DeepSeek V4 Is Out: Everything You Need to Know (April 2026)

On April 24, 2026, DeepSeek released its long-awaited V4 model family — and it landed like a freight train. The release hit #1 on Hacker News within hours, the Reddit AI communities went into immediate benchmark mode, and anyone paying attention to open-source AI has something seriously interesting to dig into.

Here's everything that matters: what it is, how it compares to the competition, how to use it today, and whether it's worth your attention as a beginner or creator.

What Is DeepSeek V4?

DeepSeek V4 is the newest flagship AI model family from DeepSeek, the Chinese AI lab that shook the industry in early 2025 with its low-cost R1 reasoning model. V4 is a Mixture-of-Experts (MoE) architecture — meaning it has a massive number of parameters but only activates a small portion of them for any given query, keeping inference fast and efficient.

This release comes in two models:

Model	Total Parameters	Active Per Token	Context Window	Training Tokens
DeepSeek-V4-Flash	284 billion	13 billion	1 million	32 trillion
DeepSeek-V4-Pro	1.6 trillion	49 billion	1 million	33 trillion

Both are released today under the MIT license — meaning fully open source, free to download, modify, and use commercially.

Both support 1 million token context natively — that's roughly 750,000 words, enough to fit multiple books or a full codebase in a single conversation window.

What's Actually New in V4

DeepSeek didn't just scale up V3. The V4 architecture is a significant rethink centered on one goal: making million-token context practical without the astronomical compute costs that usually come with it.

Hybrid attention (CSA + HCA): Standard transformer attention scales quadratically with context length — doubling the context multiplies the compute by 4x. V4 attacks this with two complementary attention types:

• Compressed Sparse Attention (CSA) compresses past tokens and retrieves only the most relevant ones for each query. V4-Pro selects the top 1,024 compressed entries; V4-Flash selects 512.
• Heavily Compressed Attention (HCA) gives a cheaper broad view of the full context. These two types alternate through the network.

The practical result: V4-Pro at 1M context uses only 27% of the inference compute and 10% of the KV cache compared to V3.2 at the same length. V4-Flash pushes that to 10% FLOPs and 7% KV cache.

Muon optimizer: DeepSeek switched from AdamW to the Muon optimizer for most of training, reporting faster convergence and more stable training at trillion-parameter scale.

FP4 expert weights: Routed expert parameters are stored in FP4 precision — half the memory of FP8 — unlocking further efficiency on modern hardware.

Thinking and Non-Thinking modes: Both V4 models can operate as reasoning models (like o3 or Qwen3) or as direct-answer models. You choose per request.

Benchmark Results

DeepSeek is calling V4 a preview, so the full benchmark suite isn't out yet. What we know so far:

• Codeforces: V4-Pro reaches a 3,206 rating — ranking 23rd among human competitive programmers globally
• Overall intelligence: Sits between GPT-5.2 and GPT-5.4 on reasoning and agentic benchmarks
• V4-Pro-Max (the API version with extended capabilities) was compared favorably against Claude Opus 4.6, GPT-5.4, and Gemini 3.1 Pro in DeepSeek's internal evals

For a fully open-source, MIT-licensed model, competing with frontier closed-source models in the GPT-5 range is a significant statement.

How to Use DeepSeek V4 Right Now

Option 1: chat.deepseek.com (free) The easiest way to try it. Go to chat.deepseek.com, select V4 from the model picker, and start chatting. No setup required. You can toggle between Thinking and Non-Thinking mode directly in the interface.

Option 2: DeepSeek API If you're a developer, the API is live today at api-docs.deepseek.com. It accepts both OpenAI and Anthropic API formats — so if you already have code written for ChatGPT or Claude, switching over is often just a URL change and model name swap.

Option 3: Hugging Face (open weights) Both models are available for download at the DeepSeek Hugging Face collection. This is the open-source route — you download the weights and run them yourself.

Can You Run DeepSeek V4 Locally?

This depends heavily on which model and what hardware you have.

