Everything We Know About the Upcoming DeepSeek V4

A quick look at current DeepSeek models

Let's take a look at all the DeepSeek models first, so you have an idea of what the company has actually been creating:

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1. DeepSeek-V2 (general LLM)

This model came out in May 2024 and has 236 billion total parameters, but only 21 billion are active when processing each token. It's built using Mixture of Experts, which basically means the model picks specific parts of itself to handle different tasks instead of using everything at once. This makes it way more efficient.

DeepSeek-V2 can handle context up to 128,000 tokens. It's good at general language tasks and coding. The best thing is that it costs about 42.5% less to train than earlier models and uses 93.3% less memory during use.

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1. DeepSeek-V3 (large language model with strong reasoning)

This V3 model is a big upgrade. DeepSeek-V3 has 671 billion total parameters with 37 billion active per token. It came out in December 2024 and honestly kind of shocked everyone.

Performance-wise, it's competing with closed models like GPT-4. It's really strong at math and coding tasks. The model is open source under the MIT license, meaning anyone can use it commercially or modify it.

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1. DeepSeek-V3.1 and V3.2 (updates with improved context handling and performance)

V3.1 was kind of a stepping stone. Not tons of public info on it, but it was basically improving on V3.

V3.2 is the current flagship. Released in late 2025. It introduces something called DeepSeek Sparse Attention (DSA), which cuts down computational costs while keeping quality high, especially for long contexts.

Through reinforcement learning improvements, V3.2 performs at a level comparable to GPT-5. They actually have two versions. The regular V3.2 is balanced and efficient. Then there's V3.2-Speciale, which maxes out reasoning and rivals Gemini 3.0 Pro. The Special version actually got a gold medal level performance in the 2025 International Math Olympiad and other competitions.

V3.2 is their first model to integrate reasoning directly into tool use. So it can think step by step while using external tools. Pretty cool for building AI agents.

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1. DeepSeek-R1 (reasoning-focused model)

R1 is all about reasoning. It uses pure reinforcement learning without supervised fine-tuning at first, which lets the model discover its own reasoning patterns through trial and error. This is different from how most models are trained.

The model shows self-verification, reflection, and generates long chains of thought. When it solves problems, you can actually see its thinking process. It breaks things down step by step.

Performance is strong. It gets around 79.8% on the American Invitational Mathematics Examination and 97.3% on MATH-500. For coding, it reaches a 2,029 Elo rating on programming challenges. It's competing with OpenAI's o1 model.

The really interesting part is cost. Running DeepSeek R1 costs about $8 per million tokens, while OpenAI's o1 costs $15 per million input tokens and $60 per million output tokens. So it's way cheaper.

Like the other V3 models, R1 is built on top of the DeepSeek-V3-Base and supports commercial use under the MIT license.

News about the upcoming coding AI model: DeepSeek V4

DeepSeek V4 expected release date

DeepSeek is aiming for a mid-February 2026 release for V4, probably around February 17th, which lines up with the Lunar New Year. That's the same timing strategy they used with their R1 model. This hasn't been officially confirmed by DeepSeek yet, but reports from people who know about the project point to this timeframe.

The company's been pretty quiet about it publicly, but there's been a lot of buzz from developers tracking updates on GitHub and research papers. Analysis of their FlashMLA codebase shows a new model identifier called "MODEL1" appearing 28 times across their files, which people think is probably V4. So basically, expect it sometime around mid-February, maybe in the next few weeks, but nothing's totally set in stone yet.

Architecture innovations of DeepSeek V4

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1. DeepSeek mHC (Manifold-Constrained Hyper-Connections)

DeepSeek mHC is a new architecture and training method to make large neural networks, for instance, large language models, easier and more stable to train. It's DeepSeek's key connection to constrain those learned connection matrices to a minfold of doubly stochastic matrices that rows and columns each sum to 1. This keeps training stable and well-behaved with hyper-connections by preventing gradients and signal magnitudes from exploding as networks get deeper.

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1. Engram memory architecture for faster recall

A core new part of DeepSeek V4 is Engram, a memory system that stores patterns and facts in a way that can be looked up quickly. The model can fetch stored data using fast lookups. This lets it remember long sequences better and keeps reasoning consistent over long tasks. It also frees up the model to focus on new information instead of recalling old facts.

