DeepSeek V4 Pro — DeepSeek's flagship for reasoning, code, and large documents

DeepSeek V4 Pro is the senior model of the DeepSeek V4 line: next to the fast, low-cost V4 Flash, it handles complex reasoning, code, and long documents. On Genosai it comes with an 800,000-token context and adjustable reasoning effort. The client price is 3.2 / 158 ₽ per 1M tokens (input / output), and all input is billed at the cache price, so even huge documents cost next to nothing on input.

Updated: September 27, 2026

• Flagship of the V4 line — The senior DeepSeek V4 model for tasks where reasoning quality matters more than speed: logic, math, complex code, and analysis.
• 800,000-token context — A whole repository, a stack of contracts, or a multi-hour transcript fits into one request without splitting it into chunks.
• Cheap input — All input is billed at the cache price of 3.2 ₽ per 1M tokens. Loading an 800,000-token document costs about 2.6 ₽.
• Reasoning effort — Six levels, from none to max: a quick answer for simple questions and a deep breakdown for logic and code.
• Function calling and API — Supports function calling and connects through the OpenAI-compatible Genosai API with the model id deepseek-v4-pro.

Contents

• What is DeepSeek V4 Pro
• Capabilities
• Examples prompt and response
• How to use on Genosai
• Prompts
• Generation cost
• How it compares
• Limitations and tips
• FAQ

What is DeepSeek V4 Pro

DeepSeek V4 Pro is the flagship model of the DeepSeek V4 line. The line has two models: the fast, economical DeepSeek V4 Flash for everyday work and V4 Pro for tasks where reasoning quality matters most. If Flash is the workhorse for a steady stream of routine requests, Pro is the one you pick to untangle tricky logic, design code that handles edge cases, or pull conclusions out of a large body of text.

The model's main strengths are reasoning, programming, and long documents. It breaks a task down step by step, writes and checks code, turns large materials into structure, and sticks to a requested answer format: a table, a list, or JSON.

On Genosai the model runs with an 800,000-token context window. That is hundreds of pages of text in a single request: contracts, technical documentation, chat exports, or the code of an entire service. The model accepts text only and replies with text; it does not read images. Function calling is supported, so V4 Pro can serve as the brain of agents and automations. There is no built-in web search, so pass fresh data in the request.

The client price is 3.2 / 158 ₽ per 1M tokens (input / output). All input is billed at the cache price, so long prompts and large documents are very cheap on input, and the main cost is the model's answer.

Capabilities

Reasoning and logic

V4 Pro shines where the answer cannot simply be recalled and has to be derived: logic puzzles, calculations, cause-and-effect analysis, and comparing options against criteria. With reasoning effort turned on, the model thinks the solution through first and then gives a short step-by-step result. That helps with financial estimates, business-process analysis, and checking someone else's reasoning for mistakes.

Code

The model writes functions and tests, finds bugs, explains unfamiliar code, and proposes refactoring. The 800,000-token context lets you pass several modules, the configuration, and an error log at once, so V4 Pro reasons about the project as a whole. Code answers usually skip the preamble: code, tests, and minimal explanation, if you ask for it that way.

Long documents

Thanks to the large context and cheap input, V4 Pro is convenient for long texts: minutes, contracts, internal policies, reports, and meeting transcripts. Ask for a summary, a decision table, a list of risks, or an answer to a specific question across the whole document without cutting it into pieces.

Agents and automations

Function calling lets you place the model inside a workflow: parse a request, choose a tool, fill in CRM fields, and draft a reply. The model holds the output structure well, so the result can be passed further down the chain without manual cleanup.

Examples prompt and response

Below are real DeepSeek V4 Pro responses obtained through the Genosai API. The first is a logic puzzle with reasoning effort set to high, the second is a coding task, and the third is a summary of a work document.

Prompt: Puzzle: three boxes contain apples, pears, and a mix of apples and pears. All three labels ("Apples", "Pears", "Mixed") are wrong — none matches the contents. From which box is it enough to draw one fruit to relabel all boxes correctly? Explain briefly, step by step.

DeepSeek V4 Pro response (reasoning_effort: high):
You need to draw one fruit from the box labeled **"Mixed"**.

Here's why, step by step:

1. **All labels are wrong**, so the box labeled "Mixed" cannot actually contain a mix of apples and pears. It must contain either **only apples** or **only pears**.

2. Take one fruit from that box.
   - If you pull out an **apple**, then this box is actually the "Apples" box.
   - If you pull out a **pear**, then this box is actually the "Pears" box.

