uttapen
Create an API key

DeepSeek V3.2 API: toman pricing and code

deepseek/deepseek-v3.2

toolsreasoningjson
Create a keyTry it
Input · per 1M tokens
78,044 toman
Output · per 1M tokens
116,050 toman
One 1,000-word request ≈
252 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 39,022 toman / 1M.Pricing and top-ups

DeepSeek V3.2 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-v3.2", 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-v3.2", messages=messages, tools=tools)
print(final.choices[0].message.content)

What is DeepSeek V3.2 good for?

The id for DeepSeek V3.2 in our API is "deepseek/deepseek-v3.2", served from DeepSeek. Context is 163,840 tokens; past that you have to summarise the history yourself. A single response can run to 65,536 tokens. On price it sits in the "very cheap" band — cheaper than 71 and dearer than 335 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 252 toman per 1,000-word exchange (78,044 in, 116,050 out, per 1M tokens). It supports cached input: repeated context is billed at 39,022 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 Llama 3.1 70B Instruct: DeepSeek V3.2 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 397 thousand-word requests on DeepSeek V3.2, and every 1,000 toman is about 3,968 words of round trip. A job with one million input tokens and one million output tokens comes to 194,094 toman in total. Filling this model's 163,840-token window costs 12,787 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-v3.2-exp", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (252 and 256 toman).

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.

What to use it for: high-volume work such as classification, tagging and bulk summarising, logic puzzles and code review, agents that reach out to APIs and databases, extracting data against a fixed schema.

Provider's own description

DeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism...

Three real jobs, priced on this model

Each figure is derived from the prices above and moves when they do.

JobTokensCost
One chat turn with a medium history1,500 in + 400 out163 toman
Summarising a ten-page document4,000 in + 600 out382 toman
Classifying a thousand short rows120,000 in + 20,000 out11,686 toman

Frequently asked

How do I call DeepSeek V3.2 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-v3.2". Nothing else in your code changes.
What does DeepSeek V3.2 cost in toman?
78,044 toman per 1M input tokens and 116,050 toman per 1M output tokens; a 1,000-word request is around 252 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does DeepSeek V3.2 take?
Up to 163,840 tokens per request, roughly 123k words. A single answer can reach 65,536 tokens.
Does DeepSeek V3.2 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.