DeepSeek V4 Officially Released: Dual Versions with Million-Token Context

DeepSeek-v4-has-finally-arrived">The Long-Awaited DeepSeek V4 Has Finally Arrived

Just moments ago, the highly anticipated DeepSeek V4 preview was officially unveiled. This release includes two versions — V4-Pro and V4-Flash — both equipped with 1M (million-token) ultra-long context as standard. Model weights and technical reports have been open-sourced simultaneously, continuing DeepSeek's longstanding commitment to open source.

On the eve of China's May Day holiday, the large model arena has once again entered a period of rapid-fire releases, and DeepSeek's update is undoubtedly one of the most closely watched announcements.

Dual-Version Strategy: Pro as the Flagship, Flash for Speed

DeepSeek V4 continues the company's established product tiering logic, using a dual-version strategy to address different use-case demands:

• V4-Pro: Positioned as the flagship reasoning model for complex task scenarios, ideal for deep analysis, long-document comprehension, multi-step reasoning, and other demanding applications.
• V4-Flash: Focused on cost-effectiveness and response speed, suited for everyday conversations, lightweight tasks, and high-concurrency API calls — the go-to choice for developers seeking rapid integration.

Both versions support million-token context windows, meaning users can input hundreds of thousands of words of long documents, codebases, or even entire books in a single session, and the model can fully comprehend and process them. Million-token context has officially shifted from a "differentiating selling point" to an "industry standard" — a trend that has become abundantly clear in this wave of releases.

Three Giants Launch Simultaneously, Diverging Paths Become Clearer

Notably, the release of DeepSeek V4 is not an isolated event. Over the past 48 hours, the large model arena has witnessed a "three kingdoms" showdown:

On April 23, Tencent's Hunyuan Hy3 preview debuted. This was the first model delivered by "prodigy" Shunyu Yao after joining Tencent — a 295-billion-parameter MoE architecture with 21B activated parameters, a 40% improvement in inference efficiency, and input pricing slashed to 1.2 RMB per million tokens. Tencent's strategy is clear: leverage extreme cost-effectiveness to drive large-scale commercialization within its own ecosystem.

In the early hours of today, OpenAI rolled out GPT-5.5 for paid users and announced API plans, emphasizing Agent workflows and multi-step task completion, with the context window also extended to 1 million tokens. However, API pricing has risen accordingly — $5 for input and $30 for output per million tokens — continuing its premium closed-source approach.

DeepSeek, meanwhile, stands firmly in the open-source camp, publicly releasing model weights and technical reports so that developers and researchers worldwide can freely use and improve upon them.

The divergence of the three paths is now crystal clear:

Million-Token Context Becomes Standard as Industry Competition Enters a New Phase

From a technological evolution perspective, this wave of releases sends a clear signal: million-token context is no longer a competitive moat — it's the price of admission.

Looking back over the past year, context windows have leaped from 8K to 32K, then 128K, and now 1M. When three leading players extend their context to the million-token level almost simultaneously, it means the next phase of competition will shift to deeper dimensions — actual reasoning quality over long contexts, tool-calling capabilities, Agent orchestration efficiency, and the "effective utilization rate" in real-world scenarios.

After all, being able to fit a million tokens is one thing; truly understanding and making high-quality use of those million tokens is another matter entirely.

The Power of Open Source: DeepSeek's Differentiating Advantage

DeepSeek's simultaneous open-sourcing of model weights and technical reports carries significant implications for the entire industry. For small and medium-sized enterprises and independent developers, this means the ability to deploy a top-tier million-token-context model locally, safeguarding data privacy while reducing long-term usage costs.

Against the backdrop of OpenAI's continued price hikes and an increasingly closed proprietary ecosystem, DeepSeek's open-source strategy effectively offers the industry a "third option" — one that neither relies on expensive closed-source APIs nor requires training models from scratch, but instead builds applications rapidly on the shoulders of open-source giants.

Looking Ahead: What Will the Second Half of the Large Model Race Be About?

As model capabilities converge and million-token context becomes standard, the "second half" of large model competition has quietly begun. The decisive factors going forward may not be about how powerful the model itself is, but rather:

• Ecosystem Building: Who can build the richest toolchain and application ecosystem around their model?
• Real-World Deployment: Who can truly solve practical problems in vertical industries?
• Cost Efficiency: Who can push inference costs to the lowest while maintaining high quality?

The release of DeepSeek V4 has added more fuel to this race. And for developers and users, this is undoubtedly good news — stronger models, longer context, more choices, and it's open source.