Choosing a model

Larger models give you more capability on complex tasks; smaller ones respond faster and cost less per token. Capability is never one-to-one across model families, so treat these recommendations as starting points and spot-check candidates on your own prompts, the playground makes side-by-side comparisons quick.

Your first request

Every model answers on the same OpenAI-compatible endpoint, so moving between them is a one-word change. This example uses deepseek-ai/DeepSeek-V4-Flash-0731 because it is the first entry in the live catalog below, not because it is the best fit for your workload — any other id from the table drops straight in.

curl https://inference.pearlresearch.ai/v1/chat/completions \
  -H "Authorization: Bearer $PEARL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-ai/DeepSeek-V4-Flash-0731",
    "messages": [{"role": "user", "content": "Hello"}]
  }'

Supported models

Pass the model ID as the model parameter in your requests. Context is shown as rounded thousands; the exact value, and each model's max output, are at GET /models.

List models via the API

The live catalog is also served by the standard OpenAI models endpoint. Each entry's id is the value you pass as model, alongside per-model context length, modalities, and supported parameters:

curl https://inference.pearlresearch.ai/v1/models \
  -H "Authorization: Bearer $PEARL_API_KEY"

Migrating from closed models

Moving from a closed-model provider? Because the endpoint is OpenAI-compatible, migrating is usually a base-URL and model-name change. As a starting point: