R1 0528 API: toman pricing and code
deepseek/deepseek-r1-0528
You are billed for the usage the request actually reported. Prices follow the market. Cached input: 121,177 toman per 1M tokens.Pricing and top-ups
R1 0528 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="deepseek/deepseek-r1-0528",
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": "deepseek/deepseek-r1-0528",
"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: "deepseek/deepseek-r1-0528",
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 R1 0528 good for?
The id for R1 0528 in our API is "deepseek/
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 173,110 toman per 1M tokens and output at 744,372 — output costs 4.3× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 121,177 toman per 1M, which matters a lot if your system prompt is long. 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 MiniMax M1, and R1 0528 costs about the same, 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 84 thousand-word requests on R1 0528, and every 1,000 toman is about 838 words of round trip. A job with one million input tokens and one million output tokens comes to 917,481 toman in total. Filling this model's 163,840-token window costs 28,362 toman on the input side alone, which is the real reason to keep conversation history short.
Its nearest relative in the catalogue is "deepseek/
DeepSeek has 18 models in our catalogue; the cheapest is DeepSeek V4 Flash 0423 at 123 toman per thousand words and the dearest DeepSeek Pro Latest at 3,642. Among the less common parameters it accepts logit_bias, logprobs, min_p, presence_penalty, repetition_penalty, top_k — all through the standard request body. By context size the nearest option from another provider is Llama Guard 4 12B at 163,840 tokens.
Where it makes sense: product chatbots and internal assistants, where cost and quality have to balance, logic puzzles and code review, agents that reach out to APIs and databases, extracting data against a fixed schema.
May 28th update to the [original DeepSeek R1](/deepseek/deepseek-r1) Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active...
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 | 557 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 1,139 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 35,661 toman |
Frequently asked
- How do I call R1 0528 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 "deepseek/ deepseek-r1-0528". Nothing else in your code changes. - What does R1 0528 cost in toman?
- 173,110 toman per 1M input tokens and 744,372 toman per 1M output tokens; a 1,000-word request is around 1,193 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does R1 0528 take?
- Up to 163,840 tokens per request, roughly 123k words. A single answer can reach 32,768 tokens.
- Does R1 0528 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.