FARGO FACTOR INSIGHT
AI Integration & Systems
Why I Use Multiple LLMs in AI Agent Systems
I use multiple LLMs because different models give me different results on real work. I add another model only when the difference is meaningful and recurring for a specific task. I am not looking for one winner. I am deciding which model makes sense for each job.
If you are building AI into your business, I think “ChatGPT or Claude?” is the wrong question.
My answer is both.
I use multiple large language models because I do not want one model’s weaknesses controlling my entire AI system.
At the time of this recording, I am using three and preparing to add a fourth. When I build AI agents, I want to use the strengths of different models instead of forcing one model to handle everything.
The decision is not ChatGPT vs. Claude
ChatGPT can be excellent until it stops giving me what I need.
Then I may move the work to Claude.
Claude can be excellent too. It can also become frustrating.
That experience changed how I think about choosing AI.
I stopped treating the decision as:
Which AI should I use?
I started treating it as:
Which AI should I use for this part of the work?
That is a different operating model.
ChatGPT is OpenAI’s conversational AI assistant. Claude is an AI platform built by Anthropic. When I talk about using multiple LLMs, I am talking about working across systems like these and the different models behind them rather than committing my business to one AI system.
Why I use multiple LLMs
The reason is practical.
Different AI systems do not always give me the same quality of result for the same kind of work.
When one becomes the bottleneck, I want another option.
That matters even more as I move beyond individual chats and build AI-enabled workflows and agents.
Instead of asking one model to be the researcher, thinker, critic, writer, analyst, and problem solver for everything, I can build a system that uses different models where they make sense.
That is how I am approaching AI inside Fargo Factor.
Not one AI.
A system of AI tools that I can use deliberately.
Multiple models can be part of the agent architecture
This is not only a personal workaround.
OpenAI’s Agents SDK allows a model to be specified for an individual agent and explicitly supports mixing different model providers across different agents.
That matters because it means model selection can happen inside the architecture of an agent system, not only when you manually open a different AI application.
Anthropic’s own model guidance also describes model selection as a balance among capability, speed, and cost.
Neither point means every business needs four models.
It means there is a legitimate reason to stop assuming one model should automatically handle every job.
What this costs me
There is a tradeoff.
As I move from using three AI systems toward four, I expect my total cost to be roughly $300 to $400 per month.
That is my estimate for my setup.
It is not a benchmark for your business and it is not a guaranteed return.
My reason for accepting the cost is straightforward: I expect the system to give me back hours each week and help me get more work done.
That is the calculation I care about.
Not whether I can save another $20 on an AI subscription.
I care whether the total system creates more value than it costs me in money, time, and friction.
When should you add another LLM?
Do not subscribe to four AI platforms because I do.
Start with the work.
If your current AI repeatedly becomes the limiting factor on a real task, test that task in another system.
Run the same work through ChatGPT and Claude.
Look at which one gives you the more useful result for that specific job.
If the difference is meaningful and recurring, you have a business reason to keep both.
Then apply the same thinking when you build agents.
Do not choose a model for an agent because that is the model you always use.
Choose it because it makes sense for what that agent needs to accomplish.
Stop looking for one AI winner
I do not think the advantage comes from finding the one large language model that beats every other model.
I think the advantage comes from knowing how to use multiple models as parts of a larger system.
That is why I use ChatGPT and Claude.
That is why I am expanding from three AI systems to four.
And that is why I am building AI agents around the idea that different models can play different roles.
The goal is not more AI. The goal is a better system for getting the work done.
Jeff Fargo is the founder of Fargo Factor. He helps business owners streamline and scale their businesses with practical AI-enabled systems.
Sources
Before you add another model, fix the system decision
More AI subscriptions do not create leverage by themselves. The useful question is where AI belongs, what each part of the workflow needs, and whether another model solves a recurring constraint. Fargo Factor helps business owners make those integration decisions deliberately.
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