Gemini 2.5 Pro and DeepSeek V3: A New Chapter in AI Convergence
The State of Industrial AI - E90
In today’s fast-evolving AI landscape, just as the industry witnessed the release of GPT4o image generation and a new version of DeepSeek, we’ve also seen the arrival of Gemini 2.5 Pro. While there isn’t an ultra or nano version—only the Pro remains—the buzz is already sky-high. Some at Google are even claiming it to be the world’s most powerful model. I’ve been testing it extensively, and despite its secretive benchmarks and guarded source details, a bigger narrative is emerging about the commoditization of AI.
I. Setting the Stage: When Secrets Become Commodity
The conversation is heating up beyond mere technical details. Recently, Microsoft’s CEO stated that AI models are increasingly becoming commoditized—essentially, they’re evolving into products that rival commodities in performance. As reported in a revealing Microsoft Information report and captured in discussions on how AI is getting “commoditized”, the industry seems to be saying that no one truly holds the secret to AGI anymore. Even as new products like OpenAI’s GPT4o image generation blur the lines between models and experiences, questions remain: Is there truly a secret behind intelligence, or are we witnessing a convergence of performance?
II. Gemini 2.5 Pro: The Benchmark Breakdown
Announced alongside DeepSeek V3, Google’s new Gemini 2.5 Pro positions itself as “Google’s most intelligent AI model”—a humbler title compared to its predecessors. This release, detailed in the official Gemini 2.5 Release Notes, introduces a series of benchmark results that can be overwhelming at first glance.
One benchmark, humorously dubbed humanity’s last exam, is as much a test of obscure trivia and difficult Latin translations as it is of advanced reasoning and even butterfly physiology. Gemini 2.5 Pro shows impressive results across knowledge-intensive tasks, science questions, and even complex coding challenges. Notably, while tools like OpenAI’s Deep Research use additional techniques—sometimes boosting their scores—Gemini 2.5 Pro demonstrates consistent performance, especially in tests like the Vista Bench, which evaluates visual language understanding in free-form responses.
III. DeepSeek V3: The New Contender
The industry is also abuzz with the announcement of DeepSeek V3. Unlike its predecessors—which focused on reasoning with minimal external data—this new base model is designed to elevate reasoning capabilities. It’s akin to what GPT-4.5 represents for OpenAI: a robust foundation upon which the next generation of reasoning models will be built.
Comparing DeepSeek V3 to Gemini 2.5 Pro, especially in mathematics and coding, reveals a subtle convergence. Both models now seem to operate within a narrow performance band when compute resources are matched. Yet, despite these similarities, nuances remain. For example, while Gemini 2.5 Pro handles long-context tasks with extraordinary finesse (it can process nearly a million tokens—roughly 750,000 words!), DeepSeek V3 shows promise in its specialized reasoning tasks, hinting at what the next R2 model might achieve.
IV. Convergence in AI Performance: Is There a Clear Leader?
One of the most intriguing outcomes of recent tests is that performance across different model families is beginning to converge. Whether it’s Gemini 2.5 Pro, DeepSeek V3, or even tools like OpenAI’s O3—which, by the way, hasn’t fully released its capabilities yet—the gap in performance is narrowing.
For instance, while some companies still report benchmarks using techniques like majority voting (which can push scores higher), others do not. This variance makes direct comparisons challenging, yet one thing is clear: if you keep compute expenditures level, the differences are marginal.
Interestingly, models like Gemini 2.5 Pro not only excel in traditional benchmarks but also demonstrate remarkable long-context processing—a feature that currently sets it apart. This performance is one of the key reasons why some believe that with enough compute, there’s a ceiling for AI performance regardless of the model family.
V. Additional Observations and Industry Impacts
The industry isn’t just focused on raw performance. Broader narratives are emerging about the future role of these models. For example, some analysts point to Microsoft’s moves, where there’s an ongoing internal race to replicate top-tier performance without solely relying on external partners. According to a Microsoft Information report and commentary about AI being “commoditized”, the implication is that access to compute money is becoming the real differentiator.
On a lighter note, while Gemini 2.5 Pro is setting benchmarks in long-context handling, the industry has also seen amusing side-by-side comparisons. For example, insights from Amodei: 100% Coding suggest that while AI might soon handle 90% of coding tasks, there are still gaps in creative problem-solving. And if you’re curious about quirky demonstrations, check out Claude Plays Pokemon for a fun twist on AI’s limitations.
Finally, consider the financial stakes: as reported in Microsoft Money from Onslaught, sales of cloud and AI services have skyrocketed, underscoring the real-world impact these converging technologies are already having.
VI. Final Thoughts: A Converging Future
The release of Gemini 2.5 Pro and DeepSeek V3 underscores an important trend: while the race for AGI continues, the performance gap between leading models is shrinking. It’s not that progress has stalled—rather, the improvements are now incremental, suggesting a future where many models perform at a similar level when compute is normalized.
At Essentia Ventures, we see this convergence as both a challenge and an opportunity. The industry is shifting from a battle of secret sauces to one of efficient compute utilization.
For now, Gemini 2.5 Pro is available free for now in Google’s AI studio, but that window is temporary. As companies continue to innovate and push the envelope, the question remains: will the next breakthrough come from raw performance or the strategic harnessing of compute power?
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