R1 Distill Llama 70B API: toman pricing and code
deepseek/deepseek-r1-distill-llama-70b
reasoningYou are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups
R1 Distill Llama 70B 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-distill-llama-70b",
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-distill-llama-70b",
"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-distill-llama-70b",
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 Distill Llama 70B good for?
R1 Distill Llama 70B comes from DeepSeek; in uttapen you reach it with the model id "deepseek/deepseek-r1-distill-llama-70b". It accepts up to 8,192 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 7,372 tokens. On price it sits in the "cheap" band — cheaper than 120 and dearer than 286 of the other paid models in the catalogue.
Capabilities available on this id: 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 603 toman per 1,000-word exchange (232,100 in, 232,100 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 Skyfall 36B V2: R1 Distill Llama 70B works out roughly 1.2× 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 166 thousand-word requests on R1 Distill Llama 70B, and every 1,000 toman is about 1,658 words of round trip. A job with one million input tokens and one million output tokens comes to 464,200 toman in total. Filling this model's 8,192-token window costs 1,901 toman on the input side alone, which is the real reason to keep conversation history short.
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. It does not support response_format, tool_choice, tools, structured_outputs, 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 Gemma 2 27B at 8,192 tokens.
What to use it for: high-volume work such as classification, tagging and bulk summarising, logic puzzles and code review, short single-turn requests (the context window is small).
DeepSeek R1 Distill Llama 70B is a distilled large language model based on [Llama-3.3-70B-Instruct](/meta-llama/llama-3.3-70b-instruct), using outputs from [DeepSeek R1](/deepseek/deepseek-r1). The model combines advanced distillation techniques to achieve high performance across...
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 | 441 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 1,068 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 32,494 toman |
Frequently asked
- How do I call R1 Distill Llama 70B 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-distill-llama-70b". Nothing else in your code changes.
- What does R1 Distill Llama 70B cost in toman?
- 232,100 toman per 1M input tokens and 232,100 toman per 1M output tokens; a 1,000-word request is around 603 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does R1 Distill Llama 70B take?
- Up to 8,192 tokens per request, roughly 6k words. A single answer can reach 7,372 tokens.
- Can I stream R1 Distill Llama 70B'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.