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

openai/gpt-3.5-turbo-0613

Tool callingJSON output
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Input · per 1M tokens
346,219 toman
Output · per 1M tokens
692,439 toman
One 1,000-word request ≈
1,350 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)
import OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });

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

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

const args = JSON.parse(call.function.arguments);
const result = { sku: args.sku, qty: 7 };

messages.push(first.choices[0].message, { role: "tool", tool_call_id: call.id, content: JSON.stringify(result) });
const final = await client.chat.completions.create({ model: "openai/gpt-3.5-turbo-0613", messages, tools });
console.log(final.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-0613",
    "messages": [{"role": "user", "content": "How many of the Nike NK-42 shoe are in stock?"}],
    "tools": [{
      "type": "function",
      "function": {
        "name": "check_stock",
        "parameters": {"type": "object", "properties": {"sku": {"type": "string"}}, "required": ["sku"]}
      }
    }]
  }'

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 "mid-range" band — cheaper than 185 and dearer than 199 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 346,219 toman per 1M input tokens and 692,439 per 1M output tokens. A 1,000-word round trip on GPT-3.5 Turbo (older v0613) lands near 1,350 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 about 30% 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 74 thousand-word requests on GPT-3.5 Turbo (older v0613), and every 1,000 toman is about 741 words of round trip. A job with one million input tokens and one million output tokens comes to 1,038,658 toman in total. Filling this model's 4,095-token window costs 1,418 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-16k", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 2.3× cheaper (1,350 against 3,151 toman). The context windows differ too: 4,095 against 16,385 tokens. Maximum answer length differs as well: 3,685 against 4,096 tokens.

OpenAI has 81 models in our catalogue; the cheapest is gpt-oss-20b at 49 toman per thousand words and the dearest GPT-5.5 Pro at 94,518. Among the less common parameters it accepts logit_bias, logprobs, max_completion_tokens, presence_penalty, 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: product chatbots and internal assistants, where cost and quality have to balance, 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 out796 toman
Summarising a ten-page document4,000 in + 600 out1,800 toman
Classifying a thousand short rows120,000 in + 20,000 out55,395 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?
346,219 toman per 1M input tokens and 692,439 toman per 1M output tokens; a 1,000-word request is around 1,350 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.