Schematron V2 Small overview
Schematron V2 Small by Inference.net
Send raw HTML and a JSON schema, and it returns schema-conformant structured JSON through Inference.net’s serverless API.
Schematron V2 Small specs
- Model ID
inference-net/schematron-v2-small- Developer
- Inference.net
- Context window
- 128,000 tokens
- Max output
- 4,096 tokens
- Input
- Text
- Output
- Text
- API
- OpenAI-compatible, base URL
https://api.aimlapi.com/v1
What Is Schematron V2 Small API?
Schematron V2 Small is a specialized HTML-to-JSON extraction model from Inference.net that turns messy HTML into clean, schema-conforming JSON. It is designed for structured extraction workflows where the schema, not a prompt, defines the output shape.
The model is described as a 3B-parameter model and is positioned as the small member of the Schematron V2 family, with extraction quality aimed at complex schemas and long pages.
Technical Reference
| Developer | Inference.net |
|---|---|
| Model ID | inference-net/schematron-v2-small |
| Release Date | September 12, 2026 |
| Context Window | 128,000 tokens (~128K) |
| Max Output Tokens | 4,096 tokens |
| Modalities | Text → Text |
| Capabilities | streaming, structured (JSON schema) output |
What Schematron V2 Small Brings to the Table
Specialized extraction model
Schematron V2 Small is built specifically for HTML-to-JSON extraction rather than general chat or reasoning tasks.
Schema-driven output
Extraction instructions are supplied through JSON Schema, Pydantic, or Zod, and the model returns schema-conforming structured output.
Strict JSON behavior
Inference.net says the model uses schema-constrained decoding and strict JSON mode so the output is valid JSON by construction.
Long-context support
The model supports a 128K token context window.
Where Schematron V2 Small Makes the Most Sense
Web scraping
The model is targeted at web scraping workflows that need reliable extraction from raw HTML into typed JSON.
Product data ingestion
It is presented as useful for turning e-commerce or catalog pages into structured feeds.
Financial document parsing
Provider and Inference.net materials describe it as suitable for extracting structured data from financial documents and filings.
Search-augmented pipelines
It is described as fitting pipelines that need dependable structured output over long pages.
Schematron V2 Small vs the Alternatives
Schematron 8B
Inference.net states that requests sent to inference-net/schematron-8b are now automatically routed to inference-net/schematron-v2-small.
Sources: https://inference.net/blog/schematron-v2/ · https://developer.puter.com/ai/models/ · https://inference.net/models/schematron-v2-small/
Use cases
Schematron V2 Small is suited to web scraping and HTML-to-JSON extraction, where it turns messy HTML into clean, schema-conforming JSON. It is also documented for product data ingestion, using a user-defined schema to return structured output from raw HTML. Another stated use case is financial document parsing, alongside other workflows that need reliable structured output over long pages. Inference.net also describes it as a fit for converting arbitrary pages into structured data.
Capabilities
- Streaming
- Structured outputs
Schematron V2 Small API pricing
| Usage | Price | Unit |
|---|---|---|
| Input | $0.0529 | 1M tokens |
| Output | $0.2433 | 1M tokens |
Prices from the AI/ML API price list, checked on Nov 4, 2026.
Schematron V2 Small vs other models: token prices
AI/ML API input and output rates for 5 models, per million tokens.
| Model | Input | Output |
|---|---|---|
 Schematron V2 Small | $0.0529 | $0.2433 |
| Schematron V2 Turbo | $0.0317 | $0.1587 |
 Ling-3.0-flash | $0.0794 | $0.2328 |
| Nova Lite 1.0 | $0.0635 | $0.2539 |
 Llama Guard 2 (8B) | $0.26 | $0.26 |
How to use the Schematron V2 Small 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-small",
"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-small",
"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-small",
"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 Small
Schematron V2 Small FAQ
How much does Schematron V2 Small cost?
Schematron V2 Small costs $0.0529 input and $0.2433 output per 1M tokens on AI/ML API, billed as you go with no subscription.
What does Schematron V2 Small do on AI/ML API?
Schematron V2 Small is an HTML-to-JSON extraction model from Inference.net that turns messy HTML into clean, schema-conforming JSON. It is built for structured extraction rather than general chat.
What input does this model expect?
It takes raw HTML plus a JSON schema, such as Pydantic, Zod, or JSON Schema definitions. Extraction instructions are carried through the schema rather than through a system or user prompt.
What output format does it produce?
It returns schema-conformant structured output in JSON. Inference.net describes the output as valid JSON by construction and strictly schema-compliant.
Does it support structured output?
Yes, it supports structured output. The model is designed to return validated JSON that conforms to the provided schema.
Does it support streaming?
Yes, the provider catalogue lists streaming as a capability for this model. The model is also described as having strict JSON mode for schema-compliant output.
What kinds of tasks is this model best for?
It is targeted at web scraping, product data ingestion, financial document parsing, and search-augmented pipelines that need reliable structured output. Inference.net also describes it as useful for complex schemas and long pages.
How should I prompt Schematron V2 Small?
Do not write a conversational prompt; send the schema and the HTML instead. The schema acts as the interface for extraction.
Is this a general-purpose LLM?
No. It is a specialized extraction model focused on HTML-to-JSON conversion and schema-constrained decoding.
What kind of pages is it intended to handle?
It is intended for messy HTML and long pages that need clean typed JSON extraction. Inference.net says it delivers frontier-level extraction quality on complex schemas and long pages.