Craft · Model fluency
The right model for the right task.
Know the strengths and weaknesses of every model, and pick the right one for each task instead of one model for everything. Reasoning depth, speed, cost, context window, tool use and languages differ from family to family; the choice is an engineering decision, not a preference.
There is no best model, only the right fit
No model is best everywhere; there is only a best fit per task. Each family trades reasoning depth, speed, cost, context window, tool use and language quality differently. A deep-reasoning model drafting a contract clause is overkill for tagging support tickets; a fast, cheap model that classifies thousands of documents will stumble through a multi-step legal analysis. Fluency is matching the task profile to the model profile, and knowing when a smaller model with good context beats a larger one.
Choosing is an engineering decision
We don't choose by benchmark headline or vendor slide. First we define the task: required reasoning depth, latency budget, cost per call at your volume, context size, whether it must call tools or stay reliably in German. Then we test the candidates against your real data and measure. The model is one swappable component in the workflow, not the workflow itself, so the rest of the system stays stable when the model changes.
Models change monthly, so does the choice
The right model today can be outdated, cheaper, or replaced within a quarter. We keep the choice deliberate and revisit it on a schedule, with workflows built so a model can be swapped without rebuilding everything around it. New releases are measured against the same task definition, and adopted only when they demonstrably help. No chasing every announcement, no lock-in to yesterday's default.
When one model does everything.
"One model for everything" feels simple, and quietly costs money, speed and reliability. The most common patterns:
- 01 The most expensive model for every trivial task Cost
- 02 A cheap model for multi-step reasoning Errors
- 03 Chosen by benchmark headline, not by task Hype
- 04 English-tuned, weak in the Swiss context Language
- 05 Picked once, never reviewed again Stale
- 06 Model hard-wired, not swappable Lock-in
None of these is an AI problem. It's a missing model decision.
Which model fits your tasks?
In the AI-Readiness audit we map your workflows and show where each model genuinely fits, and where one model for everything quietly costs money and reliability.