R1 0528 API: toman pricing and code
deepseek/deepseek-r1-0528
toolsreasoningjsonYou are billed for the usage the request actually reported. Prices follow the market. Cached input: 101,544 toman / 1M.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/deepseek-r1-0528", served from DeepSeek. Context is 163,840 tokens; past that you have to summarise the history yourself. A single response can run to 32,768 tokens. On price it sits in the "mid-range" band — cheaper than 214 and dearer than 192 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; 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 145,063 toman per 1M tokens and output at 623,769 — output costs 4.3× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 101,544 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: R1 0528 works out roughly 1× cheaper, 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 100 thousand-word requests on R1 0528, and every 1,000 toman is about 1,001 words of round trip. A job with one million input tokens and one million output tokens comes to 768,831 toman in total. Filling this model's 163,840-token window costs 23,767 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-chat-v3.1", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out 1.2× dearer (999 against 830 toman). Maximum answer length differs as well: 32,768 against 144,900 tokens.
DeepSeek has 17 models in our catalogue; the cheapest is DeepSeek V4 Flash Latest at 79 toman per thousand words and the dearest DeepSeek V4 Pro 0813 (batch) at 1,991. Among the less common parameters it accepts logit_bias, logprobs, min_p, repetition_penalty, top_k, top_logprobs — 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 | 467 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 955 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 29,883 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?
- 145,063 toman per 1M input tokens and 623,769 toman per 1M output tokens; a 1,000-word request is around 999 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.