# New DeepSeek Models for Coding in 2026: Which One to Choose

> Detailed overview: New DeepSeek models for coding in 2026: which one to choose.

Source: https://ailoot.ai/blog/en/deepseek-coding-models/
Canonical: https://ailoot.ai/blog/en/deepseek-coding-models/
Language: en
Publisher: Ailoot (https://ailoot.ai/)
Translation: guide-158
Revision: sha256-441c9f86cf57171f920df090eae15029f72e24124162ab697c0c1fb14bb007cf


For most coding tasks, start with `deepseek-flash`. This name refers to V4.1-Flash, released on September 10, 2026. Keep `deepseek-v4-pro` for comparison on your own tasks. The names can be confusing: older recommendations also mention `deepseek-v4-flash`, but that name has been retired and only temporarily routes to the new model.

This article covers the differences between the models in price and use cases, along with a general outline for connecting DeepSeek to your tool. The names, prices, and rules in the source material are based on DeepSeek's official pages as of September 16, 2026. They change, so check the current terms on the website before paying.

<div class="aside" data-semantic="callout" role="note" aria-label="Note">

Note

This material is for reference and is based on public DeepSeek data. Country-specific terms for paying for the API and ways to lower the bill are separate topics. This article is only about the models themselves and how to choose one; a link to a payment guide is at the end.

</div>



Additional instructions are in the [Ailoot documentation](https://ailoot.ai/docs/).

## Which DeepSeek models are available now: the short answer

<div class="aside" data-semantic="key-takeaway" role="note" aria-label="Key takeaway">

Key takeaway

There are currently two active models in the DeepSeek API. The first is `deepseek-flash`, which refers to the new DeepSeek-V4.1-Flash from September 10, 2026. The second is `deepseek-v4-pro` (full name DeepSeek-V4-Pro-0813). For coding, choose `deepseek-flash` in most cases: it is newer, significantly cheaper, and, according to DeepSeek itself, faster than the previous Pro. Keep `deepseek-v4-pro` as a fallback if it performs better on a specific task. The old names `deepseek-v4-flash` and `deepseek-v4-flash-vision-exp` have been retired: requests using them are temporarily redirected to V4.1-Flash.

</div>

The table shows the active and retired names for the `model` field.

| Name for the `model` field | Which model it refers to | Use for coding? |
|----|----|----|
| `deepseek-flash` | DeepSeek-V4.1-Flash, the new model from 2026-09-10 | Yes, the default option |
| `deepseek-v4-pro` | DeepSeek-V4-Pro-0813, the previous flagship model | Only as a fallback if Flash doesn't work for you |
| `deepseek-v4-flash` | Retired name, temporarily routes to V4.1-Flash | No, use `deepseek-flash` |
| `deepseek-v4-flash-vision-exp` | Retired name, temporarily routes to V4.1-Flash | No, use `deepseek-flash` |

## What changed in September 2026

On September 10, 2026, DeepSeek released DeepSeek-V4.1-Flash and updated the model names. The changes affected both the old names and plans for serving Pro.

1. A new model, V4.1-Flash, was released. According to DeepSeek's description, it is smarter and faster than the previous generation and understands images. DeepSeek also notes separately that V4.1-Flash has a smaller model cache than the previous generation, so cache requests cost less. The impact on your bill is explained below.
2. The name `deepseek-flash` now points specifically to V4.1-Flash.
3. The previous names `deepseek-v4-flash` and `deepseek-v4-flash-vision-exp` have been retired. For compatibility with older projects, these names currently route to V4.1-Flash, and requests are charged at its price. But don't rely on them: the redirection is temporary, so it's better to use `deepseek-flash` right away.
4. DeepSeek first announced that it was phasing out V4 Pro: starting at 04:00 UTC on September 14, 2026, all requests to `deepseek-v4-pro` were supposed to go to V4.1-Flash. Then the plans changed. According to the pricing page as of the date of checking, DeepSeek decided to keep serving V4 Pro after September 14, with the same pricing as before.

For a new setup, use `deepseek-flash`: as of the date of checking, this name points to V4.1-Flash. `deepseek-v4-pro` is still working, but its serving plans have already changed, so Flash remains the primary recommendation.

