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

openai/gpt-3.5-turbo:batch

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Input · per 1M tokens
72,531 toman
Output · per 1M tokens
217,594 toman
One 1,000-word request ≈
377 toman

You are billed for the usage the request actually reported. Prices follow the market. Web search: 2,901.25 toman / request.Pricing and top-ups

GPT-3.5 Turbo (batch) 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:batch",
    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 (batch) good for?

OpenAI publishes this model; we expose it under the id "openai/gpt-3.5-turbo:batch". Its context window is 16,385 tokens, roughly 12k English words in one request. A single response can run to 4,096 tokens. On price it sits in the "cheap" band — cheaper than 113 and dearer than 293 of the other paid models in the catalogue.

What you get on top of text in, text out: 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.

Input runs at 72,531 toman per 1M tokens and output at 217,594 — output costs 3× input, so trimming the answer saves more than trimming the prompt. 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 Mercury 2: GPT-3.5 Turbo (batch) works out roughly 1× 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 265 thousand-word requests on GPT-3.5 Turbo (batch), and every 1,000 toman is about 2,653 words of round trip. A job with one million input tokens and one million output tokens comes to 290,125 toman in total. Filling this model's 16,385-token window costs 1,188 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", because the underlying model is the same. The difference is the suffix: "batch" against the standard variant. On a thousand-word request this variant works out 2× cheaper (377 against 754 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.

Where it makes sense: 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 out196 toman
Summarising a ten-page document4,000 in + 600 out421 toman
Classifying a thousand short rows120,000 in + 20,000 out13,056 toman

Other variants of this model

Same core model, different execution terms and different price. This page is the batch (cheaper, slower) variant.

VariantModel idOutput / 1MContext
standardopenai/gpt-3.5-turbo435,18816,385

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 (batch) 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:batch". Nothing else in your code changes.
What does GPT-3.5 Turbo (batch) cost in toman?
72,531 toman per 1M input tokens and 217,594 toman per 1M output tokens; a 1,000-word request is around 377 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does GPT-3.5 Turbo (batch) take?
Up to 16,385 tokens per request, roughly 12k words. A single answer can reach 4,096 tokens.
Does GPT-3.5 Turbo (batch) 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.