Going Single Model vs Orchestrating Across 4 LLMs
Going Single Model vs Orchestrating Across 4 LLMs
There is a moment in every agent project where you step back and look at what you have built: a routing layer that sends requests to GPT-4 for planning, Claude for coding, Gemini for vision, and a local model for quick classifications. It works, mostly. And then you ask: what if I just used one model for everything?
The Multi-Model Trap
Orchestrating across multiple LLMs sounds smart. Each model has strengths, so you route each task to the best model. In practice, you end up maintaining four different prompt formats, handling four different rate limit systems, debugging four different failure modes, and managing four different billing accounts.
The routing logic itself becomes a source of bugs. Did the planner misclassify this task? Is the vision model getting requests it should not? Why did the coding model get a planning prompt and produce garbage?
The Nuclear Reset
Going single model means accepting that one model will be worse at some tasks in exchange for dramatic simplification. You lose optimal performance on individual tasks. You gain a system you can actually reason about.
One prompt format. One rate limit to manage. One billing account. One set of failure modes to understand. One model whose behavior you learn deeply over weeks of use instead of four models you understand superficially.
When Single Model Wins
For most agent workflows, a single capable model - Claude or GPT-4 class - handles planning, coding, and text tasks well enough. The gap between "best model for this task" and "good enough model for this task" is smaller than the cost of orchestration complexity.
The exception is vision. If your agent needs to understand screenshots, you might need a multimodal model. But even there, the trend is toward models that handle text and vision in one system.
Start with one model. Only add a second when you have clear evidence that the single model cannot handle a specific task category, not when you theoretically believe a different model would be better.
Fazm is an open source macOS AI agent. Open source on GitHub.