DeepSeek V4 Flash 0423 API: toman pricing and code
deepseek/deepseek-v4-flash
toolsreasoningjsonYou are billed for the usage the request actually reported. Prices follow the market. Cached input: 5,141 toman / 1M.Pricing and top-ups
DeepSeek V4 Flash 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-flash", 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", 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", 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", 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",
"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 0423 good for?
DeepSeek V4 Flash 0423 comes from DeepSeek; in uttapen you reach it with the model id "deepseek/deepseek-v4-flash". 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 384,000 tokens. On price it sits in the "very cheap" band — cheaper than 28 and dearer than 378 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 100 toman per 1,000-word exchange (25,707 in, 51,414 out, per 1M tokens). It supports cached input: repeated context is billed at 5,141 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 Ling 3.0 Flash Fin: DeepSeek V4 Flash 0423 works out roughly 1.1× 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 1,000 thousand-word requests on DeepSeek V4 Flash 0423, and every 1,000 toman is about 10,000 words of round trip. A job with one million input tokens and one million output tokens comes to 77,120 toman in total. Filling this model's 1,048,576-token window costs 26,956 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-latest", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out 1.3× dearer (100 against 79 toman). The context windows differ too: 1,048,576 against 1,310,720 tokens. Maximum answer length differs as well: 384,000 against 393,216 tokens. On parameters, this one takes max_completion_tokens while the other takes parallel_tool_calls.
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, max_completion_tokens, min_p, reasoning_effort, repetition_penalty — all through the standard request body. By context size the nearest option from another provider is Gemini 2.5 Flash 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.
DeepSeek V4 Flash is an efficiency-optimized Mixture-of-Experts model from DeepSeek with 284B total parameters and 13B activated parameters, supporting a 1M-token context window. It is designed for fast inference and...
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 | 59 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 134 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 4,113 toman |
Price history
| Date | Input (toman/1M) | Output (toman/1M) |
|---|---|---|
| 2026-09-07 | 25,707 | 51,414 |
| 2026-09-07 | 26,072 | 52,145 |
| 2026-09-06 | 23,274 | 46,548 |
Every price change for this model is recorded. Toman figures use today's rate.
Frequently asked
- How do I call DeepSeek V4 Flash 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-flash". Nothing else in your code changes.
- What does DeepSeek V4 Flash 0423 cost in toman?
- 25,707 toman per 1M input tokens and 51,414 toman per 1M output tokens; a 1,000-word request is around 100 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does DeepSeek V4 Flash 0423 take?
- Up to 1,048,576 tokens per request, roughly 786k words. A single answer can reach 384,000 tokens.
- Does DeepSeek V4 Flash 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.