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DeepSeek V4 Pro 0423 API: toman pricing and code

deepseek/deepseek-v4-pro

Tool callingReasoningJSON output
Create a keyTry it
Input · per 1M tokens
553,951 toman
Output · per 1M tokens
1,107,902 toman
One 1,000-word request ≈
2,160 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 46,740 toman per 1M tokens.Pricing and top-ups

DeepSeek V4 Pro 0423 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-v4-pro", 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-v4-pro", 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-v4-pro", 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-v4-pro", 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-v4-pro",
    "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 DeepSeek V4 Pro 0423 good for?

DeepSeek V4 Pro 0423 is one of DeepSeek's models. Put "deepseek/deepseek-v4-pro" in the model field and the rest of your code stays as it is. DeepSeek V4 Pro 0423 keeps 1,048,576 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 393,216 tokens. On price it sits in the "mid-range" band — cheaper than 144 and dearer than 249 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 2,160 toman per 1,000-word exchange (553,951 in, 1,107,902 out, per 1M tokens). It supports cached input: repeated context is billed at 46,740 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 GLM 5: DeepSeek V4 Pro 0423 works out about 10% more expensive, 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 46 thousand-word requests on DeepSeek V4 Pro 0423, and every 1,000 toman is about 463 words of round trip. A job with one million input tokens and one million output tokens comes to 1,661,853 toman in total. Filling this model's 1,048,576-token window costs 580,860 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-v4-pro-0813", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (2,160 and 2,122 toman). Maximum answer length differs as well: 393,216 against 943,718 tokens. On parameters, this one takes max_completion_tokens.

DeepSeek has 18 models in our catalogue; the cheapest is DeepSeek V4.1 Flash (batch) at 90 toman per thousand words and the dearest DeepSeek Pro Latest at 3,647. Among the less common parameters it accepts logit_bias, logprobs, max_completion_tokens, min_p, presence_penalty, reasoning_effort — all through the standard request body. By context size the nearest option from another provider is Ember-1 at 1,048,576 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, analysing a long document or codebase in one request.

Provider's own description

DeepSeek V4 Pro is a large-scale Mixture-of-Experts model from DeepSeek with 1.6T total parameters and 49B activated parameters, supporting a 1M-token context window. It is designed for advanced reasoning, coding,...

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 out1,274 toman
Summarising a ten-page document4,000 in + 600 out2,881 toman
Classifying a thousand short rows120,000 in + 20,000 out88,632 toman

Price history

DateInput (toman/1M)Output (toman/1M)
29 Oct 2026553,9511,107,902
29 Oct 2026296,632593,265
29 Oct 2026299,584599,169
29 Oct 2026303,078606,157
29 Oct 2026305,970611,940
29 Oct 2026308,922617,844
29 Oct 2026309,524619,049
29 Oct 2026310,669621,338
29 Oct 2026330,729661,459
28 Oct 2026307,777615,554
28 Oct 2026308,922617,844
28 Oct 2026310,669621,338

Every price change for this model is recorded. Toman figures use today's rate.

Frequently asked

How do I call DeepSeek V4 Pro 0423 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-v4-pro". Nothing else in your code changes.
What does DeepSeek V4 Pro 0423 cost in toman?
553,951 toman per 1M input tokens and 1,107,902 toman per 1M output tokens; a 1,000-word request is around 2,160 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does DeepSeek V4 Pro 0423 take?
Up to 1,048,576 tokens per request, roughly 786k words. A single answer can reach 393,216 tokens.
Does DeepSeek V4 Pro 0423 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.