GPT-3.5 Turbo Instruct API: toman pricing and code
openai/gpt-3.5-turbo-instruct
jsonYou are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups
GPT-3.5 Turbo Instruct 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="openai/gpt-3.5-turbo-instruct",
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: "openai/gpt-3.5-turbo-instruct",
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": "openai/gpt-3.5-turbo-instruct",
"messages": [{"role": "user", "content": "Classify the ticket and give it a priority from 1 to 5."}],
"response_format": {"type": "json_object"}
}'What is GPT-3.5 Turbo Instruct good for?
The id for GPT-3.5 Turbo Instruct in our API is "openai/gpt-3.5-turbo-instruct", served from OpenAI. Context is 4,095 tokens; past that you have to summarise the history yourself. A single response can run to 3,685 tokens. On price it sits in the "cheap" band — cheaper than 200 and dearer than 206 of the other paid models in the catalogue.
What it can do beyond plain text: 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.
Pricing is 435,188 toman per 1M input tokens and 580,250 per 1M output tokens. A 1,000-word round trip on GPT-3.5 Turbo Instruct lands near 1,320 toman. 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 Morph V3 Large: GPT-3.5 Turbo Instruct works out roughly 1.3× more expensive, and its context window is smaller. 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 76 thousand-word requests on GPT-3.5 Turbo Instruct, and every 1,000 toman is about 758 words of round trip. A job with one million input tokens and one million output tokens comes to 1,015,438 toman in total. Filling this model's 4,095-token window costs 1,782 toman on the input side alone, which is the real reason to keep conversation history short.
OpenAI has 95 models in our catalogue; the cheapest is gpt-oss-20b at 60 toman per thousand words and the dearest o1-pro at 282,872. Among the less common parameters it accepts logit_bias, logprobs, top_logprobs — 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 ReMM SLERP 13B at 6,144 tokens.
Good fits: high-volume work such as classification, tagging and bulk summarising, extracting data against a fixed schema, short single-turn requests (the context window is small).
This model is a variant of GPT-3.5 Turbo tuned for instructional prompts and omitting chat-related optimizations. Training data: up to Sep 2021.
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 | 885 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 2,089 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 63,828 toman |
Frequently asked
- How do I call GPT-3.5 Turbo Instruct 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 "openai/gpt-3.5-turbo-instruct". Nothing else in your code changes.
- What does GPT-3.5 Turbo Instruct cost in toman?
- 435,188 toman per 1M input tokens and 580,250 toman per 1M output tokens; a 1,000-word request is around 1,320 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does GPT-3.5 Turbo Instruct take?
- Up to 4,095 tokens per request, roughly 3k words. A single answer can reach 3,685 tokens.
- Can I stream GPT-3.5 Turbo Instruct'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.