How We Pick the Right Model for Each Request
Ask ten engineers for the best AI model and you get ten answers. All of them are partially right.
The best model for debugging code is not the best model for classifying support tickets. The best model for a customer-facing chatbot is not the best model for batch-processing ten thousand rows overnight.
Autark's job is to make that decision on every request so your team doesn't have to.
There is no single best model
The gap between different models on everyday knowledge work has narrowed. What matters now isn't which model is "best" — it's which model is best for what you're asking. And the answer changes depending on the task.
A model that excels at financial analysis might struggle with creative writing. The model that writes the best subject lines isn't the one you want drafting legal analysis. Using one model for everything means leaving quality on the table — or overpaying.
What happens on every request
When a request hits the Autark API, our routing engine evaluates:
- What kind of task is this? — analysis, writing, classification, reasoning, coding
- What's the right capability level? — fast and cost-optimized, or deep and thorough
- Which model is proven best for this? — based on continuous evaluation against real business tasks
- Are all providers healthy? — automatic failover if a backend is degraded
You send your request. We handle the routing. The response shape stays OpenAI-compatible.
The three capability levels
| Mode | Best for | Tradeoff |
|---|---|---|
| autark-flash | Simple queries, high throughput, classification | Lowest cost |
| autark | Chat, agents, everyday workloads | Balanced quality and cost |
| autark-deep | Complex reasoning, multi-step analysis | Highest capability |
See pricing for current per-token rates. You pay Autark's tier price; we manage the blended cost behind the scenes.
What "autark" actually does
autark is what most teams should use. It identifies your task, checks which models have proven edge on that type of work, and routes accordingly. As we evaluate new models and update our routing, your results improve automatically — no code changes required.
If you have specific latency or cost constraints, autark-flash and autark-deep give you explicit control while still benefiting from task-aware routing.
The routing advantage
We continuously evaluate models against real business tasks — not just academic benchmarks. When a new model proves itself better at a specific type of work, it enters the rotation. When an existing model drifts or degrades, it gets deprioritized.
The result: your requests always go to the model that's current best for what you're asking. Not the model that was best last month. Not the model with the highest benchmark score. The model that's actually proven to deliver on your type of task.
One endpoint. One API key. Always the right model for the job.