---
type: concept
title: "Multi-Model Agents"
description: "Running an agent across more than one LLM, or combining several models together, to leverage each model's distinct strengths at runtime."
tags: [mixture-of-agents, model-selection, multi-model]
videos: [the-secret-sauce-for-simple-powerful-ai-apps-ai-agents-masterclass-2, the-only-ai-developer-roadmap-you-need-in-2024]
created: 2026-07-21
updated: 2026-07-21
---

# Multi-Model Agents

A multi-model agent is one that uses more than a single LLM, either swapping between models depending on the task or combining several so their strengths compound. Cole names the idea directly: "mixture of Agents using different AI models to work together better than one model could work by itself that's a really cool concept" [0:14:30]. The premise is that different frontier models genuinely behave differently on the same input, "I've actually noticed that Claude and GPT will give different responses sometimes with this kind of task creation ... you can always try different models if you need a different kind of response" [0:11:58]. Once you accept that models have distinct personalities and failure modes, running an agent across more than one becomes a lever rather than an accident.

There are two flavors worth separating. The first is model switching: one agent whose backing model is a swappable parameter, so it can route a task to Claude, GPT, or another model as needed, "something like this into a multimodel agent that can actually use different models like Claude and GPT" [0:00:45]. The second is mixture-of-agents, where multiple models run together on the same problem and their outputs are blended or cross-checked to beat what any one could do alone. Both rely on the agent being written so the model is not hard-coded, which is exactly the decoupling that provider independence buys you.

The practical upside is resilience and quality: if one model is weak at a step, another covers it, and a router can send cheap tasks to a small fast model and hard ones to a stronger, pricier model. The main cost is added complexity, so multi-model is a deliberate choice made on top of a clear-eyed model-selection process, not a default.

## Builds on

- [Model Selection](./model-selection.md) - a multi-model agent is model selection applied at runtime instead of once at design time.

## Related

- [Provider Independence](./provider-independence.md) - decoupling the agent from any one vendor is the prerequisite for swapping models freely.
- [Agent Teams](./agent-teams.md) - mixture-of-agents overlaps with multi-agent teams where each specialist runs on a different model.
- [Local LLMs as Agents](./local-llms-as-agents.md) - a local model can be one of the models in the mix.
- [Agent Architecture Patterns](./agent-architecture-patterns.md) - routing is the pattern that decides which model handles a given request.
- [What Is an AI Agent](./what-is-an-ai-agent.md) - the primitive that a multi-model setup varies the backing model of.
- [Reasoning Model as a Tool](./reasoning-model-as-a-tool.md) - Architecture where a fast, lightweight model drives the conversation and calls a slow, powerful reasoning model as just another tool, keeping the flow nimble while still getting reasoning-grade answers on the hard step.
- [LLM Cost Optimization](./llm-cost-optimization.md) - Systematically cutting the bill for running LLM applications at scale by sending the bulk of cheap-to-serve traffic to far cheaper models and paying premium rates only for the rare hard requests.

## Tools

- [OpenRouter](../entities/tools/openrouter.md) - a single API surface for reaching many models, which makes multi-model routing straightforward.
- [Claude](../entities/tools/claude.md) - one of the models Cole swaps between for different response styles.
- [GPT-4o](../entities/tools/gpt-4o.md) - the OpenAI model paired against Claude in the same agent.
- [Pydantic AI](../entities/tools/pydantic-ai.md) - lets the backing model be a swappable parameter of the agent.
- [Builder.io](../entities/tools/builder-io.md) - Figma-to-code product Cole cites as the canonical case where training a custom model beat general GPT, then used GPT to refine the generated code.

## Sources

- [The ONLY AI Developer Roadmap You Need in 2024](../sources/the-only-ai-developer-roadmap-you-need-in-2024.md) - "[0:14:30] mixture of Agents using different AI models to work together better than one model could work by itself that's a really cool concept"
- [The Secret Sauce for SIMPLE + POWERFUL AI Apps - AI Agents Masterclass #2](../sources/the-secret-sauce-for-simple-powerful-ai-apps-ai-agents-masterclass-2.md) - "[0:11:58] I've actually noticed that Claude and GPT will give different responses sometimes with this kind of task creation ... you can always try different models if you need a different kind of response"
- [The Secret Sauce for SIMPLE + POWERFUL AI Apps - AI Agents Masterclass #2](../sources/the-secret-sauce-for-simple-powerful-ai-apps-ai-agents-masterclass-2.md) - "[0:00:45] there's even a bonus at the end where I can show how you can make something like this into a multimodel agent that can actually use different models like Claude and GPT"
