fails
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  budget_exceeded: haiku  # Use if over budget

Performance Profiling#

Built-in profiling to suggest optimal model configurations:

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claude-code --profile-models --analyze-costs

Community Impact#

The introduction of this feature has significant implications:

Developer Feedback#

The community response has been overwhelmingly positive:

+9000 votes on the feature request
Immediate adoption by power users
Requests for similar functionality in custom commands

Economic Impact#

For teams using Claude Code extensively:

50-70% cost reduction reported by early adopters
No significant performance degradation
Improved response times for simple tasks

Ecosystem Growth#

This feature enables:

More sophisticated agent marketplaces
Specialized agent libraries
Cost-optimized agent templates

Conclusion#

The model parameter feature for sub-agents represents a significant evolution in Claude Code’s architecture. It transforms sub-agents from simple task delegators into a sophisticated, cost-optimized system that can leverage the full spectrum of Claude’s model family.

Key takeaways:

Strategic model selection can reduce costs by 60% or more
Task-appropriate models improve both efficiency and performance
Modular architecture enables fine-grained optimization
Progressive enhancement ensures optimal resource utilization

As AI development tools mature, features like this demonstrate the importance of flexibility and optimization in production systems. The ability to match model capabilities to task requirements isn’t just about cost—it’s about building sustainable, scalable AI workflows that can grow with your needs.

The sub-agent model parameter is more than a feature; it’s a paradigm shift in how we think about AI agent orchestration. By enabling developers to optimize at the task level rather than the session level, Claude Code has taken a significant step toward truly efficient AI-powered development.

Note: This feature is available in Claude Code version 1.0.64 and later. Update your installation to access this functionality.

Quick Reference#

Configuration Template#

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---
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name: agent-name
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description: Agent description
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tools: tool1, tool2  # Optional
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model: sonnet  # Optional: sonnet | opus | full-model-name
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---
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Agent prompt here...

Model Selection Cheat Sheet#

Use Case	Recommended Model	Rationale
Code formatting	Sonnet	Pattern-based
Bug detection	Sonnet	Rule analysis
Code generation	Sonnet/Opus	Complexity-dependent
Architecture design	Opus	Creative reasoning
Security analysis	Opus	Deep analysis
Documentation	Sonnet	Structured output
Testing	Sonnet	Template-based
Performance optimization	Opus	Complex analysis

Start optimizing your Claude Code workflows today with strategic model selection!