Selecting Models for Different Agent Tasks

In a production agent system, you don't just use "the biggest model." You use the most efficient model for the specific role in your graph. A high-performance agent often uses multiple models: a "Heavyweight" for planning, and "Lightweights" for simple extraction or tool validation.

In this lesson, we will explore the selection criteria for agentic LLMs and how to build a "Multi-Model" architecture.

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1. What Makes a Model "Agent-Ready"?

Not all LLMs are created equal for agency. A model might be great at writing poetry but terrible at following a tool schema.

Key Technical Requisites

1. Native Function Calling / Tool Use: Does the model have a specialized "Tool" API? (e.g., OpenAI's tools parameter or Anthropic's tool_use). Models without native tool-calling are significantly less reliable.
2. Structured Output (JSON Mode): Pure agency relies on JSON. If a model outputs "Sure, here is your JSON: json ...", your code has to strip the markdown, which adds latency and error risk.
3. Instruction Following (Recall): In an agent, the instructions are often long (System Prompt + History + Tool Descriptions). You need a model with high "Long Context Recall."
4. Latency (TTFT): For real-time agents, the Time to First Token is critical.

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2. Categorizing Models by "Gears"

Think of your model selection like a car's gearbox.

High Gear: The Orchestrator (Reasoning Heavy)

• Role: Deciding the plan, handling complex ambiguity, and re-ranking results.
• Models: Claude 3.5 Sonnet, GPT-4o, O1, Llama 3.1 405B.
• Why: These models understand "Why." They can handle complex branching logic without getting lost.

Medium Gear: The Tool Executor

• Role: Writing code, summarizing medium-sized docs, or classifying intent.
• Models: GPT-4o-mini, Claude 3.5 Haiku, Gemini 1.5 Flash.
• Why: They are 10x cheaper and 5x faster than orchestrators while maintaining high accuracy for "known" tasks.

Low Gear: The Utility Worker

• Role: Cleaning text, extracting a single date from a sentence, or simple sentiment analysis.
• Models: Llama 3 8B (local), Mistral 7B.
• Why: These can run locally (Module 12) or at near-zero cost, perfect for high-volume, low-complexity preprocessing.

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3. Comparative Benchmark for Agentic Tasks

Task	Top Pick	Reason
Logic/Planning	Claude 3.5 Sonnet	Industry favorite for reasoning and long-context safety.
Code Generation	GPT-4o	Highly specialized in Python/Javascript syntax.
High-Volume Tool Use	Gemini 1.5 Flash	Massive context window (1M+) and ultra-low cost.
Local / Privacy	Llama 3.1 70B	Best-in-class open-source reasoning.

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4. Multi-Model Architecture: The Router Pattern

In a production LangGraph system, you can define different LLMs for different nodes.

graph TD
    User -->|Query| Route[Node: Intent Classifier]
    Route -->|Complex| Orchestrator[Node: Claude 3.5 Sonnet]
    Route -->|Simple| Worker[Node: GPT-4o-mini]
    Orchestrator --> Tools[Tool Node]
    Worker --> Tools

Why Router?

• Cost: 80% of user queries are simple. Why pay "Sonnet" prices for a "Haiku" task?
• Latency: Smaller models respond faster, improving the "snappiness" of the UI.

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5. Avoiding "Model Lock"

Production agents should be Model Agnostic. Using LangChain's init_chat_model or standard abstractions allows you to swap a model in one line of code.

from langchain.chat_models import init_chat_model

# Swap 'openai' for 'anthropic' without changing the graph logic
model = init_chat_model("gpt-4o", model_provider="openai")

Warning: Every model has different "Prompt Sensitivity." A prompt that works for OpenAI might fail for Claude. You must test your "Agent Personas" against multiple models.

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6. The Context Window Strategy

When selecting a model, look at the Ratio of Context to Output.

• Agent Task: "Read these 50 emails and tell me if we owe anyone money."
• Requirement: Large Input Window (Claude's 200k or Gemini's 1M).
• Agent Task: "Write a 5,000-word technical spec."
• Requirement: High Output Token Limit. (Most models cap output at 4,096 or 8,192 tokens).

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Summary and Mental Model

Choose your model like you choose a Team Member.

• You don't hire a PhD Astrophysicist to sort the mail.
• You don't hire a high-school intern to design your cloud architecture.

Matching the IQ of the task to the IQ of the model is the first rule of AI unit economics.

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Exercise: Model Assignment

1. Scenario: An agent that summarizes a 1-hour YouTube transcript and then sends a Slack notification.
   • Planner Model: ?
   • Summarizer Model: ? (Note: High token count).
2. Scenario: A real-time voice agent that translates Spanish to English with <500ms delay.
   • Selection Priority: (Latency vs. Intelligence vs. Cost?)
3. The Budget: If you have $100 for 1 million tasks, and each task requires 500 tokens. Which model must you use? (Do the math!)