Schematron V2 Turbo API: toman pricing and code
inference-net/schematron-v2-turbo
You are billed for the usage the request actually reported. Prices follow the market. Cached input: 10,387 toman per 1M tokens.Pricing and top-ups
Schematron V2 Turbo 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-turbo",
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-turbo",
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-turbo",
"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 Turbo good for?
Inference Net publishes this model; we expose it under the 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 81 toman per 1,000-word exchange (10,387 in, 51,933 out, per 1M tokens). It supports cached input: repeated context is billed at 10,387 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 Command R7B (12-2024), and Schematron V2 Turbo costs about the same, and its context window is the same size. 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 1,235 thousand-word requests on Schematron V2 Turbo, and every 1,000 toman is about 12,346 words of round trip. A job with one million input tokens and one million output tokens comes to 62,319 toman in total. Filling this model's 128,000-token window costs 1,329 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 Turbo is a 3B-parameter HTML-to-JSON extraction model from Inference.net. It prioritizes throughput for high-volume extraction workloads. Extraction instructions must be supplied through a JSON schema in response_format rather...
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 | 36 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 73 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 2,285 toman |
Price history
| Date | Input (toman/1M) | Output (toman/1M) |
|---|---|---|
| 13 Sep 2026 | 10,387 | 51,933 |
| 12 Sep 2026 | 10,387 | 51,933 |
Every price change for this model is recorded. Toman figures use today's rate.
Frequently asked
- How do I call Schematron V2 Turbo 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-turbo". Nothing else in your code changes. - What does Schematron V2 Turbo cost in toman?
- 10,387 toman per 1M input tokens and 51,933 toman per 1M output tokens; a 1,000-word request is around 81 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Schematron V2 Turbo take?
- Up to 128,000 tokens per request, roughly 96k words. A single answer can reach 8,192 tokens.
- Can I stream Schematron V2 Turbo'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.