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1. Advanced attention and precision techniques

The model adds improvements in how it pays attention to important parts of the input. New attention methods like sparse attention let the model handle long sequences without slowing down too much. Precision techniques such as mixed numeric formats make calculations more accurate while using less memory. These changes let V4 reason more clearly about complex problems like long code logic or layered documents.

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1. Mixture-of-experts

DeepSeek V4 continues using a mixture‑of‑experts (MoE) structure. In this design, the model has many small expert modules and only activates the most relevant ones for each task. This lets the system scale up without making every part active all the time. With MoE, V4 stays efficient even as it grows in size and capability. Combined with Engram, this structure lets the model balance memory and computation in a powerful way.

Key capabilities of DeepSeek V4 over previous models

DeepSeek model 1 will be a big jump from older versions. The main upgrades expected in this new model include:

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1. Strong focus on coding

DeepSeek V4 is built first and foremost for software engineering work. Internal tests reportedly show V4 beating both Claude and GPT models in long-context code generation. It will handle very long code understanding, debugging, and refactoring across languages and systems. The model should help with tasks like tracking bugs, writing tests, and explaining complex code. This focus makes it more useful for developers compared to general‑purpose AI models.

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1. Support for long context

V4 is designed to read and work with very large amounts of text or code in one go. Most AI models run out of memory after a few hundred thousand tokens. DeepSeek V4 is planned to support contexts well over 1 million tokens, which lets it handle entire codebases, long documents, or big data sets without splitting them into smaller chunks. This improves continuity and stops the model from forgetting earlier information.

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1. Improved computational efficiency

Behind the scenes, V4 uses smart engineering changes to reduce how much computation it needs. For example, it uses sparse attention methods that focus on computing power where it matters most instead of on every possible interaction. This means the model can run long contexts with less memory and energy.

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1. Multi-file reasoning

One big upgrade is the model's ability to understand how many files relate to each other. Instead of just reading one file at a time, V4 is expected to track imports, functions, definitions, and references across a whole project. This lets it analyze dependencies, spot errors that span multiple files, and offer refactoring suggestions that work on the whole system.

A short review of Pippit: visualize your DeepSeek prompts

Pippit is an AI tool that offers a video generator and an AI design tool to create images and videos. So, when you use DeepSeek to write a detailed prompt, idea, or script, you can bring that text into Pippit to make visuals or videos. Pippit's advanced text-to-image or video model reads the prompt and generates social clips, product showcases, or story visuals that reflect what prompts you created in DeepSeek.

Why choose Pippit to visualize your DeepSeek prompts

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1. Multimodel video generation response to DeepSeek Prompts

Pippit lets you input prompts generated by DeepSeek with a product page link, PowerPoint, or local footage, and instantly turn it into product highlights, viral TikToks, engaging reels, or funny meme videos. The platform uses different AI models depending on what you need. You can use Veo 3.1, Sora 2, Agent mode, or Lite mode, select any duration, and set the video language.

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1. Visualize AI image prompts from DeepSeek for any use

Pippit uses the latest models, Nano Banana Pro and Seedream 4.5, to generate high-quality, customizable visuals from simple text prompts generated by large language models like DeepSeek. The AI design tool understands language really well and captures your intent with sharp details, balanced lighting, and well-defined textures. You can upload reference images, adjust aspect ratios, and customize everything from colors to specific elements you want included.

How to turn DeepSeek prompts into videos in Pippit

You can click the link below and then follow these three steps to create social media clips, ads, and more with Pippit using the prompts you generate with DeepSeek:

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1. Open the Video generator

• First, you need to sign up for a Pippit account. You can use your Google, TikTok, or Facebook login, whichever is easiest for you.
• Once you're in, click "Video generator" from the left navigation panel.
• Now you'll see a text field where you enter your prompt generated by DeekSeek.

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1. Generate your video from DeepSeek prompts

• Hit "Add media and more" if you want to upload photos, video clips, or any reference material.
• Click "Choose a model" to select your model based on what kind of video you need. Lite mode works for quick marketing videos, Agent mode is good for creative stuff, Veo 3.1 handles realistic videos pretty well, and Sora 2 is for more polished content.
• If you're using agent mode, you can click "Upload reference video" to show the AI a style you want to recreate.
• Open "Customize video settings" to adjust the length. Set it to whatever makes sense for your project, anywhere from 15 seconds to a few minutes.
• You can also pick your language preference here if you want voiceovers or captions.
• Once everything looks right, click "Generate" and let Pippit create your video. It'll add animations, transitions, and effects based on what you described in your prompt.

