Multi-Model Routing: The Right AI for Every Task

Most teams pick one model and send every task through it. That works until the bill arrives, or until a task the model isn't suited for produces a bad result. The fix isn't a smarter single model — it's routing: matching each task to whichever model is actually the right tool for it.

The Decision Matrix We Use

Every task we route falls into one of four buckets:

• Complex reasoning and code → Claude or OpenAI's GPT-5.6 Sol / Codex. These are the models we reach for on architecture decisions, multi-step agent logic, and anything where a wrong answer is expensive.
• High-volume, low-complexity tasks → smaller hosted models like Gemma or Llama 3. Classification, tagging, simple extraction — tasks where speed and cost matter more than reasoning depth.
• Privacy-sensitive or offline work → local inference via Ollama. No data leaves the machine, no per-token cost, at the price of some capability.
• Structured extraction → a fine-tuned small model where the task is narrow and repetitive enough that a general-purpose LLM is overkill.

Building the Routing Layer

In practice this is a thin dispatch layer in front of your agents, not a separate product. A request comes in, gets classified by task type and complexity, and gets sent to the model tier that fits. The classification step can be as simple as a rules-based check (task type, input length, whether the output needs to be verifiable) — it doesn't need its own model in most small-business setups.

What to Actually Do

If you're running everything through one model today, start by logging what each request actually needed — not what model handled it. Most teams find a large share of their traffic could have gone through a cheaper tier with no quality loss. That gap is where routing pays for itself.