schematron-v2-turbo
Schematron V2 Turbo is a 3B HTML-to-JSON extraction model from Inference.net that prioritizes throughput for high-volume extraction. Extraction instructions are supplied as a JSON schema in response_format.
Precio: Entrada: 7.2 créditos / 1 M de tokens (≈ $0.04); salida: 36 créditos / 1 M de tokens (≈ $0.18).
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Schematron V2 Turbo API on ModelFold
Schematron V2 Turbo is a 3B HTML-to-JSON extraction model from Inference.net that prioritizes throughput for high-volume extraction. Extraction instructions are supplied as a JSON schema in response_format. Try Schematron V2 Turbo in the ModelFold Playground, then call it from your own code through an OpenAI-compatible chat completions API.
What Is the Schematron V2 Turbo API
Schematron V2 Turbo at a glance
Schematron V2 Turbo is a chat model from Inference.net. It accepts text as input and returns text, with a context window of up to 128K tokens.
Use it without hosting anything
On ModelFold you call Schematron V2 Turbo with a single API key — no model weights to download and no inference servers to run. The same key also works for the other chat, image, video and audio models on ModelFold.
Core Capabilities of Schematron V2 Turbo
Up to 128K tokens of context
Keep long conversations, large documents and sizeable code in a single request, so the model can reason over the full material instead of fragments.
Structured outputs
Ask for JSON that follows a schema (response_format), which makes extraction, classification and system integrations more reliable.
How to Use Schematron V2 Turbo on ModelFold
- 1
Step 1: Try it in the Playground
Open the Playground at the top of this page, type a prompt and see how Schematron V2 Turbo responds before writing any code.
- 2
Step 2: Create an API key
Sign in to ModelFold and create an API key in the console. Keep it secret and send it as a Bearer token.
- 3
Step 3: Send a request
Call POST https://modelfold.ai/v1/chat/completions with "model": "schematron-v2-turbo" and your messages. Existing OpenAI-compatible SDKs work by changing the base URL and key.
- ✓
Step 4: Ship and monitor
Integrate the response into your product and check token usage and spend for every call in the console logs.
What You Can Build with Schematron V2 Turbo
High-volume pipelines
Run classification, extraction, rewriting and batch jobs at scale where cost per request matters.
Low-latency experiences
Power chat interfaces, autocomplete and interactive tools where users expect fast responses.
Long-document workflows
Feed in long reports, contracts, manuals or codebases and ask for summaries, comparisons and answers grounded in the material.
Frequently Asked Questions
What is Schematron V2 Turbo?⌄
Schematron V2 Turbo is a 3B HTML-to-JSON extraction model from Inference.net that prioritizes throughput for high-volume extraction. Extraction instructions are supplied as a JSON schema in response_format.
Which inputs does Schematron V2 Turbo support?⌄
Schematron V2 Turbo accepts text and returns text. Through ModelFold you send text messages; documents attached in the Playground are converted to text.
How long is the context window of Schematron V2 Turbo?⌄
Up to 128K tokens, shared between your input and the model’s output.
Does Schematron V2 Turbo support function calling?⌄
Function calling is not listed for Schematron V2 Turbo; requests that include tools will be rejected. Use plain prompts or JSON output instead.
How is Schematron V2 Turbo billed on ModelFold?⌄
Pay as you go by tokens: input and output tokens are charged at the per-million-token prices shown on this page. There is no monthly fee, and failed requests are not charged.
How do I call Schematron V2 Turbo through the API?⌄
Send a POST request to https://modelfold.ai/v1/chat/completions with "model": "schematron-v2-turbo", using your ModelFold API key as the Bearer token.
| Entrada | 7.2 créditos 1M tokens≈ $0.04 |
| Salida | 36 créditos 1M tokens≈ $0.18 |
Se cobra por token, con tarifas separadas para entrada y salida. Los tokens de razonamiento cuentan como salida. El entorno de pruebas aplica exactamente el mismo cálculo.
Leer la documentación de la API →AI Agents · Claude Code / Cursor / Trae →curl https://modelfold.ai/v1/chat/completions \
-H "Authorization: Bearer $MODELFOLD_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"schematron-v2-turbo","messages":[{"role":"user","content":"Hello"}]}'