AI Model Comparison

Model comparison by workload

The Model Recommender ranks a curated catalog against your workload, team size, and priorities. Every result carries public benchmark evidence and compares against the model you run today.

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One model catalog feeds both the recommender and gateway routing
Two ways to pick a model

Ranked on evidence

Before

Picking by reputation

  • Defaults to whichever model is trending
  • No view of the cost or quality trade-off for your tasks
  • Switching later means re-testing everything informally
  • No structured comparison against what you run now
With Cortega

Model Recommender

  • Ranks a curated catalog against your workloads and priorities
  • Every result carries public benchmark evidence
  • Shows cost, quality, and performance versus your current model
  • Includes open-weight models if you want them considered
How it works

Four inputs

01

Workloads and scale

Pick the workloads you run and the number of people they serve.

02

Estimated usage

Requests per month and tokens per request, pre-filled from your workloads and editable.

03

Base model to beat

Name the model you run today. Recommendations must meet or beat it on capability.

04

Priorities

Weight what matters: quality, cost, coding, reasoning, chat, speed, or tool use.

Backed by the platform

One catalog

01

One catalog, two uses

Pick a model here, then configure it as a routing target in your gateway.

AI Border Gateway →
02

Public benchmark evidence

Rankings are backed by public benchmarks, so a result is something you can check.

03

Configure it as a routing target

Put the models that fit into weighted, failover, or conditional routing, enforced at the gateway.

AI Border Gateway →

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Explore how Cortega fits your AI environment.

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