inference-net · Efficient
Schematron V2 Small
Inference.net's 3B-parameter HTML-to-JSON extraction model, focused on accuracy for complex schemas and long web pages. It turns HTML into typed, structured data for web scraping and product catalog ingestion, with a 128K-token context window. Supply HTML in the user message and extraction instructions in a JSON schema via response_format; it does not follow ordinary chat or system prompts.
- Structured output
- Input
- Text
- Output
- Text
- Discovery tier
- Efficient
Pricing
USD per 1M tokens- Input
- $0.05
- Output
- $0.23
Reference prices as published by the model provider. API example
Specifications
- Context window
- 128K
- Max output
- 4.1K
Side by side
Schematron V2 Small compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| Schematron V2 Smallinference-netThis model | Schematron V2 Turboinference-netRemove | |
|---|---|---|
| Provider | inference-net | inference-net |
| Discovery tier | Efficient | Efficient |
| Added | Sep 12, 2026 | Sep 12, 2026 |
| Limits | ||
| Context window | 128K | 128K |
| Max output | 4.1K | 8.19KBest |
| Pricing, USD per 1M tokens | ||
| Input | $0.05 | $0.03Best |
| Output | $0.23 | $0.15Best |
| Capabilities | ||
| Structured output | Yes | Yes |
| Reasoning effort | — | — |
Public evaluations
Benchmarks
There are no public benchmark results for these models yet.
Developers
Use it through the API
Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.
curl https://www.noumen.xyz/api/v1/chat/completions \
-H "Authorization: Bearer $NOUMEN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "inference-net/schematron-v2-small",
"messages": [{ "role": "user", "content": "Explain this stack trace." }]
}'import os
from openai import OpenAI
client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
model="inference-net/schematron-v2-small",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)inference-net
Related models
Other inference-net models, starting with the same family.
- Efficient