DeepSeek V4

DeepSeek V4 Has Launched: Open-Sourced on April 24, 2026 — Specs and Highlights Recap

DeepSeek V4 officially launched and went open source under the MIT license on April 24, 2026. A recap of the release: Pro/Flash editions, 1M-token context, CSA+HCA hybrid attention, benchmarks, and how to access it.

V4 Preview
DeepSeek Research Team2026-04-248 min read
#DeepSeek V4#Official Launch#Open Source#1M Context#Agentic Coding

DeepSeek V4 Has Launched: Open-Sourced on April 24, 2026 — Specs and Highlights Recap

The wait is over. DeepSeek V4 officially launched on April 24, 2026, fully open-sourced under the MIT license, with model weights published on Hugging Face. All the earlier speculation about V4's timing — TechNode reports, HuggingFace upload activity, and competitive pressure from GPT-5.4 and Claude 4.6 — has now been settled. This article recaps the core facts, specifications, and benchmark results of the release.

Release at a Glance

  • Release date: April 24, 2026 — DeepSeek-V4 preview officially launched.
  • License: MIT, with weights published on Hugging Face.
  • Two editions: DeepSeek-V4-Pro and DeepSeek-V4-Flash.
  • Core focus: Agentic coding + 1M-token context + extreme efficiency.
  • Legacy retirement: deepseek-chat and deepseek-reasoner will be retired on July 24, 2026.

Two Editions, Detailed Specs

DeepSeek shipped two editions at once, covering everything from high-end reasoning to low-cost, high-throughput workloads:

SpecDeepSeek-V4-ProDeepSeek-V4-Flash
Total parameters1.6 trillion (1.6T)284 billion (284B)
Active parameters49B13B
Context window1M tokens1M tokens
Max output~384K tokens~384K tokens
FocusHigh-end reasoning & agentic codingFaster, lower-cost workloads

Architecture Highlight: Hybrid Attention (CSA + HCA)

V4's real efficiency breakthrough lies in its MoE (Mixture of Experts) + hybrid attention architecture, which combines:

  • CSA (Compressed Sparse Attention)
  • HCA (Heavily Compressed Attention)

This design means that at a 1M-token context, per-token compute is roughly 27% of V3.2's, and KV Cache memory is about 10% of V3.2's. In other words, V4 turns ultra-long context from an "expensive luxury" into an affordable default — and that is precisely why V4 can offer million-token context at such low prices.

Benchmarks (V4-Pro, Measured)

V4-Pro delivers top-tier results among open-source models across major benchmarks:

BenchmarkScore
SWE-bench Verified80.6% (highest among open models, tied with Gemini 3.1 Pro)
LiveCodeBench Pass@193.5
Codeforces rating3206
MMLU-Pro87.5%
GPQA Diamond90.1%
GSM8K92.6%
Terminal-Bench 2.067.9%

The SWE-bench Verified score of 80.6% is the highest among current open-source models, putting V4 firmly in the top tier for agentic coding.

API Pricing

After a 75% price cut at launch, V4's long-term pricing is highly competitive:

EditionInput (per 1M tokens)Output (per 1M tokens)
V4-Pro$0.435$0.87
V4-Flash$0.14$0.28

Compared to closed-source frontier models, V4 is roughly 5–30x cheaper while being fully open source — a combination no closed-source competitor can match.

How to Use DeepSeek V4

V4 is live now. Here's how to access it:

Option 1: Web at chat.deepseek.com

Head to chat.deepseek.com to try it directly, with two modes:

  • Expert Mode: For complex reasoning and agentic coding tasks.
  • Instant Mode: For faster, lower-cost everyday queries.

Option 2: Official API

Sign up at platform.deepseek.com, create an API key, and call the model deepseek-v4-pro.

Note: the legacy models deepseek-chat and deepseek-reasoner will be retired on July 24, 2026, so plan your migration to V4 soon.

from openai import OpenAI client = OpenAI( api_key="YOUR_API_KEY", base_url="https://api.deepseek.com", ) response = client.chat.completions.create( model="deepseek-v4-pro", messages=[{"role": "user", "content": "Implement quicksort in Python"}], ) print(response.choices[0].message.content)

Option 3: Atlas Cloud and Local Deployment

  • Atlas Cloud: A DeepSeek-recommended partner offering hosted access at launch — atlascloud.ai.
  • Local deployment: V4 is open-sourced under MIT, with weights on Hugging Face. You can self-host via inference frameworks such as vLLM and TGI.

A Look Back at Market Expectations

Before V4's official launch, the industry pieced together its timing from several independent signals: TechNode's reporting on internal progress, dense upload activity on HuggingFace, and the competitive pressure created by the back-to-back launches of GPT-5.4, Claude 4.6, and Gemini 3.1 Pro — all pointing to an imminent next-generation model from DeepSeek. Those expectations have now been met: V4 is officially released, answering the market with efficiency and open-source commitment that exceeded what many predicted.

Conclusion

DeepSeek V4's launch brings together five defining traits: agentic coding ability (SWE-bench 80.6%), a 1M-token context window, CSA+HCA extreme efficiency, full open source (MIT), and very low pricing. For developers, now is the best time to get hands-on: try it at chat.deepseek.com, or plug the API into your workflow.


Sources

The following is a summary of DeepSeek's official release (2026-04-24):

Some third-party benchmark figures may change as evaluations are updated.

Last updated: April 24, 2026

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