DeepSeek V4.1 Flash 1017 for coding and writing
DeepSeek V4.1 Flash 1017 is available on Genosai for coding, document analysis and business writing. It has a context window of up to 1 million tokens, with rates of RUB 10 for input, RUB 40 for output and RUB 2 for cached input per million tokens. Open the model in Studio, paste your task and start a text conversation.
Updated: October 23, 2026
- October 17 release — Choose the specific DeepSeek V4.1 Flash 1017 version in the model catalog and API.
- Up to 1M tokens — A large context window for source material, conversation history and the model response within the available limit.
- RUB 10/40/2 rates — Per million tokens for input, output and cached input, respectively. The minimum charge per request is RUB 0.10.
- Streaming responses — Read the text while the model generates the rest of its answer.
- Studio and API — Start in your browser or use cr/deepseek-v4.1-flash-1017 in your own integration.
Contents
- What is DeepSeek V4.1 Flash 1017
- Capabilities
- Examples of prompts and responses
- How to use it on Genosai
- Prompts for everyday work
- Generation cost
- Comparison with alternatives
- Limitations and tips
- FAQ
What is DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 is an update released by DeepSeek on October 17, 2026. In its official announcement, the developer describes improvements in coding and task completion over the previous version. Those are developer-reported results. Use representative tasks from your own workflow to decide whether the model meets your requirements.
On Genosai, this version is available in the browser-based text studio and through the API. You can use it to explain a function, draft a business email, organize requirements or summarize text that you provide. A useful starting point is a short request with source material and a clear description of the expected result.
The version name matters when you repeat an experiment. Genosai also lists other DeepSeek models, so select V4.1 Flash 1017 specifically. Keep your original prompt alongside the result: choosing the same model does not make different instructions equivalent. The Studio button on this page already includes the selection for this version.
Capabilities
For coding, start with a small task you can verify. Provide the function, its expected behavior and a failing input, then ask for a minimal fix and tests. Run the resulting code in your own environment. This makes it easier to spot an unnecessary dependency, a changed function signature or an assumption that does not match your application.
For business writing, specify the audience, length and facts to preserve. A customer email should include the relevant dates and next step, with explicit instructions about promises the model must not invent. For editing, you can request a list of proposed changes before asking for a revised draft. This keeps the scope of the work clear.
The context window supports up to 1 million tokens. Tokens are not the same as pages, words or characters; the relationship depends on the language and content. Source material and conversation history occupy space, and the response also needs room. A large window is useful when relevant material is extensive, but it is not a reason to send every document with every request.
Streaming displays the response as it is generated. You can begin reading an explanation or draft before the complete answer is ready. The time to the first fragment and the total completion time vary with the task and service load. Evaluate the experience on the kinds of requests you actually plan to send.
Examples of prompts and responses
The following responses were generated with version 1017 through Genosai on October 23, 2026. The examples cover a code correction with checks and a customer reply constrained by a short list of facts. They demonstrate a prompting approach rather than a guarantee that every task will produce the same quality or wording.
Fixing a Python function
Prompt:
Fix this Python function and add three short assert checks. Return the arithmetic mean of the numbers, or None for an empty list. Output code only.
def average(numbers):
return sum(numbers) / len(numbers)Model response:
def average(numbers):
if not numbers:
return None
return sum(numbers) / len(numbers)
assert average([]) is None
assert average([1, 2, 3]) == 2
assert average([2, 4]) == 3_Generated on Genosai.io with DeepSeek V4.1 Flash 1017._
The checks cover an ordinary list, a fractional mean and an empty input. Before using the function in an application, decide how it should handle other inputs, such as strings or missing values. Those decisions belong to your application contract; the example deliberately limits the task to the behavior stated in the prompt.
Writing a factual customer reply
Prompt:
Write a customer-service reply in English, 60–80 words. Facts: order #42 is delayed by two days; the new shipping date is October 26; a tracking number will follow after shipment. Apologize politely, state the date and next step. Do not invent a discount or compensation.Model response:
Thank you for contacting us regarding order #42. We sincerely apologize that your order has been delayed by two days. The new shipping date is October 26. We understand this is not the experience you expected. As soon as your order ships, we will send you a tracking number so you can monitor its delivery. We appreciate your patience and understanding. Sincerely, Customer Service Team_Generated on Genosai.io with DeepSeek V4.1 Flash 1017._
Review the shipping date before sending the message, and adapt the wording to your company's voice. A shipping date should not become a delivery promise. The same method works for a meeting update or a response about a changed schedule: provide the facts first, then ask the model to turn them into clear prose.
How to use it on Genosai
- Click Open in Studio near the top of this page. The link opens the text tab with DeepSeek V4.1 Flash 1017 selected.
- Sign in to your account and check your available balance. Confirm the selected model in the text-chat form.
- Paste the source text or code, followed by your task and constraints. Use explicit headings when working with several excerpts.
- Specify the output format, such as a list, table, email or code with tests. Set a length target when brevity matters.
- Send the message, review the result and follow up with a specific correction if necessary. Keep successful prompts for future tasks.
