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Different models excel at different tasks. GPT handles reasoning and tool use well. Claude writes better prose. Specialized models exist for code, math, and domain-specific work. Handoffs let agents route subtasks to the right model. If you’ve already built an MCP + tools workflow, handoffs let you keep a fast “coordinator” model most of the time and route to stronger models only when needed. 
import asyncio
from dedalus_labs import AsyncDedalus, DedalusRunner
from dotenv import load_dotenv

load_dotenv()

async def main():
client = AsyncDedalus()
runner = DedalusRunner(client)

    result = await runner.run(
        input=(
            "Find me the nearest basketball games in January in San Francisco, then write a concise plan for attending."
        ),
        model=["openai/gpt-5.2", "anthropic/claude-opus-4-5"],
        mcp_servers=["windsor/ticketmaster-mcp"],  # Discover events via Ticketmaster
    )

    print(result.final_output)

if **name** == "**main**":
asyncio.run(main())

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When to Use Handoffs

Handoffs shine when a task has distinct phases requiring different capabilities:
  • Research → Writing: GPT gathers information, Claude writes the final piece
  • Analysis → Code: A reasoning model plans the approach, a code model implements it
  • Triage → Specialist: A general model routes to domain-specific models
For simple tasks where one model handles everything, stick to a single model.

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Model Strengths

A rough guide to model selection:
TaskGood Models
Tool calling, reasoningopenai/gpt-5.2, xai/grok-4-1-fast-reasoning
Writing, creative workanthropic/claude-opus-4-5
Code generationanthropic/claude-opus-4-5, openai/gpt-5-codex
Fast, cheap responsesgpt-5-mini

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Next steps

Images & Vision

Add image generation/vision to your workflow

Use Cases

Multi-capability patterns
Last modified on April 9, 2026