<div class="aside" data-semantic="callout" role="note" aria-label="Attention">

Attention

According to DeepSeek, independent tests by several parties rank V4.1-Flash ahead of V4-Pro in performance, price, and speed. This is the company's own statement; this article contains no independent measurements. Test it on your own task: run the same prompt on both models and compare the results and time.

</div>

## How does `deepseek-flash` differ from `deepseek-v4-pro` in price?

`deepseek-flash` costs less than Pro. DeepSeek calculates the cost per 1 million tokens separately for input and output, and also accounts for cache hits on input. Tokens are fragments of text used to measure the size of your request and the model's response. The table shows prices as of September 16, 2026, in dollars per 1M tokens.

| What we're measuring                  | `deepseek-flash` | `deepseek-v4-pro` |
|---------------------------------------|------------------|-------------------|
| Input, without cache (off-peak)       | \$0.15           | \$0.66            |
| Input, cache hit (off-peak)           | \$0.003          | \$0.022           |
| Output (off-peak)                     | \$0.60           | \$1.98            |

Prices are exactly twice as high during peak hours. `deepseek-flash` is about three to four times cheaper than the flagship model across most rows. For coding, where long conversations can add up to millions of tokens, the difference in your bill is significant.

The longer the conversation with an agent, the more tokens it uses. So when choosing a model, consider the price per million: it determines your monthly bill.

<div class="aside" data-semantic="callout" role="note" aria-label="Note">

Note

API prices are not the same as a DeepSeek chat subscription. This is about paying per token for requests to the model from your tool or code. Country-specific terms for paying for the API are covered in a separate article; see the link at the end.

</div>





## How context caching and peak hours affect your bill

DeepSeek's pricing has separate rates for input with a cache hit and for use outside peak hours. Consider these terms when making repeat requests and scheduling non-urgent tasks.

**Context cache.** By default, DeepSeek stores the beginnings of your requests on disk. If the next request starts the same way as the previous one, the model takes the matching part from the cache, which costs several times less. For `deepseek-flash` off-peak, input with a cache hit costs \$0.003 instead of \$0.15 per 1M tokens without a cache hit. When working with code, the system instruction and project context at the beginning of a request often repeat, which lets you use the cache. You don't need to enable anything; it's on for everyone by default.

**Peak hours.** DeepSeek has two rates: one during peak hours and one that's half the price outside them. Peak hours are 01:00–04:00 and 06:00–10:00 UTC, Monday through Friday. All other times, including weekends, are considered off-peak and cost half as much. If you have non-urgent tasks—large runs or batch code processing—run them outside the specified UTC peak intervals at half the price.

## Context and maximum response length

Both models support context of up to 1 million tokens. You can send many files and long conversations in it. Whether that's enough for a large coding task depends on the size of your files, so check with your own project.

The maximum response length is up to 384 thousand tokens. By default, the limit is lower: 8 thousand tokens without reasoning mode, 64 thousand in reasoning mode, and 128 thousand if you raise reasoning effort to `max`. If you expect a long response—for example, a large code file in full—explicitly set a higher `max_tokens` value; otherwise, the response may be cut off halfway through.

## Do you need reasoning mode for coding?

DeepSeek has a reasoning mode: before responding, the model first thinks through the solution. According to DeepSeek's description, this is designed to improve response accuracy on difficult tasks. The mode is on by default, with effort set to `high`.

It is controlled by the `reasoning_effort` parameter, which according to DeepSeek documentation accepts the following values:

- `none` - turn reasoning off. Suitable for simple and fast requests that don't require a complex solution.
- `low` - minimum reasoning level.
- `high` - the default value.
- `max` - maximum reasoning level for the most difficult tasks; the response may be longer.

Check for yourself how this affects price and quality on your own tasks: more reasoning usually means a longer response and more output tokens. For everyday coding, it's reasonable to start with `high` (the default) and see whether the result works for you. In reasoning mode, parameters such as `temperature` are ignored, and `top_p` does not go below 0.95.

