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

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.

Additional instructions are in the Ailoot documentation.

Which DeepSeek models are available now: the short answer#

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

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.

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.

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.

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. If you're looking into free options, see the selection of free models for coding.

Which model should you choose for your task?#

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.

When you're ready, take a look at Ailoot.