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GPT-3.5 Turbo API: toman pricing and code

openai/gpt-3.5-turbo

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Input · per 1M tokens
145,063 toman
Output · per 1M tokens
435,188 toman
One 1,000-word request ≈
754 toman

You are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups

GPT-3.5 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="openai/gpt-3.5-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))

What is GPT-3.5 Turbo good for?

GPT-3.5 Turbo is one of OpenAI's models. Put "openai/gpt-3.5-turbo" in the model field and the rest of your code stays as it is. GPT-3.5 Turbo keeps 16,385 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 4,096 tokens. On price it sits in the "cheap" band — cheaper than 173 and dearer than 233 of the other paid models in the catalogue.

Capabilities available on this id: it supports tool calling, so it can invoke your own functions with valid arguments; 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 754 toman per 1,000-word exchange (145,063 in, 435,188 out, per 1M tokens). 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 Aion-2.0: GPT-3.5 Turbo works out roughly 1.2× cheaper, 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 133 thousand-word requests on GPT-3.5 Turbo, and every 1,000 toman is about 1,326 words of round trip. A job with one million input tokens and one million output tokens comes to 580,250 toman in total. Filling this model's 16,385-token window costs 2,377 toman on the input side alone, which is the real reason to keep conversation history short.

This id gets confused with "openai/gpt-3.5-turbo:batch", because the underlying model is the same. The difference is the suffix: no suffix (the standard variant) against "batch". On a thousand-word request this variant works out 2× dearer (754 against 377 toman).

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, 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 Phi 4 at 16,384 tokens.

What to use it for: high-volume work such as classification, tagging and bulk summarising, agents that reach out to APIs and databases, extracting data against a fixed schema.

Provider's own description

GPT-3.5 Turbo is OpenAI's fastest model. It can understand and generate natural language or code, and is optimized for chat and traditional completion tasks. 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.

JobTokensCost
One chat turn with a medium history1,500 in + 400 out392 toman
Summarising a ten-page document4,000 in + 600 out841 toman
Classifying a thousand short rows120,000 in + 20,000 out26,111 toman

Other variants of this model

Same core model, different execution terms and different price. This page is the standard variant.

VariantModel idOutput / 1MContext
batch (cheaper, slower)openai/gpt-3.5-turbo:batch217,59416,385

Put the variant's id verbatim in the model field; nothing else in your code changes.

Frequently asked

How do I call GPT-3.5 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 "openai/gpt-3.5-turbo". Nothing else in your code changes.
What does GPT-3.5 Turbo cost in toman?
145,063 toman per 1M input tokens and 435,188 toman per 1M output tokens; a 1,000-word request is around 754 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does GPT-3.5 Turbo take?
Up to 16,385 tokens per request, roughly 12k words. A single answer can reach 4,096 tokens.
Does GPT-3.5 Turbo support streaming and tool calling?
Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape. Structured output through response_format works too.