V4-Flash (284B total, 13B active): The Flash model is the practical local inference target. With only 13 billion parameters active per token:

• BF16 (full precision): ~26 GB VRAM — needs an RTX 4090 (24 GB) plus some system RAM offload, or 2x GPUs
• Q8 quantized: ~13 GB VRAM — single RTX 4090 is sufficient
• Q4 quantized: ~7 GB VRAM — even mid-range GPUs like an RTX 4070 can handle this

If you're new to running models locally, this is where you'd start. Check out our guide to how much VRAM you need for AI models before buying hardware.

V4-Pro (1.6T total, 49B active): The Pro model requires serious hardware for local inference:

• Q4 quantized: ~25 GB VRAM — 2x RTX 4090 or better
• Full precision: Multi-node GPU clusters. Not a home setup.

For most people, V4-Flash locally or V4-Pro via the API is the right approach.

If you're not set up with Python or CUDA yet, start with our terminal beginners guide — you'll need those basics before running models locally.

DeepSeek V4 vs GPT-5.5

Released just yesterday, GPT-5.5 is the other big story this week. Here's how they compare on the factors that matter:

	DeepSeek V4-Pro	GPT-5.5
Open source	Yes (MIT)	No
Context window	1 million tokens	128K tokens
Run locally	Yes (Flash)	No
API cost	DeepSeek API pricing	$5 input / $30 output per 1M tokens
Benchmarks	Between GPT-5.2 and 5.4	Terminal-Bench 82.7%, SWE-Bench 58.6%
Thinking mode	Yes	No
Best for	Coding, long-context tasks	General use, tool-calling

The short version: GPT-5.5 leads on general intelligence benchmarks. DeepSeek V4-Pro competes directly on coding and agentic tasks while being fully open, runnable locally, and much cheaper per token via API.

For someone who just wants the best general AI assistant: GPT-5.5 or Claude Opus 4.7. For developers who want open weights, long context, or cost control: DeepSeek V4 is compelling.

What This Means for the AI Market

DeepSeek has done this before — released an open-source model that punches at or near closed-source frontier quality. V4 continues that pattern, and a few things stand out:

The 1M context game is now open. Until today, practical million-token context was locked behind Gemini or Claude pricing. MIT-licensed open weights with native 1M context is a new category.

MoE efficiency keeps improving. V4-Flash has 13B active parameters — comparable to Llama 3.1 13B for inference cost — but with access to 284B total parameters of "knowledge." The quality-per-FLOP ratio keeps moving up.

V3 and V3.2 are retiring July 24, 2026. If you're using DeepSeek V3 in production, update your code to V4.

Frequently Asked Questions

Is DeepSeek V4 free to use? Yes — both the model weights (via Hugging Face) and the web chat (chat.deepseek.com) are free. API access is paid at DeepSeek's standard pricing.

Is DeepSeek V4 safe to use? Like all AI models, DeepSeek V4 can make mistakes. The model is produced by a Chinese company — if data sovereignty matters for your use case, run it locally on your own hardware using the open weights.

How does DeepSeek V4 compare to Llama 4? DeepSeek V4-Pro sits above Llama 4 Scout/Maverick on most coding and reasoning benchmarks. V4-Flash is broadly comparable to Llama 4 Maverick while offering native 1M context.

Can I use DeepSeek V4 commercially? Yes. Both V4 models are MIT licensed — you can use them in commercial products, modify the weights, and redistribute.

When will the full (non-preview) version be released? DeepSeek labels this release a preview. No firm date for the full release, but preview models from DeepSeek have historically become stable within 4-8 weeks.

Does DeepSeek V4 support tool use and function calling? Yes — both V4 models support function calling and agentic use cases via the API.

Bottom Line

DeepSeek V4 is the best open-source AI model available today, and it's competitive with frontier closed-source models on coding and long-context tasks. The MIT license, 1M token context, and Flash model's accessibility for local inference make this a release that matters regardless of what hardware you're running.

If you're a beginner: try it free at chat.deepseek.com today.

If you're a developer: the API is live, and it's already OpenAI and Anthropic API compatible. Getting started is fast.