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DeepSeek V4 Flash Vision Exp API: toman pricing and code

deepseek/deepseek-v4-flash-vision-exp

Image inputTool callingReasoningJSON output
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Input · per 1M tokens
74,645 toman
Output · per 1M tokens
223,935 toman
One 1,000-word request ≈
388 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 2,375 toman per 1M tokens.Pricing and top-ups

DeepSeek V4 Flash Vision Exp example: reading an image into JSON

The example is picked from this model's own capabilities. Drop in your key and it runs as is.

# tip: a data URI works too — base64 the file and prefix it with data:image/jpeg;base64,
curl https://api.uttapen.ir/v1/chat/completions \
  -H "Authorization: Bearer sk-up-…" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek/deepseek-v4-flash-vision-exp",
    "messages": [{
      "role": "user",
      "content": [
        {"type": "text", "text": "Return only the invoice number and the total, as JSON."},
        {"type": "image_url", "image_url": {"url": "https://example.com/factor.jpg"}}
      ]
    }]
  }'
import base64, json
from openai import OpenAI

client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")

img = base64.b64encode(open("factor.jpg", "rb").read()).decode()

resp = client.chat.completions.create(
    model="deepseek/deepseek-v4-flash-vision-exp",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Return the invoice number, the date and the total as JSON."},
            {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img}"}},
        ],
    }],
    response_format={"type": "json_object"},
)
print(json.loads(resp.choices[0].message.content))
import OpenAI from "openai";
import { readFileSync } from "node:fs";

const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });
const img = readFileSync("factor.jpg").toString("base64");

const resp = await client.chat.completions.create({
  model: "deepseek/deepseek-v4-flash-vision-exp",
  messages: [{
    role: "user",
    content: [
      { type: "text", text: "Return the invoice number, the date and the total as JSON." },
      { type: "image_url", image_url: { url: `data:image/jpeg;base64,${img}` } },
    ],
  }],
  response_format: { type: "json_object" },
});
console.log(JSON.parse(resp.choices[0].message.content));

What is DeepSeek V4 Flash Vision Exp good for?

DeepSeek V4 Flash Vision Exp is one of DeepSeek's models. Put "deepseek/deepseek-v4-flash-vision-exp" in the model field and the rest of your code stays as it is. DeepSeek V4 Flash Vision Exp 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 262,144 tokens. On price it sits in the "cheap" band — cheaper than 280 and dearer than 118 of the other paid models in the catalogue.

Capabilities available on this id: it reads images directly, which makes it a real option for invoices, forms and screenshots; 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 388 toman per 1,000-word exchange (74,645 in, 223,935 out, per 1M tokens). It supports cached input: repeated context is billed at 2,375 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 4 Maverick, and DeepSeek V4 Flash Vision Exp costs about the same, 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 258 thousand-word requests on DeepSeek V4 Flash Vision Exp, and every 1,000 toman is about 2,577 words of round trip. A job with one million input tokens and one million output tokens comes to 298,580 toman in total. Filling this model's 1,048,576-token window costs 78,271 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-v4.1-flash", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 60% more expensive (388 against 245 toman). Maximum answer length differs as well: 262,144 against 943,718 tokens. On parameters the other takes parallel_tool_calls, top_a.

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, min_p, presence_penalty, reasoning_effort, repetition_penalty — 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, pulling text and fields out of images, 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 Flash Vision Exp is an experimental vision-enabled version of DeepSeek V4 Flash 0731 from DeepSeek, adding image understanding while matching the base model on text capabilities including agents,...

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 out202 toman
Summarising a ten-page document4,000 in + 600 out433 toman
Classifying a thousand short rows120,000 in + 20,000 out13,436 toman

Price history

DateInput (toman/1M)Output (toman/1M)
29 Sep 202674,645223,935
28 Sep 2026152,337457,010
27 Sep 202674,645223,935
21 Sep 202676,168228,505
18 Sep 202674,645223,935
9 Sep 202676,168228,505
8 Sep 202676,168228,505
8 Sep 2026152,337457,010
8 Sep 202676,168228,505
8 Sep 2026152,337457,010
7 Sep 202676,168228,505
7 Sep 2026152,337457,010

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

Frequently asked

How do I call DeepSeek V4 Flash Vision Exp 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-vision-exp". Nothing else in your code changes.
What does DeepSeek V4 Flash Vision Exp cost in toman?
74,645 toman per 1M input tokens and 223,935 toman per 1M output tokens; a 1,000-word request is around 388 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does DeepSeek V4 Flash Vision Exp take?
Up to 1,048,576 tokens per request, roughly 786k words. A single answer can reach 262,144 tokens.
Does DeepSeek V4 Flash Vision Exp support streaming and tool calling?
Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape. Image input is accepted through image_url, as a data URI or a public URL. Structured output through response_format works too.