## How do you connect DeepSeek to your tool?

The DeepSeek API is compatible with OpenAI and Anthropic formats. To connect through a compatible tool, you'll need the API base URL, an access key, and the model name. The base URLs are:

```
# OpenAI format
https://api.deepseek.com

# Anthropic format
https://api.deepseek.com/anthropic
```

According to DeepSeek, its model can be connected as an engine in Claude Code, GitHub Copilot, and OpenCode without writing code. The exact field names differ between tools, but the general process is:

1. Get an access key from your DeepSeek account.
2. In the tool settings, enter the DeepSeek base URL and your key. For tools that use the Anthropic format (for example, Claude Code), enter `https://api.deepseek.com/anthropic`; for those that use the OpenAI format, enter `https://api.deepseek.com`.
3. Enter `deepseek-flash` in the model field.
4. Ask a short question and check the tool's usage information for the model actually used: the request should be served by `deepseek-flash`, not the previous engine. The name in the settings alone is not enough; the model naming itself in a generated response also does not confirm that the switch worked. Check your tool's documentation to find where to view information about the model used. If it doesn't show this information, you can't confirm the switch from a response alone.

This article doesn't provide step-by-step setup for a specific tool: each has its own screens, and they change. Before connecting, check whether your tool supports an external model URL (not all tools let you change it) and which format it uses—OpenAI or Anthropic. We have a separate guide to connecting a model with OpenCode: [OpenCode: what it is and how to connect a model](https://ailoot.ai/blog/en/opencode-installation-models/). If you're looking into free options, see the [selection of free models for coding](https://ailoot.ai/blog/en/free-coding-models/).

<div class="aside" data-semantic="callout" role="note" aria-label="Note">

Note

Your account has a limit on simultaneous requests: it's higher for `deepseek-flash` and lower for `deepseek-v4-pro`. It's hard to reach while working alone; the limit matters when many users are working through one account at the same time. If you exceed it, the API returns a 429 error: in that case, reduce the number of simultaneous requests; the error itself doesn't mean something is broken.

</div>

## Which model should you choose for your task?

| Your situation | What to use | Why |
|----|----|----|
| Everyday coding, an agent, vibe coding | `deepseek-flash` | Newer, cheaper, and according to DeepSeek, outperforms the flagship Pro |
| You need the highest quality on a difficult task | Compare for yourself: `deepseek-flash` with `reasoning_effort` `max` against `deepseek-v4-pro` | The choice depends on the result for your task |
| An old project using `deepseek-v4-flash` | Change the name to `deepseek-flash` | The old name has been retired and only temporarily routes to the new model |
| Large non-urgent runs | `deepseek-flash` outside peak hours | Half the price outside the specified UTC peak hours |

Start with `deepseek-flash`, leave the reasoning mode at its default `high`, and try `deepseek-v4-pro` when you have a reason to compare. When choosing, evaluate how the model handles your tasks and how much its work costs.

## What next

Country-specific terms for paying for the API and further ways to reduce token costs are beyond the scope of choosing a model. Below is a link to a payment guide, official documentation with pricing and caching rules, and related articles about tools.

<div class="section" role="doc-bibliography" aria-label="Sources">

### Sources

- [DeepSeek API - models and pricing](https://api-docs.deepseek.com/quick_start/pricing/)
- [DeepSeek - V4.1-Flash release, 2026-09-10](https://api-docs.deepseek.com/news/news260910)
- [DeepSeek API - quick start and model list](https://api-docs.deepseek.com/)
- [DeepSeek API - reasoning mode](https://api-docs.deepseek.com/guides/thinking_mode)
- [DeepSeek API - context caching](https://api-docs.deepseek.com/guides/kv_cache)
- [DeepSeek API - rate limits](https://api-docs.deepseek.com/quick_start/rate_limit)
- [DeepSeek API - chat parameters](https://api-docs.deepseek.com/api/create-chat-completion)
- [Free models for coding in 2026](https://ailoot.ai/blog/en/free-coding-models/)
- [OpenCode: what it is and how to connect a model](https://ailoot.ai/blog/en/opencode-installation-models/)

</div>







When you're ready, take a look at [Ailoot](https://ailoot.ai/).

## Questions about New DeepSeek Models for Coding in 2026: Which One to Choose

### Should I choose the model with the larger context window automatically?

No. Compare the supported response length and the requirements of your actual coding task as well as context size. Start with the article’s cheaper Flash option and keep Pro only if it produces a better verified result on your task.

### Does the cache-read price apply to every repeated project read?

No. Compare actual cache usage rather than assuming that a repeated read is a hit. Include uncached input, output and any applicable peak-hour terms when estimating the task bill.


## Ailoot · The company and its project portfolio

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