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GPT-3.5 Turbo (older v0613) API: toman pricing and code

openai/gpt-3.5-turbo-0613

toolsjson
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Input · per 1M tokens
290,125 toman
Output · per 1M tokens
580,250 toman
One 1,000-word request ≈
1,131 toman

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

GPT-3.5 Turbo (older v0613) example: tool calling

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-…")

tools = [{
    "type": "function",
    "function": {
        "name": "check_stock",
        "description": "Returns the stock level of a product",
        "parameters": {
            "type": "object",
            "properties": {"sku": {"type": "string"}},
            "required": ["sku"],
        },
    },
}]

messages = [{"role": "user", "content": "How many of the Nike NK-42 shoe are in stock?"}]
first = client.chat.completions.create(model="openai/gpt-3.5-turbo-0613", messages=messages, tools=tools)
call = first.choices[0].message.tool_calls[0]

# you run the function yourself — the model never touches the database
args = json.loads(call.function.arguments)
result = {"sku": args["sku"], "qty": 7}

messages += [first.choices[0].message, {"role": "tool", "tool_call_id": call.id, "content": json.dumps(result)}]
final = client.chat.completions.create(model="openai/gpt-3.5-turbo-0613", messages=messages, tools=tools)
print(final.choices[0].message.content)

What is GPT-3.5 Turbo (older v0613) good for?

The id for GPT-3.5 Turbo (older v0613) in our API is "openai/gpt-3.5-turbo-0613", 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 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.

Pricing is 290,125 toman per 1M input tokens and 580,250 per 1M output tokens. A 1,000-word round trip on GPT-3.5 Turbo (older v0613) lands near 1,131 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 Devstral 2 2512: GPT-3.5 Turbo (older v0613) works out roughly 1.2× 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 88 thousand-word requests on GPT-3.5 Turbo (older v0613), and every 1,000 toman is about 884 words of round trip. A job with one million input tokens and one million output tokens comes to 870,375 toman in total. Filling this model's 4,095-token window costs 1,188 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "openai/gpt-3.5-turbo", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out 1.5× dearer (1,131 against 754 toman). The context windows differ too: 4,095 against 16,385 tokens. Maximum answer length differs as well: 3,685 against 4,096 tokens. On parameters, this one takes max_completion_tokens while the other takes max_tokens.

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, max_completion_tokens, top_logprobs — all through the standard request body. It does not support include_reasoning, max_tokens, 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 ReMM SLERP 13B at 6,144 tokens.

Good fits: high-volume work such as classification, tagging and bulk summarising, agents that reach out to APIs and databases, extracting data against a fixed schema, short single-turn requests (the context window is small).

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 out667 toman
Summarising a ten-page document4,000 in + 600 out1,509 toman
Classifying a thousand short rows120,000 in + 20,000 out46,420 toman

Frequently asked

How do I call GPT-3.5 Turbo (older v0613) 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-0613". Nothing else in your code changes.
What does GPT-3.5 Turbo (older v0613) cost in toman?
290,125 toman per 1M input tokens and 580,250 toman per 1M output tokens; a 1,000-word request is around 1,131 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does GPT-3.5 Turbo (older v0613) take?
Up to 4,095 tokens per request, roughly 3k words. A single answer can reach 3,685 tokens.
Does GPT-3.5 Turbo (older v0613) 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.