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DeepSeek V4.1 Flash API: toman pricing and code

deepseek/deepseek-v4.1-flash

Image inputTool callingReasoningJSON output
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Input · per 1M tokens
15,580 toman
Output · per 1M tokens
173,110 toman
One 1,000-word request ≈
245 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 6,232 toman per 1M tokens.Pricing and top-ups

DeepSeek V4.1 Flash 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-v4.1-flash",
    "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-v4.1-flash",
    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-v4.1-flash",
  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 V4.1 Flash good for?

DeepSeek V4.1 Flash is one of DeepSeek's models. Put "deepseek/deepseek-v4.1-flash" in the model field and the rest of your code stays as it is. DeepSeek V4.1 Flash 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 943,718 tokens. On price it sits in the "very cheap" band — cheaper than 300 and dearer than 85 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 245 toman per 1,000-word exchange (15,580 in, 173,110 out, per 1M tokens). It supports cached input: repeated context is billed at 6,232 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 Claude Haiku 5.5: DeepSeek V4.1 Flash 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 408 thousand-word requests on DeepSeek V4.1 Flash, and every 1,000 toman is about 4,082 words of round trip. A job with one million input tokens and one million output tokens comes to 188,690 toman in total. Filling this model's 1,048,576-token window costs 16,337 toman on the input side alone, which is the real reason to keep conversation history short.

This id gets confused with "deepseek/deepseek-v4.1-flash:batch", because the underlying model is the same. The difference is the suffix: no suffix (the standard variant) against "batch". On a thousand-word request this variant works out about 20% more expensive (245 against 202 toman). Maximum answer length differs as well: 943,718 against 131,072 tokens. On parameters, this one takes logprobs, parallel_tool_calls, top_a, top_logprobs.

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, 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 V4.1 Flash is a sparse mixture-of-experts model from DeepSeek, and the first built on the company's Causal Encoder-Decoder (CED) architecture. It activates 8B parameters on input and 16B on...

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 out93 toman
Summarising a ten-page document4,000 in + 600 out166 toman
Classifying a thousand short rows120,000 in + 20,000 out5,332 toman

Other variants of this model

Same core model, different execution terms and different price. This page is the standard variant.

VariantModel idOutput / 1MContext
batchdeepseek/deepseek-v4.1-flash:batch116,3301,048,576

Put the variant's id verbatim in the model field; nothing else in your code changes.

Price history

DateInput (toman/1M)Output (toman/1M)
4 Nov 202615,580173,110
29 Oct 202615,580173,110
27 Oct 202648,471145,412
27 Oct 202614,9413,462
26 Oct 202616,51566,059
24 Oct 20266,924346,219
23 Oct 2026103,866415,463
23 Oct 202657,126228,505
22 Oct 2026103,866415,463
22 Oct 202624,235173,110
22 Oct 202611,84147,363
22 Oct 20268,28833,154

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

Frequently asked

How do I call DeepSeek V4.1 Flash 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-v4.1-flash". Nothing else in your code changes.
What does DeepSeek V4.1 Flash cost in toman?
15,580 toman per 1M input tokens and 173,110 toman per 1M output tokens; a 1,000-word request is around 245 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does DeepSeek V4.1 Flash take?
Up to 1,048,576 tokens per request, roughly 786k words. A single answer can reach 943,718 tokens.
Does DeepSeek V4.1 Flash 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.