R1 API: toman pricing and code
deepseek/deepseek-r1
toolsreasoningjsonYou are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups
R1 example: tool calling
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-…")
tools = [{
"type": "function",
"function": {
"name": "check_stock",
"description": "Returns the stock level of a product",
"parameters": {
"type": "object",
"properties": {"sku": {"type": "string"}},
"required": ["sku"],
},
},
}]
messages = [{"role": "user", "content": "How many of the Nike NK-42 shoe are in stock?"}]
first = client.chat.completions.create(model="deepseek/deepseek-r1", messages=messages, tools=tools)
call = first.choices[0].message.tool_calls[0]
# you run the function yourself — the model never touches the database
args = json.loads(call.function.arguments)
result = {"sku": args["sku"], "qty": 7}
messages += [first.choices[0].message, {"role": "tool", "tool_call_id": call.id, "content": json.dumps(result)}]
final = client.chat.completions.create(model="deepseek/deepseek-r1", messages=messages, tools=tools)
print(final.choices[0].message.content)import OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });
const tools = [{
type: "function",
function: {
name: "check_stock",
description: "Returns the stock level of a product",
parameters: { type: "object", properties: { sku: { type: "string" } }, required: ["sku"] },
},
}];
const messages = [{ role: "user", content: "How many of the Nike NK-42 shoe are in stock?" }];
const first = await client.chat.completions.create({ model: "deepseek/deepseek-r1", messages, tools });
const call = first.choices[0].message.tool_calls[0];
const args = JSON.parse(call.function.arguments);
const result = { sku: args.sku, qty: 7 };
messages.push(first.choices[0].message, { role: "tool", tool_call_id: call.id, content: JSON.stringify(result) });
const final = await client.chat.completions.create({ model: "deepseek/deepseek-r1", messages, tools });
console.log(final.choices[0].message.content);curl https://api.uttapen.ir/v1/chat/completions \
-H "Authorization: Bearer sk-up-…" \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek/deepseek-r1",
"messages": [{"role": "user", "content": "How many of the Nike NK-42 shoe are in stock?"}],
"tools": [{
"type": "function",
"function": {
"name": "check_stock",
"parameters": {"type": "object", "properties": {"sku": {"type": "string"}}, "required": ["sku"]}
}
}]
}'What is R1 good for?
R1 comes from DeepSeek; in uttapen you reach it with the model id "deepseek/deepseek-r1". It accepts up to 64,000 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 16,000 tokens. On price it sits in the "mid-range" band — cheaper than 228 and dearer than 178 of the other paid models in the catalogue.
Capabilities available on this id: 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.
For a back-of-envelope figure: about 1,207 toman per 1,000-word exchange (203,088 in, 725,313 out, per 1M tokens). 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 Kimi K2 0905: R1 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 83 thousand-word requests on R1, and every 1,000 toman is about 829 words of round trip. A job with one million input tokens and one million output tokens comes to 928,400 toman in total. Filling this model's 64,000-token window costs 12,998 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-r1-0528", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out 1.2× dearer (1,207 against 999 toman). The context windows differ too: 64,000 against 163,840 tokens. Maximum answer length differs as well: 16,000 against 32,768 tokens. On parameters the other takes logit_bias, logprobs, min_p, top_logprobs.
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 repetition_penalty, top_k — all through the standard request body. By context size the nearest option from another provider is WizardLM-2 8x22B at 65,535 tokens.
What to use it for: 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.
DeepSeek R1 is here: 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 in an inference pass....
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 | 595 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 1,248 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 38,877 toman |
Frequently asked
- How do I call R1 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". Nothing else in your code changes.
- What does R1 cost in toman?
- 203,088 toman per 1M input tokens and 725,313 toman per 1M output tokens; a 1,000-word request is around 1,207 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does R1 take?
- Up to 64,000 tokens per request, roughly 48k words. A single answer can reach 16,000 tokens.
- Does R1 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.