import requests r = requests.post( "https://api.aimlapi.com/v1/chat/completions", headers={"Authorization": "Bearer " + AIMLAPI_KEY}, json={ "model": "inference-net/schematron-v2-turbo", "messages": [ { "role": "user", "content": "Hello!" } ] }, ) print(r.json())
const r = await fetch("https://api.aimlapi.com/v1/chat/completions", { method: "POST", headers: { Authorization: `Bearer ${process.env.AIMLAPI_KEY}`, "Content-Type": "application/json", }, body: JSON.stringify({ "model": "inference-net/schematron-v2-turbo", "messages": [ { "role": "user", "content": "Hello!" } ] }), }); console.log(await r.json());
curl -X POST https://api.aimlapi.com/v1/chat/completions \ -H "Authorization: Bearer $AIMLAPI_KEY" \ -H "Content-Type: application/json" \ -d '{"model":"inference-net/schematron-v2-turbo","messages":[{"role":"user","content":"Hello!"}]}'
OpenAI-compatible — swap the base URL and it works with your existing SDK.
| Type | Price |
|---|---|
| Input | |
| Output | |
It is a high-throughput HTML-to-JSON extraction model for turning messy HTML into schema-conforming JSON. It is designed for web scraping and data extraction pipelines that need strict structured output.
Yes. The model supports structured outputs and schema-constrained decoding, so output is valid JSON by construction.
Yes. The provider catalogue lists streaming as a capability for this model.
Send raw or cleaned HTML together with a JSON Schema, or a typed schema definition such as Pydantic or Zod. The model is built to extract data that conforms to the schema you provide.
The model uses strict JSON mode and schema-constrained decoding. The provider documentation states that it produces 100% schema-compliant output.
Yes. It is specifically described as being purpose-built for messy HTML and robust to noisy markup.
It produces clean, typed JSON rather than free-form narration. The model is optimized for schema-first extraction workflows.
It fits web scraping, product data ingestion, and document-to-JSON extraction workflows. The emphasis is on speed and cost at scale.
Schematron V2 Turbo is the throughput-optimized sibling, intended for faster processing and lower cost at scale. The documentation also says it exceeds the quality of the previous-generation Schematron 3B while staying focused on high-throughput extraction.
No. The provider documentation says the endpoint does not support tool calling.