For an integration, use cr/deepseek-v4.1-flash-1017 as the model value in a request to /v1/chat/completions. Studio is a convenient place to refine a task before automating it. Store your API key on the server side of your integration rather than exposing it in browser code.
Prompts for everyday work
A useful prompt combines source material, an action and a way to check the result. Replace the bracketed text in the examples below with your own information. Start with one task, inspect the response and change one instruction at a time. This helps you understand which part of the prompt improved the answer.
Code review
Review this code: [code]. Required behavior: [contract].
List issues by importance. For each, provide a failing input,
a minimal fix and a check. Preserve the public API.Document summary
Summarize [text] for [audience]. Extract decisions, deadlines,
owners and open questions. Refer to the source section headings.
If a fact is missing, write "not specified" rather than guessing.Business email
Write an email to [recipient] about [situation]. Facts: [list].
Use a calm, specific tone and no more than 120 words.
End with one next step. Add no promises beyond the supplied facts.Development plan
Break [task] into stages for [team]. For each stage, specify
its deliverable, dependencies and acceptance criteria.
List missing requirements first. Do not invent time estimates.Editing a draft
Edit [text] for [audience]. Preserve the meaning, numbers
and names. Remove repetition and vague phrasing.
After the revision, list the three most significant changes.Generation cost
Genosai uses the following rates for DeepSeek V4.1 Flash 1017. These are prices per million tokens of each type, not a fixed price for a number of messages. The interface expresses the balance in credits, with 1 credit corresponding to RUB 1. The final charge depends on actual usage.
| Token type | Price per million |
|---|---|
| Regular input without a cache hit | RUB 10 |
| Model output | RUB 40 |
| Input read from cache | RUB 2 |
Each request has a minimum charge of RUB 0.10. For example, 100,000 regular input tokens and 10,000 output tokens cost RUB 1 + RUB 0.40 = RUB 1.40. If 80,000 of those input tokens are actually read from cache, the calculation becomes RUB 0.20 + RUB 0.16 + RUB 0.40 = RUB 0.76.
The minimum matters for short requests. A request with 1,000 regular input tokens and 1,000 output tokens has a calculated token cost of RUB 0.05, but the final charge is RUB 0.10. When estimating many short conversations, account for the number of separate requests as well as the token totals.
Cache pricing applies automatically to input tokens actually reported as cache reads. A repeated topic or similar wording does not guarantee a cache hit. Conversation history can increase the input for later messages, while longer responses increase output usage. See Pricing for available packs, starting-credit conditions and top-ups; check your own account for any credits already granted.
Comparison with alternatives
Compare models using the same tasks and acceptance criteria. Check whether they preserve facts, follow the requested format and produce code that passes your tests. Include the cost of retries when assessing a workflow. A family name or context-window size does not by itself establish that a model is the best fit for a particular job.
| Model page | What to check |
|---|---|
| DeepSeek V4.1 Flash 1017 | Whether you need this version and its RUB 10/40/2 rates |
| DeepSeek V4 Flash | The settings of the other DeepSeek catalog entry |
| DeepSeek V3.2 | Its output on familiar tasks from your workflow |
| GPT-5.4 Mini | An alternative approach to explanations and drafts |
A small evaluation set can include a function to fix, a customer email and a document excerpt to summarize. Run tests for the code, verify the email facts and check the summary against its source. Review the current rates on each model page before making a cost comparison, since configurations and prices can differ.
Limitations and tips
This Genosai integration accepts text. Attached files, images, web search and tool calls are not supported. Paste relevant code and document excerpts into the message. When a task depends on recent information, provide that information with its date and source instead of assuming the model can open a link or search for updates.
Check calculations, references and executable code before using the answer. Ask the model to separate supplied facts from assumptions and identify missing information. For a long document, ask a specific question and require references to the section headings you supplied. That makes the result easier to audit than an unrestricted request for general conclusions.
Start with a small part of your own task and one of the prompts above. Open DeepSeek V4.1 Flash 1017 using the button on this page, confirm the selected model and send your first message. Save useful instructions together with the checks you applied, so the next attempt has a clear standard to meet.
FAQ
What does 1017 mean in the model name?
It identifies the DeepSeek V4.1 Flash update released on October 17, 2026. Genosai lists it separately so you can select this particular version.
How much does it cost on Genosai?
The rates per million tokens are RUB 10 for regular input, RUB 40 for output and RUB 2 for cached input. Each request has a minimum charge of RUB 0.10; the final amount depends on token usage.
How do I select this model in Studio?
Click Open in Studio on this page. The link opens the text tab with DeepSeek V4.1 Flash 1017 selected; sign in to send a message.
Can I attach PDFs or images?
This Genosai integration accepts text and does not support attached files or images. Paste the relevant document excerpts as text and label each section.
When does the RUB 2 cached-input rate apply?
It applies to input tokens actually reported as cache reads by the service. Repeating a similar request does not guarantee a cache hit; other input tokens are billed at RUB 10 per million.
Which model ID should I use in the API?
Set model to cr/deepseek-v4.1-flash-1017 in a request to /v1/chat/completions. Both regular and streaming responses are supported, at the same RUB 10/40/2 rates per million tokens.