Schematron V2 Turbo overview
Schematron V2 Turbo by Inference.net
Send a page and a JSON Schema, and get back schema-conforming JSON for HTML-to-JSON extraction.
Schematron V2 Turbo specs
- Model ID
inference-net/schematron-v2-turbo- Developer
- Inference.net
- Context window
- 128,000 tokens
- Max output
- 8,192 tokens
- Input
- Text
- Output
- Text
- API
- OpenAI-compatible, base URL
https://api.aimlapi.com/v1
What Is Schematron V2 Turbo API?
Schematron V2 Turbo is Inference.net’s high-throughput HTML-to-JSON extraction model, designed to turn messy HTML into schema-conforming JSON using schema-constrained decoding. It is part of the Schematron family and is described as optimized for speed and cost at scale.
The model is presented as a 3B-parameter extraction model for web scraping and data ingestion workflows, with structured output support and strict JSON generation by construction.
Technical Reference
| Developer | Inference.net |
|---|---|
| Model ID | inference-net/schematron-v2-turbo |
| Release Date | September 12, 2026 |
| Context Window | 128,000 tokens (~128K) |
| Max Output Tokens | 8,192 tokens |
| Modalities | Text → Text |
| Capabilities | streaming, structured (JSON schema) output |
What Schematron V2 Turbo Brings to the Table
Schema-first extraction
You supply a JSON Schema or typed schema such as Pydantic or Zod, and the model returns JSON that conforms to that schema.
Strict JSON output
Inference.net describes the model as using strict JSON mode and schema-constrained decoding so output is valid JSON by construction.
Optimized for throughput
The Turbo variant is positioned as the throughput- and cost-optimized sibling in the Schematron family.
Long-context HTML handling
The model is documented with a 128K-token context window for processing long or noisy HTML inputs.
Where Schematron V2 Turbo Makes the Most Sense
Web scraping
Suitable for extracting structured records from messy web pages into schema-conforming JSON.
Data ingestion
Designed for pipelines that convert arbitrary HTML documents into typed data for downstream systems.
High-volume extraction
Recommended where speed, latency, and cost matter more than maximum extraction quality.
Schematron V2 Turbo vs the Alternatives
Schematron V2 Small
Inference.net describes Schematron V2 Small as the better choice for complex schemas or very long pages, while Turbo is tuned for maximum throughput and lowest cost at scale.
Schematron 3B
Inference.net states that requests sent to the older Schematron 3B route are now automatically routed to Schematron V2 Turbo.
Sources: https://inference.net/blog/schematron-v2/ · https://inference.net/models/schematron-v2-turbo/ · https://developer.puter.com/ai/inference-net/schematron-v2-turbo/
Use cases
High-throughput HTML-to-JSON extraction is a documented use case for Schematron V2 Turbo, especially when cost and latency matter.
It is suited to web scraping pipelines that need clean, typed JSON from messy HTML with schema adherence.
It is also documented for product data ingestion and converting arbitrary web pages into structured data.
For latency-sensitive crawling and scraping workflows, the model is described as a fit for financial document parsing for live trading and real-time price monitoring.
Capabilities
- Streaming
- Structured outputs
Schematron V2 Turbo API pricing
| Usage | Price | Unit |
|---|---|---|
| Input | $0.0317 | 1M tokens |
| Output | $0.1587 | 1M tokens |
Prices from the AI/ML API price list, checked on Nov 4, 2026.
Schematron V2 Turbo vs other models: token prices
AI/ML API input and output rates for 5 models, per million tokens.
| Model | Input | Output |
|---|---|---|
 Schematron V2 Turbo | $0.0317 | $0.1587 |
 Command R7B (12-2024) | $0.0397 | $0.1587 |
 Gemma 3 (12B) | $0.0529 | $0.1587 |
 Ministral 3 3B 2512 | $0.1587 | $0.1587 |
| Schematron V2 Small | $0.0529 | $0.2433 |
How to use the Schematron V2 Turbo API
View docs
Build with your agent
Give your agent the model details, code and documentation.
1Get your API key
Create API keyexport AIMLAPI_KEY="8f2b4c7d9a1e6b3f…"2Make your first request
curl '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": "Write a haiku about the ocean."
}
]
}'import os
import requests
headers = {
"Authorization": f"Bearer {os.environ['AIMLAPI_KEY']}",
"Content-Type": "application/json",
}
payload = {
"model": "inference-net/schematron-v2-turbo",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}
response = requests.post("https://api.aimlapi.com/v1/chat/completions", headers=headers, json=payload, timeout=120)
response.raise_for_status()
print(response.json())const response = 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": "Write a haiku about the ocean."
}
]
}),
});
if (!response.ok) throw new Error(`HTTP ${response.status}`);
console.log(await response.json());Models similar to Schematron V2 Turbo
Schematron V2 Turbo FAQ
How much does Schematron V2 Turbo cost?
Schematron V2 Turbo costs $0.0317 input and $0.1587 output per 1M tokens on AI/ML API, billed as you go with no subscription.
What is Schematron V2 Turbo best for on AI/ML API?
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.
Does Schematron V2 Turbo support structured output on AI/ML API?
Yes. The model supports structured outputs and schema-constrained decoding, so output is valid JSON by construction.
Can Schematron V2 Turbo stream responses on AI/ML API?
Yes. The model supports streaming.
What kind of input should I send to Schematron V2 Turbo?
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.
How strict is the JSON output from Schematron V2 Turbo?
The model uses strict JSON mode and schema-constrained decoding. The vendor states that it produces 100% schema-compliant output.
Is Schematron V2 Turbo suitable for noisy web pages?
Yes. It is specifically described as being purpose-built for messy HTML and robust to noisy markup.
What output style does Schematron V2 Turbo produce?
It produces clean, typed JSON rather than free-form narration. The model is optimized for schema-first extraction workflows.
What workloads fit Schematron V2 Turbo on AI/ML API?
It fits web scraping, product data ingestion, and document-to-JSON extraction workflows. The emphasis is on speed and cost at scale.
How does Schematron V2 Turbo differ from Schematron V2 Small?
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.