3. Now consider the remaining two boxes. Both have wrong labels, and you already know the true content of the former "Mixed" box.
   - The box labeled with the fruit you just found (e.g., if you found an apple, the box labeled "Apples") cannot contain that fruit (label is wrong). It also cannot contain the other pure fruit (because that's already in the original "Mixed" box). Therefore, it must be the **mixed** box.
   - The last box then takes the remaining pure fruit.

Thus, a single draw from the "Mixed" box is enough to deduce and correct all three labels.

_Generated on Genosai.io with DeepSeek V4 Pro._

This answer took about 11 seconds and roughly 500 output tokens including reasoning, which comes to less than 0.1 ₽.

Prompt: Write a Python function that validates a credit card number with the Luhn algorithm (digits only, spaces allowed). Add 3 pytest tests. No long explanations.

DeepSeek V4 Pro response:
def luhn_check(card_number: str) -> bool:
    digits = [int(c) for c in card_number if c.isdigit()]
    if not digits:
        return False

    checksum = 0
    num_digits = len(digits)
    parity = num_digits % 2

    for i, digit in enumerate(digits):
        if i % 2 == parity:
            digit *= 2
            if digit > 9:
                digit -= 9
        checksum += digit

    return checksum % 10 == 0

# test_luhn.py
import pytest
from luhn import luhn_check

def test_valid_card_number():
    assert luhn_check("4532015112830366") == True

def test_valid_card_with_spaces():
    assert luhn_check("4532 0151 1283 0366") == True

def test_invalid_card_number():
    assert luhn_check("4532015112830367") == False

_Generated on Genosai.io with DeepSeek V4 Pro._

We ran this code, and all three tests pass. Note one detail worth reviewing: the function silently ignores any non-digit character, not just spaces, so for production use you may want stricter input validation.

Prompt: Here are meeting minutes. Summarize: 1) the key numbers in one line, 2) a table of decisions: task | owner | due date, 3) one main risk. Keep it short.
[followed by four-item sales team meeting minutes]

DeepSeek V4 Pro response:
1) Key numbers: Revenue $435,000 vs $500,000 target (87%); 7 of 12 enterprise deals closed; retail +9%; lead-to-meeting conversion 32%→24%; cost per lead $11.00→$14.50; loss reason recorded on only 40% of lost deals.

2) Decisions:

| Task | Owner | Due date |
|---|---|---|
| Bring back previous first-call script | Olivia | Sep 25 |
| Prepare lawyer-approved standard contract template | Paul | Oct 3 |
| Rebuild ad campaigns; cut keywords below 1% conversion | Sam | Oct 1 |
| Take over 5 stalled enterprise accounts and hold follow-up meetings | Dennis | Oct 10 |

3) Main risk: Enterprise segment slippage — 3 large deals already pushed to October and contract delays threaten the pipeline.

_Generated on Genosai.io with DeepSeek V4 Pro._

How to use on Genosai

1. Open the Genosai studio on the Text tab.
2. Pick DeepSeek V4 Pro in the model list.
3. If needed, open the chat settings panel: temperature, top_p, a response limit of up to 16,384 tokens, reasoning effort, and a system prompt.
4. Describe the task: goal, context, and answer format. Paste the document or code directly into the message.
5. Read the answer and refine it in the same thread — the history is saved.

Billing is per token with no subscription, you can top up with a Russian bank card, and no VPN is needed.

Reasoning effort

The chat settings panel has a Reasoning effort option. For DeepSeek V4 Pro the levels are none, low, medium, high, xhigh, and max, and the same values are used in the API. The default is none: the model answers right away. For logic, math, and complex code, choose medium or high, and keep max for the hardest tasks. Reasoning tokens are billed as output tokens, so it is better to leave reasoning off for simple questions.

Through the API

The Genosai API follows the OpenAI format, so the usual SDKs and libraries work. The model list is available at GET /v1/models.

curl https://api.genosai.io/v1/chat/completions \
  -H "Authorization: Bearer sdk_YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v4-pro",
    "reasoning_effort": "high",
    "messages": [{"role": "user", "content": "Solve this step by step..."}]
  }'

Prompts

The prompts below play to V4 Pro's strengths: reasoning, code, and large documents. Stating the answer format explicitly works well — a table, a number of points, a length limit.

Here is a supply contract. List every buyer obligation with deadlines and penalties in a table: clause | obligation | deadline | penalty.