Try the prompt from DeepSeek: Generate a high-quality video of a small dog dancing in a modern living room. Scene is a cozy home interior with large windows and soft morning sunlight casting natural shadows on the wooden floor. The dog stands on its hind legs and performs playful side-to-side hops, spinning in a small circle and pawing the air rhythmically. Camera uses a slow handheld-style pan from left to right with slight natural motion. Warm color grading, realistic movement, joyful mood.

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1. Export and share

• Select "Edit more" to open the inside video editor to enhance your video content further.
• Click "Download" to save the video to your computer.
• If you want to share it right away, click "Publish" to auto schedule and post your video on social media platforms like TikTok, Facebook, and Instagram.

How to turn DeepSeek prompts into images in Pippit

Follow these steps to turn AI image prompts generated by DeepSeek for posters, flyers, wallpapers, social media posts, or artwork creation.

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1. Open the AI design tool

• Click "Image studio" under "Creation."
• Click "AI design" under "Level up marketing images."
• Tell DeepSeek what kind of image you want, and then copy that prompt and use it in Pippit.

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1. Turn DeepSeek prompts to images

• Click the "+" button if you want to upload reference pictures. Maybe you have examples of the style you're going for, or specific elements you want the AI to use as inspiration. This step is optional but helpful.
• To choose between the Seedream or Nano Banana model, click "Model." Each one has different strengths. If you're not sure which to use, just leave it on Auto and let Pippit decide.
• Pick the "aspect ratio" you need. Square for social media posts, landscape for websites, portrait for phone screens, whatever fits your project.
• Click "Generate" and wait a bit while Pippit's AI creates your image based on everything you described.

Try the prompt from DeepSeek: A dog running across an open park space, ears lifted mid-motion, mouth open slightly, paws blurred from speed. The background stretches into streaks of green and brown as the camera struggles to keep focus. Sunlight flashes across the body in broken patches. Shot handheld, fast shutter but imperfect tracking, visible noise in shadowed areas, motion blur left intact to preserve realism.

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1. Export your image

• Check the generated result from the DeepSeek prompt. You can adjust your prompt details to generate more images in different styles.
• Leverage the inside tools to fine-tune your image content based on your needs.
• Click "Download" to save your image to your local device.

Conclusion

So let's recap what we covered here. We looked at the DeepSeek's current lineup and explored what's coming with V4, which is shaping up to be a serious upgrade with its million-token context window, Engram memory architecture, and laser focus on coding tasks. These improvements make it more capable of handling complex projects and large datasets. This clearly shows that DeepSeek has come a long way in just a couple of years. It's now a go-to tool for serious coding, research, and problem-solving tasks.

FAQs

What is special about the DeepSeek v3 model?

DeepSeek V3 model stands out for its ability to handle very large inputs, with a context window of up to 128K tokens, which lets it read and reason over long documents or codebases. It uses a Mixture-of-Experts (MoE) design, which keeps it fast and efficient by only activating parts of the model as needed. V3 also has hierarchical memory to recall important info, a truth-anchoring system to reduce errors, and advanced training techniques that improve text quality and performance.

What models are included in the DeepSeek models list?

DeepSeek model lineup includes V2 for improved context and reasoning, V3 with a 128K-token window and MoE, and V3.1 for deeper reasoning. There is also V3.2- Speciale model, which works well for reasoning tasks and competition-level problem solving. The R1 series focuses on step-by-step logical reasoning.

How does DeepSeek handle long context tasks?

DeepSeek AI model handles long context through sparse attention mechanisms that select only the most relevant tokens instead of comparing everything to everything else. This makes long-context processing much faster, while giving you high-quality output. V3.2 and the upcoming V4 can process over a million tokens, which means they can work with entire codebases or massive documents in one go.

Will DeepSeek V4 be open source?

As of now, DeepSeek has not officially confirmed whether V4 will be fully open source. However, DeepSeek has a clear pattern of making its models and weights available to the public. So based on their track record with V2, V3, and R1, it's pretty likely V4 will follow the same pattern when it drops in mid-February.

Can DeepSeek models be used locally?

Yes, you can run DeepSeek models locally. V3 and V3.1 have open weights that you can download and run on your own CPU or GPU. The smaller distilled versions of R1, like the 7B, 14B, and 32B parameter models, work on regular consumer hardware. V4 is expected to run on dual RTX 409s or a single RTX 5090 thanks to its Mixture-of-Experts design.