DeepSeek V4 Flash 0731 (batch) API: toman pricing and code
deepseek/deepseek-v4-flash-0731:batch
toolsreasoningjsonYou are billed for the usage the request actually reported. Prices follow the market. Cached input: 8,704 toman / 1M.Pricing and top-ups
DeepSeek V4 Flash 0731 (batch) example: JSON output against a schema
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-…")
schema = {
"name": "ticket",
"schema": {
"type": "object",
"properties": {
"category": {"type": "string", "enum": ["fani", "mali", "forush"]},
"priority": {"type": "integer", "minimum": 1, "maximum": 5},
"summary": {"type": "string"},
},
"required": ["category", "priority", "summary"],
"additionalProperties": False,
},
"strict": True,
}
resp = client.chat.completions.create(
model="deepseek/deepseek-v4-flash-0731:batch",
messages=[{"role": "user", "content": "Ticket: "For two days I cannot download my invoice and I was charged twice.""}],
response_format={"type": "json_schema", "json_schema": schema},
)
print(json.loads(resp.choices[0].message.content))import OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });
const resp = await client.chat.completions.create({
model: "deepseek/deepseek-v4-flash-0731:batch",
messages: [{ role: "user", content: "Ticket: "For two days I cannot download my invoice and I was charged twice."" }],
response_format: {
type: "json_schema",
json_schema: {
name: "ticket",
strict: true,
schema: {
type: "object",
properties: {
category: { type: "string", enum: ["fani", "mali", "forush"] },
priority: { type: "integer", minimum: 1, maximum: 5 },
summary: { type: "string" },
},
required: ["category", "priority", "summary"],
additionalProperties: false,
},
},
},
});
console.log(JSON.parse(resp.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-0731:batch",
"messages": [{"role": "user", "content": "Classify the ticket and give it a priority from 1 to 5."}],
"response_format": {"type": "json_object"}
}'What is DeepSeek V4 Flash 0731 (batch) good for?
DeepSeek V4 Flash 0731 (batch) comes from DeepSeek; in uttapen you reach it with the model id "deepseek/deepseek-v4-flash-0731:batch". 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 54 and dearer than 352 of the other paid models in the catalogue.
What it can do beyond plain text: 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.
Pricing is 40,618 toman per 1M input tokens and 81,235 per 1M output tokens. A 1,000-word round trip on DeepSeek V4 Flash 0731 (batch) lands near 158 toman. It supports cached input: repeated context is billed at 8,704 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 Qwen3 32B: DeepSeek V4 Flash 0731 (batch) works out roughly 1.2× 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 633 thousand-word requests on DeepSeek V4 Flash 0731 (batch), and every 1,000 toman is about 6,329 words of round trip. A job with one million input tokens and one million output tokens comes to 121,853 toman in total. Filling this model's 1,048,576-token window costs 42,591 toman on the input side alone, which is the real reason to keep conversation history short.
This id gets confused with "deepseek/deepseek-v4-flash-0731", because the underlying model is the same. The difference is the suffix: "batch" against the standard variant. Both land at nearly the same price on a thousand-word request (158 and 158 toman). The context windows differ too: 1,048,576 against 1,310,720 tokens. Maximum answer length differs as well: 943,718 against 131,072 tokens. On parameters the other takes logprobs, parallel_tool_calls, seed, top_a.
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, min_p, reasoning_effort, repetition_penalty, top_k — all through the standard request body. It does not support seed, which most models here do, so test before switching if your code relies on them. By context size the nearest option from another provider is Gemini 2.5 Flash at 1,048,576 tokens.
Good fits: 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 0731 is a sparse mixture-of-experts model from DeepSeek, with 13B active parameters out of 284B total. This re-post-trained revision is suited for coding, reasoning, and agent workflows....
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 | 93 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 211 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 6,499 toman |
Other variants of this model
Same core model, different execution terms and different price. This page is the batch (cheaper, slower) variant.
| Variant | Model id | Output / 1M | Context |
|---|---|---|---|
| standard | deepseek/deepseek-v4-flash-0731 | 81,235 | 1,310,720 |
Put the variant's id verbatim in the model field; nothing else in your code changes.
Frequently asked
- How do I call DeepSeek V4 Flash 0731 (batch) 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-0731:batch". Nothing else in your code changes.
- What does DeepSeek V4 Flash 0731 (batch) cost in toman?
- 40,618 toman per 1M input tokens and 81,235 toman per 1M output tokens; a 1,000-word request is around 158 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does DeepSeek V4 Flash 0731 (batch) take?
- Up to 1,048,576 tokens per request, roughly 786k words. A single answer can reach 943,718 tokens.
- Does DeepSeek V4 Flash 0731 (batch) 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.