CodeSOTA / AI task routing

One routing layer.
Every kind of AI task.

Start with the work you need done. Compare models across quality and cost, inspect the evidence, and use a common API to choose a route.

Available today: model selection across the registry and an execution API for code & text. Document, speech, and vision execution are planned.

What do you want to do?

Choose a task, compare actual benchmark observations, and inspect the Pareto frontier before selecting a route.

Benchmark tradeoffs

Find the Pareto frontier.

Every dot is a recorded result. Highlighted models have no competitor in this cohort that is at least as good on both axes and strictly better on one.

Loading benchmark observations…

01 / Define the work

Documents

Input
Scans & PDFs
Target output
Text, tables & fields

Registry selection only · execution planned

Target outputs describe the task. This preview returns model recommendations. It does not process your files or run inference.

Explore documents evidence →

02 / Inspect the shortlist

03 / Use the same selection API

curl 'https://www.codesota.com/api/pareto-router?task=document-ocr&objective=balanced&limit=3'

The product we are building

Choose the task.
Keep your integration.

Our direction is a common API and account across AI tasks, with versioned recipes and a stable output contract for each task. Switch the model behind document parsing, transcription, voice generation, or code without rewriting your application.

Explore the router →
  1. A contract for each task.Documents return text and layout; transcription returns segments; voice returns audio. Each task keeps its own output format across providers.
  2. Evidence behind each route.Compare models on the same task and protocol, with measured quality, latency, and cost as coverage grows.
  3. One place to run and compare.The roadmap brings provider adapters, versioned recipes, task validation, fallback, and usage accounting into the same routing layer.

First expansion / Documents

Documents are the
next execution family.

Turn scans and PDFs into text, tables, or structured fields. Choose for your documents, language, and deployment.

Explore OCR & document AI

Already have a shortlist?

Compare the tools you are considering.

Working with speech or code?

Explore the task guides and evidence behind future speech routes and the existing coding API.

Evidence behind the choice

A benchmark score needs context.

A strong result on one test does not guarantee a fit for your documents, recordings, or repository. Check the model version, evaluation protocol, source, and deployment requirements. Then test representative examples.

How to read our evidence →
  1. Match the task.Use a benchmark that measures the work you need done.
  2. Check the source.Distinguish reported results from independently reproduced runs.
  3. Try your workload.Measure output quality, speed, and cost on your own examples.

Explore the wider library

More tasks, model results, and research when you need to go deeper.

All tasks →

Explore the wider catalogue, from embeddings to computer vision.

Benchmarks →

Inspect results, metrics, sources, and evaluation protocols.

Models →

Look up a model and the evidence recorded for it.

Research & papers →

Follow original experiments and the claims they support.