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DeepSeek V4 Flash Latest API: toman pricing and code

~deepseek/deepseek-v4-flash-latest

This id is an alias that always points at the newest version of this model. Send a fixed version's id if answers must not change when a new version ships.

Tool callingReasoningJSON output
Create a keyTry it
Input · per 1M tokens
20,427 toman
Output · per 1M tokens
103,866 toman
One 1,000-word request ≈
162 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 12,118 toman per 1M tokens.Pricing and top-ups

DeepSeek V4 Flash Latest 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-flash-latest", 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-flash-latest", 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-flash-latest", 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-flash-latest", 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-flash-latest",
    "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 Flash Latest good for?

DeepSeek V4 Flash Latest comes from DeepSeek; in uttapen you reach it with the model id "~deepseek/deepseek-v4-flash-latest". It accepts up to 1,048,576 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 943,718 tokens. On price it sits in the "very cheap" band — cheaper than 330 and dearer than 61 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 162 toman per 1,000-word exchange (20,427 in, 103,866 out, per 1M tokens). It supports cached input: repeated context is billed at 12,118 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 Voxtral Small 24B 2507: DeepSeek V4 Flash Latest works out about 9% 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 617 thousand-word requests on DeepSeek V4 Flash Latest, and every 1,000 toman is about 6,173 words of round trip. A job with one million input tokens and one million output tokens comes to 124,293 toman in total. Filling this model's 1,048,576-token window costs 21,419 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-flash-0731", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (162 and 162 toman).

DeepSeek has 18 models in our catalogue; the cheapest is DeepSeek V4 Flash 0423 at 123 toman per thousand words and the dearest DeepSeek Pro Latest at 3,642. Among the less common parameters it accepts logit_bias, logprobs, min_p, parallel_tool_calls, 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: 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, analysing a long document or codebase in one request.

Provider's own description

This model always redirects to the latest model in the DeepSeek V4 Flash family.

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 out72 toman
Summarising a ten-page document4,000 in + 600 out144 toman
Classifying a thousand short rows120,000 in + 20,000 out4,529 toman

Price history

DateInput (toman/1M)Output (toman/1M)
5 Nov 202620,427103,866
5 Nov 202615,026103,866
5 Nov 202615,511103,866
4 Nov 202616,861103,866
4 Nov 202618,246103,866
4 Nov 202619,146103,866
31 Oct 202620,081103,866
31 Oct 202620,081103,866
31 Oct 202613,433103,866
31 Oct 202618,41946,428
31 Oct 202613,433103,866
31 Oct 202618,073103,866

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

Frequently asked

How do I call DeepSeek V4 Flash Latest 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-flash-latest". Nothing else in your code changes.
What does DeepSeek V4 Flash Latest cost in toman?
20,427 toman per 1M input tokens and 103,866 toman per 1M output tokens; a 1,000-word request is around 162 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does DeepSeek V4 Flash Latest take?
Up to 1,048,576 tokens per request, roughly 786k words. A single answer can reach 943,718 tokens.
Does DeepSeek V4 Flash Latest 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.