Google won. (Gemini 2.5 Pro is INSANE)

Gemini 2.5 Pro is being positioned as a major step forward in “thinking” AI—delivering faster responses and strong benchmark performance while Google pushes more reasoning capability directly into the model rather than bolting it on after the fact. Early results show it moving to the top of popular leaderboards immediately after release, including beating well-known competitors such as GPT-4.5 and DeepSeek on benchmark-style comparisons. The practical takeaway is that the model is not just smarter on paper; it’s also fast enough and cost-lean enough to be usable as a default in real workflows.

A key reason for the excitement is how Google is packaging reasoning. Earlier “thinking” features in Google models were described as patched-on behavior—prompting the model to think, then returning an answer after a follow-up. With Gemini 2.5 Pro, the thinking behavior is treated as a baked-in capability, designed to handle more complex prompts and support context-aware agent behavior. That shift matters because it changes how reliably the model can follow multi-step instructions and operate under richer context.

The transcript also ties performance to concrete product implications. In T3 Chat, Gemini models are already being added early, with rate limits described as high enough that pro subscribers should not hit frequent throttling. The creator expects pricing to be competitive as well, noting that Google has confirmed Pro models will be available on paid tiers soon—an important operational detail because paid access is typically what keeps rate limits and service availability stable.

Benchmark highlights include strong performance on math and notable wins on an exam where OpenAI had built the benchmark itself—framing it as a rare case of a company outperforming another on the latter’s own test. Coding, however, remains mixed: thinking models can feel worse in editor workflows because they may take longer to respond, and the added internal context can sometimes lead to “self-gaslighting” errors—examples include a physics-style prompt where the model’s reasoning produces an incorrect outcome (like gravity behaving the wrong way) and a “ball in a hexagon” coding/logic test where it performs well until a corner case causes the ball to fall out.

Beyond model quality, the transcript argues that Google’s advantage is structural. Model performance is framed as a function of three inputs: data, hardware, and science. Google is portrayed as unusually strong across all three—especially because it builds custom hardware and can tightly integrate model training and inference with that infrastructure. The discussion contrasts this with other AI companies that may excel in only one area (science, data, or hardware) and must partner externally for the rest.

The transcript also emphasizes context length and multimodal tooling as differentiators. Gemini’s 1 million token input window is highlighted as “nuts,” enabling tasks that would be impossible with smaller context limits. Real examples include extracting dozens of YouTube and SoundCloud links from a large, messy blog page by feeding the model a huge HTML blob and having it generate executable JavaScript to pull the links. Native PDF support is also praised, including parsing images, graphs, and charts from PDFs.

Finally, the creator flags a limitation: the “thinking” traces seen in Google’s AI Studio are not exposed through the API, meaning developers using API access may not get the same visible reasoning data—only the final response after a wait. Even with that caveat, Gemini 2.5 Pro is described as a compelling combination of speed, reasoning capability, and practical tooling, reinforcing the claim that Google is well positioned to lead the AI race.