Revolutionary Step of Deepseek: When Technology Changes the Game (December 1)

The release of Deepseek v3.2 has become the main hot topic today, and for good reason. The company demonstrated results that directly compete with the latest closed models from industry leaders, including Gemini3.0. This unquestionably elevates Deepseek to the category of open-source SOTA, with all measurable metrics confirming this status.

What does this breakthrough actually rely on?

From a technical perspective, the innovation does not lie in revolutionary new architecture. Deepseek continues to apply DSA and consistently invests in the post-training phase, which accounts for over 10% of the total computational budget. But somehow, the company has found a way to maximize the efficiency of this approach. By leveraging the full potential of experimental version v3.2, the team achieved results that directly contradict the narrative of a “computational power wall.”

Sibin Gou, one of Deepseek’s key researchers, expressed an interesting hypothesis: if Gemini3 demonstrated capabilities on the pretraining front, then v3.2 focuses on scaling reinforcement learning (RL) and chain-of-thought reasoning (CoT). This does not mean deflation of computational power — on the contrary, it requires greater expenditure during inference. The key idea: scaling should continue at all levels, and fluctuations about its limits are just noise.

Market context and real value

However, a very critical point arises here. Deepseek itself admits that token efficiency in this version is “inferior” compared to alternatives. Moreover, the specialized version of the model uses significantly more tokens to achieve the same results. This directly impacts the practical cost of deployment.

According to analysts, demand for computational power remains fundamentally unquenched. The real problem is not that computations are decreasing, but that their cost remains too high for large-scale commercial deployment. Only revolutionary breakthroughs in hardware and model architectures can fundamentally change this equation.

What does this mean for big players?

For companies like OpenAI, which built their competitive advantage precisely on the “model capabilities” as the main “moat,” this Deepseek release sounds like a serious warning. An open-source alternative, already approaching closed solutions, narrows the gap regarding exclusive technological advantage of closed developments.

December 1: the perfect storm in the market?

Interestingly, this release coincides exactly with the third anniversary of ChatGPT’s launch. Today’s market evening is likely to become saturated with volatility: several unpredictable macro factors from Japan, BTC movements, and rumors about Amazon re:Invent as the next catalyst for change. Among analysts, predictions are already circulating about how aggressively the market will respond to the competition symbolized by today’s events.

What’s next: is v3 exhausted?

In conclusion: some participants in research circles are already asking whether version v3 has been pushed to its limits, and whether it’s time to think about v4. If Deepseek spent a year simply optimizing version 3, it speaks to the depth of work and the seriousness of the company’s ambitions. The number of pivots in the AI space is clearly increasing.