Schematron V2 Small API: toman pricing and code
inference-net/schematron-v2-small
You are billed for the usage the request actually reported. Prices follow the market. Cached input: 17,311 toman per 1M tokens.Pricing and top-ups
Schematron V2 Small example: JSON output against a schema
The example is picked from this model's own capabilities. Drop in your key and it runs as is.
from openai import OpenAI
import json
client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")
schema = {
"name": "ticket",
"schema": {
"type": "object",
"properties": {
"category": {"type": "string", "enum": ["fani", "mali", "forush"]},
"priority": {"type": "integer", "minimum": 1, "maximum": 5},
"summary": {"type": "string"},
},
"required": ["category", "priority", "summary"],
"additionalProperties": False,
},
"strict": True,
}
resp = client.chat.completions.create(
model="inference-net/schematron-v2-small",
messages=[{"role": "user", "content": "Ticket: "For two days I cannot download my invoice and I was charged twice.""}],
response_format={"type": "json_schema", "json_schema": schema},
)
print(json.loads(resp.choices[0].message.content))import OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });
const resp = await client.chat.completions.create({
model: "inference-net/schematron-v2-small",
messages: [{ role: "user", content: "Ticket: "For two days I cannot download my invoice and I was charged twice."" }],
response_format: {
type: "json_schema",
json_schema: {
name: "ticket",
strict: true,
schema: {
type: "object",
properties: {
category: { type: "string", enum: ["fani", "mali", "forush"] },
priority: { type: "integer", minimum: 1, maximum: 5 },
summary: { type: "string" },
},
required: ["category", "priority", "summary"],
additionalProperties: false,
},
},
},
});
console.log(JSON.parse(resp.choices[0].message.content));curl https://api.uttapen.ir/v1/chat/completions \
-H "Authorization: Bearer sk-up-…" \
-H "Content-Type: application/json" \
-d '{
"model": "inference-net/schematron-v2-small",
"messages": [{"role": "user", "content": "Classify the ticket and give it a priority from 1 to 5."}],
"response_format": {"type": "json_object"}
}'What is Schematron V2 Small good for?
Schematron V2 Small comes from Inference Net; in uttapen you reach it with the model id "inference-net/
Capabilities available on this id: it returns schema-valid JSON through response_format, ready to hand to your code. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper.
For a back-of-envelope figure: about 126 toman per 1,000-word exchange (17,311 in, 79,630 out, per 1M tokens). It supports cached input: repeated context is billed at 17,311 toman per 1M, which matters a lot if your system prompt is long. You are always charged for the usage the request actually reported, never for the estimate, and a failed request costs nothing.
The closest alternative with the same capabilities from a different provider is Llama 3.2 1B Instruct: Schematron V2 Small works out about 20% more expensive, and its context window is larger. Both run on the same key and the same code, so trying the other one is a single string change.
To make the figure concrete: 100,000 toman of credit buys roughly 794 thousand-word requests on Schematron V2 Small, and every 1,000 toman is about 7,937 words of round trip. A job with one million input tokens and one million output tokens comes to 96,941 toman in total. Filling this model's 128,000-token window costs 2,216 toman on the input side alone, which is the real reason to keep conversation history short.
Its nearest relative in the catalogue is "inference-net/
Inference Net has 2 models in our catalogue; the cheapest is Schematron V2 Turbo at 81 toman per thousand words and the dearest Schematron V2 Small at 126. Among the less common parameters it accepts logit_bias, min_p, presence_penalty, repetition_penalty, top_k — all through the standard request body. It does not support include_reasoning, reasoning, tool_choice, tools, which most models here do, so test before switching if your code relies on them. By context size the nearest option from another provider is Nova Micro 1.0 at 128,000 tokens.
What to use it for: high-volume work such as classification, tagging and bulk summarising, extracting data against a fixed schema.
Schematron V2 Small is a 3B-parameter HTML-to-JSON extraction model from Inference.net. It prioritizes extraction quality for complex schemas and long pages. Extraction instructions must be supplied through a JSON schema...
Three real jobs, priced on this model
Each figure is derived from the prices above and moves when they do.
| Job | Tokens | Cost |
|---|---|---|
| One chat turn with a medium history | 1,500 in + 400 out | 58 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 117 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 3,670 toman |
Price history
| Date | Input (toman/1M) | Output (toman/1M) |
|---|---|---|
| 13 Sep 2026 | 17,311 | 79,630 |
| 12 Sep 2026 | 17,311 | 79,630 |
Every price change for this model is recorded. Toman figures use today's rate.
Frequently asked
- How do I call Schematron V2 Small from Iran?
- Sign up with your mobile number, top the wallet up in toman, create an API key, then in the official OpenAI SDK point base_url at https:/
/ api.uttapen.ir/ v1 and set model to "inference-net/ schematron-v2-small". Nothing else in your code changes. - What does Schematron V2 Small cost in toman?
- 17,311 toman per 1M input tokens and 79,630 toman per 1M output tokens; a 1,000-word request is around 126 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Schematron V2 Small take?
- Up to 128,000 tokens per request, roughly 96k words. A single answer can reach 4,096 tokens.
- Can I stream Schematron V2 Small's output?
- Yes — with stream=true you get SSE events as the tokens are produced. This model has no tool calling and no image input, so pick a different one if you need either.