gpt-oss-120b (batch) API: toman pricing and code
openai/gpt-oss-120b:batch
You are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups
gpt-oss-120b (batch) example: a multi-step problem with reasoning
The example is picked from this model's own capabilities. Drop in your key and it runs as is.
from openai import OpenAI
client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")
resp = client.chat.completions.create(
model="openai/gpt-oss-120b:batch",
messages=[{"role": "user", "content": (
"A shop has 3 warehouses. A ships 120 orders a day, B ships 85 and C ships 40. "
"If C closes and its load is split between A and B in proportion to their capacity, how many does each ship a day? "
"Work through it step by step and give just the two numbers at the end."
)}],
reasoning_effort="medium", # low | medium | high
)
print(resp.choices[0].message.content)
# reasoning tokens count as output tokens too:
print(resp.usage.completion_tokens_details)curl https://api.uttapen.ir/v1/chat/completions \
-H "Authorization: Bearer sk-up-…" \
-H "Content-Type: application/json" \
-d '{
"model": "openai/gpt-oss-120b:batch",
"messages": [{"role": "user", "content": "120 and 85 daily orders split across two warehouses; work through it step by step."}],
"reasoning": {"effort": "medium"}
}'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-oss-120b:batch",
messages: [{ role: "user", content: "120 and 85 daily orders are split between two warehouses; work through it step by step and give just the two numbers at the end." }],
reasoning_effort: "medium",
});
console.log(resp.choices[0].message.content);
console.log(resp.usage.completion_tokens_details);What is gpt-oss-120b (batch) good for?
gpt-oss-120b (batch) is one of OpenAI's models. Put "openai/
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; it has a reasoning mode that pays off on multi-step problems, maths and debugging. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper. Keep in mind that reasoning tokens are output tokens and do appear on the bill.
Input runs at 10,248 toman per 1M tokens and output at 47,086 — output costs 4.6× 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 Nova Micro 1.0: gpt-oss-120b (batch) works out about 9% cheaper, and its context window is larger. 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 1,333 thousand-word requests on gpt-oss-120b (batch), and every 1,000 toman is about 13,333 words of round trip. A job with one million input tokens and one million output tokens comes to 57,334 toman in total. Filling this model's 131,072-token window costs 1,343 toman on the input side alone, which is the real reason to keep conversation history short.
This id gets confused with "openai/
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, min_p, presence_penalty, reasoning_effort, repetition_penalty, top_k — all through the standard request body. By context size the nearest option from another provider is Aion-2.0 at 131,072 tokens.
Where it makes sense: high-volume work such as classification, tagging and bulk summarising, logic puzzles and code review, agents that reach out to APIs and databases, extracting data against a fixed schema.
gpt-oss-120b is an open-weight, 117B-parameter Mixture-of-Experts (MoE) language model from OpenAI designed for high-reasoning, agentic, and general-purpose production use cases. It activates 5.1B parameters per forward pass and is optimized...
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 | 34 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 69 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 2,171 toman |
Other variants of this model
Same core model, different execution terms and different price. This page is the batch variant.
| Variant | Model id | Output / 1M | Context |
|---|---|---|---|
| standard | openai/gpt-oss-120b | 207,732 | 131,072 |
Put the variant's id verbatim in the model field; nothing else in your code changes.
Price history
| Date | Input (toman/1M) | Output (toman/1M) |
|---|---|---|
| 24 Sep 2026 | 10,248 | 47,086 |
| 7 Sep 2026 | 51,933 | 207,732 |
Every price change for this model is recorded. Toman figures use today's rate.
Frequently asked
- How do I call gpt-oss-120b (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-oss-120b:batch". Nothing else in your code changes. - What does gpt-oss-120b (batch) cost in toman?
- 10,248 toman per 1M input tokens and 47,086 toman per 1M output tokens; a 1,000-word request is around 75 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does gpt-oss-120b (batch) take?
- Up to 131,072 tokens per request, roughly 98k words. A single answer can reach 117,964 tokens.
- Does gpt-oss-120b (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.