Coming unexpectedly soon after the release of Gemini 2.0, Google released Gemini 2.5 Pro under its “experimental” banner this week. It was a low-key arrival, with the firm continuing to drop new versions regularly and often and to avoid the controversies of its past high-profile launches, but it seems the changes are far from incremental. It appears Google has reclaimed a leading position in terms of raw model capability. By most accounts and by some margin this is now the most powerful AI model available.
This is a reasoning model that thinks before it responds. Building upon previous Gemini versions, it also maintains native multimodality, handling text, audio, images, video, and code. It features a very large context window, starting at one million tokens with plans to expand, and can output up to 65,000 tokens meaning its suited to dealing with the largest code bases.
Google will hope this release vindicates their deep investments in custom hardware (TPUs), software, and the focused efforts of the reorganised and consolidated Google DeepMind research team. The emphasis on advanced reasoning, coding, and multi-modal performance across a massive context window points to highly integrated strengths, that will put pressure on the other labs to respond.
But having the ‘best’ model is only part of the story. The crucial question now is: how do these increasingly powerful models deliver real-world value? There seem to be three main paths:
Chatbots: While 2.5 Pro will power formidable chatbots, this market is crowded. ChatGPT dominates, with others like Claude fulfilling a range of niches. It will remain hard for Google’s Gemini app to gain significant share here, and simple chat massively underutilise the model’s advanced capabilities.
Google Products: Integrating AI into Search, Workspace (Gmail, Docs), and other Google services holds potential. However, attempts so far, like adding piecemeal chat features or AI summaries, haven’t been transformative. Simply “bolting on” AI to classical software is not the best way to harness new power.
Developers: Enabling developers to build new products accelerate the adoption of agents using these models is key. Google has somewhat improved its developer tooling, but Gemini 2.5 Pro’s current “experimental” status means usage limits, no pricing details yet, and limited global availability. It’s not ready for production and it will be several months before we know its actual impact.
Takeaways: Looking ahead, most see the real AI potential in sophisticated agents capable of handling long and complex tasks. At ExoBrain our focus is on building and deploying this new digital workforce. While Gemini 2.5 Pro appears to be the most powerful engine right now, availability and platform limitations will constrain its potential. If this is a high watermark for Google, the model will not be remembered as others will soon surpass it, if this release frequency is maintained, pricing is competitive, and developers are fully catered for (maybe even furnished with new agent building tools like those from OpenAI) Google could finally start to flex its muscles and dominate. To build transformative agents, developers require faster access to robust, globally available models and better tools. As some voice concerns about the demand for the huge expansion in datacentre capacity and compute, models like 2.5 Pro show what’s possible but the demand won’t be there until they are truly ready to power the agentic AI revolution.