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Entities

Extraction, identification, and structured data.

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Top capability factors for this outcome
How Balanced works

Balanced doesn't just pick a model. It understands your task, decides the best execution strategy, and delivers a result that exactly matches what the task requires. No more, no less.

No one else does this. Other APIs give you model access — you guess which model to use. Balanced eliminates the guessing.

The pipeline

1
Analyze — Balanced examines the task's requirements: what capabilities does it need? How complex is it? What quality level does it actually demand?
2
Decide — Should the task be executed in one step, or broken into subtasks? Simple tasks run as-is. Complex tasks get decomposed.
3
Select models — For each step (or subtask), Balanced picks the model that exactly matches the requirements. A subtask needing deep reasoning gets a powerful model. A simple subtask gets a fast, cheap one.
4
Execute & verify — Each step runs, the output is verified for quality and consistency. If something doesn't pass, it gets re-executed.
5
Assemble — All outputs are combined into one finished result. You receive a single, coherent deliverable — never fragments.

Why this is better

  • Better quality — each subtask gets the model that's best at that specific type of work
  • Lower cost — subtasks that don't need expensive models use cheap ones
  • No guessing — you don't have to choose a model or worry about over/underpaying
  • Exact fit — the result matches the task's actual requirements, not more, not less

Example

A market research report gets decomposed into: competitor identification (fast model, /bin/bash.02), strategy analysis (reasoning model, /bin/bash.15), trend detection (powerful model, /bin/bash.30), synthesis (writing model, /bin/bash.08). Total: /bin/bash.67 — cheaper than using an expensive model for everything (.00), with higher quality because each section was handled by the right specialist.

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