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Swytcho gives you access to a curated set of AI models through a single, OpenAI-compatible API. Each model family is optimized for a different balance of capability, speed, and cost — so you can pick the right tool for every task without changing your integration code.

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Model Families

swytcho-1

General purpose — Swytcho’s flagship model. Best for complex reasoning, long-context tasks, nuanced instruction following, and production workloads where quality matters most.

swytcho-1-mini

Fast & cost-efficient — A smaller, distilled model optimized for low latency and high throughput. Ideal for classification, summarization, and high-volume pipelines.

swytcho-embed

Embeddings — Converts text into high-dimensional vector representations. Purpose-built for semantic search, retrieval-augmented generation (RAG), and similarity scoring.

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At a Glance

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List Available Models

To fetch the full list of models currently available on your account, send a GET request to the /v1/models endpoint. The response follows the standard OpenAI models list format.
A successful response returns an array of model objects:
Always query the /v1/models endpoint to discover the latest available models. New versions and experimental previews are added here first, before they are reflected in this documentation.

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Choosing the Right Model

Use the decision guide below to match your task to the best model.
1

Identify your task type

  • Open-ended generation, reasoning, or coding → start with swytcho-1
  • High-volume classification, extraction, or chat → start with swytcho-1-mini
  • Semantic search, RAG retrieval, or similarity → use swytcho-embed
2

Estimate your context length

If your combined prompt and expected output exceed 32 000 tokens, you need swytcho-1. For shorter exchanges, swytcho-1-mini is faster and cheaper.
3

Prototype with swytcho-1, optimize with swytcho-1-mini

Build your feature with swytcho-1 first to establish a quality baseline. Once you are satisfied with the results, test swytcho-1-mini against the same inputs — many tasks perform equally well at a fraction of the cost.
4

Benchmark before committing

Run both models against a representative sample of your real data. Measure accuracy, latency, and cost per request, then choose the model that meets your thresholds.

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Model Versioning and Stability

Swytcho uses a date-pinned alias system to give you control over model updates:
  • Floating aliases (e.g., swytcho-1) always resolve to the latest stable release. Use these during development for access to the newest improvements.
  • Date-pinned versions (e.g., swytcho-1-2025-04-01) are frozen snapshots. Use these in production when you need deterministic behavior across deployments.
When Swytcho retires a model version, it sends deprecation notices at least 90 days in advance via email and dashboard banner. The floating alias is automatically migrated to the next stable release on the deprecation date.
Pin to a date-stamped model version in your production configuration and update it deliberately during planned maintenance windows. This prevents unexpected behavior changes from silent alias rotations.