gpt-oss-20b (batch) API: toman pricing and code
openai/gpt-oss-20b:batch
reasoningjsonYou are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups
gpt-oss-20b (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-20b: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-20b: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-20b: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-20b (batch) good for?
gpt-oss-20b (batch) comes from OpenAI; in uttapen you reach it with the model id "openai/gpt-oss-20b:batch". It accepts up to 131,072 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 117,964 tokens. On price it sits in the "very cheap" band — cheaper than 33 and dearer than 373 of the other paid models in the catalogue.
What it can do beyond plain text: 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.
Pricing is 14,506 toman per 1M input tokens and 58,025 per 1M output tokens. A 1,000-word round trip on gpt-oss-20b (batch) lands near 94 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 Reka Flash 3: gpt-oss-20b (batch) works out roughly 1.2× 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 1,064 thousand-word requests on gpt-oss-20b (batch), and every 1,000 toman is about 10,638 words of round trip. A job with one million input tokens and one million output tokens comes to 72,531 toman in total. Filling this model's 131,072-token window costs 1,901 toman on the input side alone, which is the real reason to keep conversation history short.
This id gets confused with "openai/gpt-oss-20b", 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 1.6× dearer (94 against 60 toman). On parameters the other takes logprobs, seed, tool_choice, tools.
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, min_p, reasoning_effort, repetition_penalty, top_k — all through the standard request body. It does not support tool_choice, tools, seed, 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 Aion-2.0 at 131,072 tokens.
Good fits: high-volume work such as classification, tagging and bulk summarising, logic puzzles and code review, extracting data against a fixed schema.
gpt-oss-20b is an open-weight 21B parameter model released by OpenAI under the Apache 2.0 license. It uses a Mixture-of-Experts (MoE) architecture with 3.6B active parameters per forward pass, optimized for...
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 | 45 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 93 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 2,901 toman |
Other variants of this model
Same core model, different execution terms and different price. This page is the batch (cheaper, slower) variant.
| Variant | Model id | Output / 1M | Context |
|---|---|---|---|
| standard | openai/gpt-oss-20b | 37,716 | 131,072 |
Put the variant's id verbatim in the model field; nothing else in your code changes.
Frequently asked
- How do I call gpt-oss-20b (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-20b:batch". Nothing else in your code changes.
- What does gpt-oss-20b (batch) cost in toman?
- 14,506 toman per 1M input tokens and 58,025 toman per 1M output tokens; a 1,000-word request is around 94 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does gpt-oss-20b (batch) take?
- Up to 131,072 tokens per request, roughly 98k words. A single answer can reach 117,964 tokens.
- Can I stream gpt-oss-20b (batch)'s output?
- Yes — with stream=true you get SSE events as the tokens are produced. This model has no tool calling and no image input, so pick a different one if you need either.