Google Debuts Touted Gemini 2.5 in the ‘Winner-Take-All’ AI Model Race

Google unveiled its most powerful generative artificial intelligence model yet, one that opens a new front in the competitive AI race as it performs mostly head-and-shoulders above its staunchest rivals in industry benchmarks.

Gemini 2.5, the latest from Google’s family of generative AI models with the same name, is its “most intelligent” model and outperforms top-of-the-line models from OpenAI, Anthropic, Grok and DeepSeek “by a significant margin,” Google DeepMind Chief Technology Officer Koray Kavukcuoglu wrote in a Wednesday (March 26) blog post.

That means enterprises deploying generative AI models can tap a more powerful tool in the Google Cloud arsenal to build their custom apps.

“Google’s Gemini 2.5 has landed — a masterpiece of reasoning, multimodality and raw computational might,” Anders Indset, founder of investment firm Njordis, told PYMNTS. Google is “thrusting itself into an AI race that’s no longer a sprint but a relentless, winner-take-all siege.”

In reasoning and knowledge, Gemini 2.5 beat OpenAI’s o3-mini and GPT-4.5, Claude 3.7 Sonnet, Grok 3 Beta and DeepSeek R1. The same went for code editing, visual reasoning, long context and multilingual performance. In science, it beat everything except Claude. In math, it was second only to Grok. It performed comparatively weakest in code generation, where it was third.

The first version of Gemini 2.5 that Google is releasing is an experimental Pro version. It is available to Gemini Advanced paid users and will head to Google Cloud’s Vertex AI platform “soon,” per the blog post. Developers and enterprises can try it out at the Google AI Studio.

Read also: Google Wants 500 Million Gemini AI Users by Year’s End

Gemini 2.5 is a thinking or reasoning model, which pauses to cycle through its logic before responding to improve the accuracy and performance of its answers. It analyzes information, comes to logical conclusions, adds context and understands nuance to reach a decision, according to the post.

Google’s rivals have already released their own reasoning models, which include those from OpenAI, Anthropic, Grok, DeepSeek and others. Google itself has released a reasoning model called Gemini 2.0 Flash Thinking.

However, Gemini 2.5 goes beyond the reasoning capabilities of Gemini 2.0 Flash Thinking, which uses reinforcement learning (rewarding right answers, punishing wrong ones) and chain-of-thought prompting, per the post.

With Gemini 2.5, Google was able to reach a new level of performance by combining a “significantly enhanced base model with improved post-training,” the post said.

Going forward, Google will incorporate these thinking capabilities directly into all its models, so they can handle “more complex problems and support even more capable, context-aware agents,” according to the post.

See also: Google’s Gemini 2.0 Promises Autonomous Control of Complex Business Tasks

Like Google’s other Gemini models, Gemini 2.5 is natively multimodal, meaning it can analyze and understand text, audio, video, images and code — capabilities built in from the ground up, not bolted on.

Gemini 2.5 also offers a context window of 1 million tokens (about 750,000 English words), so it can accept very long prompts, a feature matched only by Alibaba on some of its Qwen generative AI models.

“The context window is incredibly important for the AI race,” Ilia Badeev, head of data science at Trevolution Group, told PYMNTS. “The length of the context is one of the most crucial parameters for the practical use of [AI models].”

“With a larger context, the model can provide better assistance with programming, answering questions and text generation — anything basically,” Badeev said.

Gemini 2.5 blew away the competition in long-context performance with 83.1%, the blog post said. OpenAI’s o3-mini came in at 61.4% and its GPT-4.5 at 64%.

Google plans to double the context window soon, per the post.

“If Google does indeed implement a 2 million token context, it will be an unprecedented advantage over other models, even with lower benchmarks,” Badeev said.

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