Here are three project modules and an error log. Find the cause of the failure, explain the mechanism, and propose a fix with a test.

Solve the problem step by step and check the answer by substitution at the end. If data is missing, say exactly what.

Compare two architecture options on cost, maintenance complexity, scalability, and risks. Finish with a table and a recommendation.

Here is a two-hour meeting transcript. Write minutes: decisions, owners, deadlines, open questions.

Write a function with error handling and type hints, and add tests for edge cases. No long explanations.

Generation cost

The client price of DeepSeek V4 Pro on Genosai is 3.2 / 158 ₽ per 1M tokens (input / output). All input is billed at the cache price, with no separate, higher rate for input tokens. That makes long prompts and large documents very cheap on input, while most of the bill comes from the model's answer.

For a rough budget: a 100,000-token document with a 2,000-token answer costs about 0.64 ₽ (0.32 ₽ for input and 0.32 ₽ for output). Even a fully loaded 800,000-token context costs about 2.6 ₽ on input. With reasoning effort turned on, thinking tokens are billed as output tokens: 3,000 reasoning tokens add about 0.5 ₽.

There is no subscription; only actual usage is charged. Current prices are in the Pricing section.

How it compares

On input price, V4 Pro is one of the most cost-effective models in the catalog for long contexts, and on output price it sits in the middle.

DeepSeek V4 Flash is the junior model of the same line with the same context: about 2.9 times cheaper on input and 3 times cheaper on output. Use Flash for a stream of simple tasks and Pro for complex logic and code. DeepSeek V3.2 is cheaper on output (34 vs 158 ₽), but its input is 7 times more expensive and its context is only 120,000 tokens. Qwen3.5 397B costs 15 times more than V4 Pro on input and 2 times more on output. GPT-6 Sol is 50 times more expensive on input and about 5 times on output, but it accepts images. Claude Sonnet 5 offers the same 800,000-token context, but its input costs about 59 times more and its output almost 6 times more. Gemini 3.1 Pro is 50 times more expensive on input and about 6 times on output, with a 120,000-token context.

Limitations and tips

DeepSeek V4 Pro works with text only: it does not accept images, screenshots, or scans. Paste code, tables, and documents into the message as text. There is no built-in web search, so the model does not know fresh news, exchange rates, or prices; pass current data in the request.

Manage reasoning effort deliberately. With reasoning off, the model answers faster and cheaper, which is enough for copy, summaries, and reference answers. For math, logic, algorithms, and complex code, choose medium or high: the answer arrives later but is more reliable. Remember that reasoning tokens are billed as output tokens.

Use the large context wisely: load the relevant files and documents, and state the question clearly at the end of the prompt. Specify the answer format explicitly — a table, a list, JSON, a length limit.

Most importantly, verify the result manually when money, contracts, or important decisions depend on it. Even a strong model can misread a figure or blur a detail: run the code, and check facts and numbers against the source.

FAQ

What is DeepSeek V4 Pro?

It is the flagship language model of the DeepSeek V4 line. The junior model, V4 Flash, is faster and cheaper, while V4 Pro is built for tasks that need deeper reasoning: complex code, logic, math, and analysis of large documents.

How much does DeepSeek V4 Pro cost on Genosai?

The client price is 3.2 / 158 ₽ per 1M tokens (input / output). All input is billed at the cache price, with no separate higher input rate. Most of the cost comes from output tokens, including reasoning tokens. There is no subscription; you pay only for actual usage.

How does V4 Pro differ from DeepSeek V4 Flash?

V4 Pro is the senior model with stronger reasoning; V4 Flash is the fast, economical option. Both have an 800,000-token context. Flash is about 2.9 times cheaper on input (1.12 vs 3.2 ₽) and 3 times cheaper on output (52.8 vs 158 ₽ per 1M).

Can DeepSeek V4 Pro work with images?

No. The model accepts text only and replies with text. Paste code, tables, and documents into the message as text. To analyze pictures, choose a model with vision from the catalog.

What is reasoning effort and when should I turn it on?

It is a setting that lets the model think before answering. The levels are none, low, medium, high, xhigh, and max, and the default is none. For logic, math, and complex code, pick medium or high. Reasoning tokens are billed as output tokens.

Can I use DeepSeek V4 Pro through an API?

Yes. The Genosai API follows the OpenAI format: send a request to /v1/chat/completions with the model deepseek-v4-pro and an sdk_... key. Function calling and the reasoning_effort parameter are supported, and the model list is at GET /v1/models.

Try DeepSeek V4 Pro on Genosai