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DeepSeek V3 0324 API: toman pricing and code

deepseek/deepseek-chat-v3-0324

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Input · per 1M tokens
72,531 toman
Output · per 1M tokens
290,125 toman
One 1,000-word request ≈
471 toman

You are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups

DeepSeek V3 0324 example: JSON output against a schema

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-…")

schema = {
    "name": "ticket",
    "schema": {
        "type": "object",
        "properties": {
            "category": {"type": "string", "enum": ["fani", "mali", "forush"]},
            "priority": {"type": "integer", "minimum": 1, "maximum": 5},
            "summary": {"type": "string"},
        },
        "required": ["category", "priority", "summary"],
        "additionalProperties": False,
    },
    "strict": True,
}

resp = client.chat.completions.create(
    model="deepseek/deepseek-chat-v3-0324",
    messages=[{"role": "user", "content": "Ticket: "For two days I cannot download my invoice and I was charged twice.""}],
    response_format={"type": "json_schema", "json_schema": schema},
)
print(json.loads(resp.choices[0].message.content))

What is DeepSeek V3 0324 good for?

DeepSeek publishes this model; we expose it under the id "deepseek/deepseek-chat-v3-0324". Its context window is 163,840 tokens, roughly 123k English words in one request. A single response can run to 147,456 tokens. On price it sits in the "cheap" band — cheaper than 135 and dearer than 271 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. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper.

For a back-of-envelope figure: about 471 toman per 1,000-word exchange (72,531 in, 290,125 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 Qwen3 Coder 480B A35B: DeepSeek V3 0324 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 212 thousand-word requests on DeepSeek V3 0324, and every 1,000 toman is about 2,123 words of round trip. A job with one million input tokens and one million output tokens comes to 362,656 toman in total. Filling this model's 163,840-token window costs 11,884 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", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (471 and 456 toman). Maximum answer length differs as well: 147,456 against 16,384 tokens. On parameters, this one takes logprobs, 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 logit_bias, logprobs, min_p, repetition_penalty, top_k, top_logprobs — all through the standard request body. It does not support include_reasoning, reasoning, 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 Llama Guard 4 12B at 163,840 tokens.

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

Provider's own description

DeepSeek V3, a 685B-parameter, mixture-of-experts model, is the latest iteration of the flagship chat model family from the DeepSeek team. It succeeds the [DeepSeek V3](/deepseek/deepseek-chat-v3) model and performs really well...

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 out225 toman
Summarising a ten-page document4,000 in + 600 out464 toman
Classifying a thousand short rows120,000 in + 20,000 out14,506 toman

Frequently asked

How do I call DeepSeek V3 0324 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-chat-v3-0324". Nothing else in your code changes.
What does DeepSeek V3 0324 cost in toman?
72,531 toman per 1M input tokens and 290,125 toman per 1M output tokens; a 1,000-word request is around 471 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does DeepSeek V3 0324 take?
Up to 163,840 tokens per request, roughly 123k words. A single answer can reach 147,456 tokens.
Does DeepSeek V3 0324 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.