DeepSeek V4 Pro 0813 (batch) API: toman pricing and code
deepseek/deepseek-v4-pro-0813:batch
toolsreasoningjsonYou are billed for the usage the request actually reported. Prices follow the market. Cached input: 37,716 toman / 1M.Pricing and top-ups
DeepSeek V4 Pro 0813 (batch) example: a multi-step problem with reasoning
The example is picked from this model's own capabilities. Drop in your key and it runs as is.
from openai import OpenAI
client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")
resp = client.chat.completions.create(
model="deepseek/deepseek-v4-pro-0813:batch",
messages=[{"role": "user", "content": (
"A shop has 3 warehouses. A ships 120 orders a day, B ships 85 and C ships 40. "
"If C closes and its load is split between A and B in proportion to their capacity, how many does each ship a day? "
"Work through it step by step and give just the two numbers at the end."
)}],
reasoning_effort="medium", # low | medium | high
)
print(resp.choices[0].message.content)
# reasoning tokens count as output tokens too:
print(resp.usage.completion_tokens_details)curl https://api.uttapen.ir/v1/chat/completions \
-H "Authorization: Bearer sk-up-…" \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek/deepseek-v4-pro-0813:batch",
"messages": [{"role": "user", "content": "120 and 85 daily orders split across two warehouses; work through it step by step."}],
"reasoning": {"effort": "medium"}
}'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-pro-0813:batch",
messages: [{ role: "user", content: "120 and 85 daily orders are split between two warehouses; work through it step by step and give just the two numbers at the end." }],
reasoning_effort: "medium",
});
console.log(resp.choices[0].message.content);
console.log(resp.usage.completion_tokens_details);What is DeepSeek V4 Pro 0813 (batch) good for?
The id for DeepSeek V4 Pro 0813 (batch) in our API is "deepseek/deepseek-v4-pro-0813:batch", served from DeepSeek. Context is 1,048,576 tokens; past that you have to summarise the history yourself. A single response can run to 943,718 tokens. On price it sits in the "mid-range" band — cheaper than 264 and dearer than 142 of the other paid models in the catalogue.
What you get on top of text in, text out: 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.
Input runs at 382,965 toman per 1M tokens and output at 1,148,895 — output costs 3× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 37,716 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 GPT-3.5 Turbo 16k: DeepSeek V4 Pro 0813 (batch) works out roughly 1.3× 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 50 thousand-word requests on DeepSeek V4 Pro 0813 (batch), and every 1,000 toman is about 502 words of round trip. A job with one million input tokens and one million output tokens comes to 1,531,860 toman in total. Filling this model's 1,048,576-token window costs 401,568 toman on the input side alone, which is the real reason to keep conversation history short.
This id gets confused with "deepseek/deepseek-v4-pro-0813", because the underlying model is the same. The difference is the suffix: "batch" against the standard variant. On a thousand-word request this variant works out 1.3× dearer (1,991 against 1,583 toman). Maximum answer length differs as well: 943,718 against 384,000 tokens. On parameters the other takes logprobs, seed, top_logprobs.
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.
Where it makes sense: product chatbots and internal assistants, where cost and quality have to balance, 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 Pro 0813 is a large-scale mixture-of-experts model from DeepSeek. This is the GA release of DeepSeek V4 Pro.
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 | 1,034 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 2,221 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 68,934 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-pro-0813 | 913,372 | 1,048,576 |
Put the variant's id verbatim in the model field; nothing else in your code changes.
Frequently asked
- How do I call DeepSeek V4 Pro 0813 (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-pro-0813:batch". Nothing else in your code changes.
- What does DeepSeek V4 Pro 0813 (batch) cost in toman?
- 382,965 toman per 1M input tokens and 1,148,895 toman per 1M output tokens; a 1,000-word request is around 1,991 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does DeepSeek V4 Pro 0813 (batch) take?
- Up to 1,048,576 tokens per request, roughly 786k words. A single answer can reach 943,718 tokens.
- Does DeepSeek V4 Pro 0813 (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.