Google Claims Gemini 2.5 Outperforms Best Models From OpenAI, DeepSeek, and Other AI Tech Giants

Google has just introduced Gemini 2.5, which the company calls its “smartest AI model yet.” The first version of the model, Gemini 2.5 Pro, achieved impressive benchmark scores in a variety of tests.

Google claims that Gemini 2.5 outperforms the best models from OpenAI, DeepSeek, and other AI tech giants

Gemini 2.5 Pro is available now through Google AI Studio and in the Gemini app if you are a Gemini Advanced user . Gemini 2.5 Pro will also be available through Vertex AI in the near future.

Google has not shared pricing for the Gemini 2.5 Pro or other Gemini 2.5 models at this time.

All of the models that Gemini 2.5 uses are “thinking models,” meaning they can process the thought process before generating a response. These “reasoning” models are the next big thing in the AI ​​space because they generate more complex and often more accurate responses.

“Now, with Gemini 2.5, we’ve achieved a new level of performance by combining a significantly improved base model with improved post-training ,” Google said.

“In the future, we will build these thinking capabilities directly into all of our models so they can handle more complex problems and support agents with even better context awareness . ”

How does Gemini 2.5 compare to OpenAI models?

Google's Gemini 2.5 Pro models outperform previous top models from OpenAI and DeepSeek.

The benchmark scores for Gemini 2.5 shared by Google are quite impressive. Gemini 2.5 Pro Experimental scored 18.5% on Humanity's Last Exam.

That score means that, at least for now, Gemini 2.5 Pro Experimental is the best model by that metric. Its score beats OpenAI 03-mini (14%) and DeepSeek R1 (8.6%).

That particular test is considered difficult, although it is not the only way to measure the performance of an AI model.

Google also highlighted the Gemini 2.5 Pro's programming capabilities and the model's math and science benchmarks. Gemini 2.5 Pro is currently leading in math and science benchmarks as measured by GPQA and AIME 2025.

Is it possible to program in Gemini 2.5?

Programming is the main focus of Gemini 2.5. Google claims “a huge leap over 2.0” and teases more improvements are on the way.

Google's new model can create web apps and agentic code. A demo from Google shows the Gemini 2.5 Pro being used to create a game from a single line prompt.

4 Reasons Why Google's Gemini 2.5 Pro Matters for Enterprise AI

Here are four key points to keep in mind for enterprise teams when evaluating the Gemini 2.5 Pro.

1. Structured, transparent reasoning – a new standard for clarity of thought

What sets Gemini 2.5 Pro apart isn’t just its intelligence—it’s how clearly that intelligence demonstrates its work. Google’s step-by-step training method creates a structured train of thought (CoT) that doesn’t feel like rambling or guesswork, like what we’ve seen from models like DeepSeek . These CoTs aren’t truncated into shallow summaries like OpenAI’s models. The new Gemini model presents ideas in numbered steps, with sub-bullets and extremely clear internal logic.

In practical terms, this is a breakthrough in reliability and tractability. Business users evaluating output for critical tasks – such as reviewing policy implications, coding logic, or summarizing complex research – can now see how the model came up with an answer. This means they can validate, correct, or redirect answers with more confidence. This is a big step forward from the “black box” feel that still exists in many large language model (LLM) outputs .

For a more in-depth look at how this model works, check out the analysis video where Gemini 2.5 Pro is put to the test live. One example discussed: When asked about the limitations of large language models, Gemini 2.5 Pro demonstrated remarkable awareness. It outlined common weaknesses and categorized them into areas such as “physical intuition,” “new concept synthesis,” “long-term planning,” and “moral nuance,” providing a framework for understanding what the model knows and how to approach the problem.

Enterprise engineering teams can leverage this capability to:

• Debug complex logic chains in mission-critical applications
• Better understanding of model limitations in specific domains
• Providing more transparent AI-enabled decisions to stakeholders
• Improve their own critical thinking by studying the model's approach

One notable limitation is that while this structured reasoning is available in the Gemini app and Google AI Studio, it is not currently accessible via API — a shortcoming for developers looking to integrate this capability into enterprise applications.

2. A real contender for cutting-edge technology – not just in theory

The model currently tops the Chatbot Arena leaderboard by a significant margin – more than 35 Elo points ahead of the next best model, notably given that the OpenAI 4o update came out the day after the Gemini 2.5 Pro launched. And while benchmark dominance is often fleeting (with new models launching every week), the Gemini 2.5 Pro really feels different.

It excels at tasks that reward deep reasoning: coding, nuanced problem solving, summarizing across documents, and even abstract planning. In internal testing, it performed particularly well on previously difficult benchmarks like “Humanity’s Last Exam,” a popular benchmark for exposing LLM weaknesses in abstract and nuanced areas.

Business groups may not care which model wins which academic rankings. But they will care that the model can think — and show you how it thinks. Vibe testing is important.

As respected AI engineer Nathan Lambert notes, “Google has the best models again, because they should have started this whole AI boom. A serious mistake has been corrected.” Business users should see this as not just Google catching up to its competitors, but potentially surpassing them in capabilities that matter to business applications.

3. Finally, Google's encryption game is strong

Traditionally, Google has lagged behind OpenAI and Anthropic in terms of developer-focused coding support. Gemini 2.5 Pro changes that.

In hands-on tests, it demonstrated strong one-shot performance on coding challenges, including building a working Tetris game that ran on the first try when exported to Replit—no debugging required. More remarkably, it explained the code structure clearly, labeled variables and steps thoughtfully, and presented its approach before writing a single line of code.

This model competes with Anthropic’s Claude 3.7 Sonnet, which is considered the leader in code generation and a key reason for Anthropic’s success in the enterprise. But Gemini 2.5 offers a key advantage: a massive token context window of 1 million. Claude 3.7 Sonnet currently only offers 500,000 tokens.

This large context window opens up new possibilities for reasoning across the entire codebase, reading online documentation, and working across multiple dependent files. Software engineer Simon Willison's experience demonstrates this advantage.

Using Gemini 2.5 Pro to deploy a new feature to our codebase, the model identified the necessary changes across 18 different files and completed the entire project in about 45 minutes, averaging less than 3 minutes per modified file. This is a serious tool for businesses experimenting with agent frameworks or AI-powered development environments.

4. Multi-method integration with agent-like behavior

While some models like OpenAI's latest 4o might show more flash with eye-catching image generation, the Gemini 2.5 Pro feels like it's quietly redefining what grounded multimodal reasoning looks like.

In one example, Ben Dickson’s hands-on experiment for VentureBeat demonstrated the model’s ability to extract key information from a technical paper about search algorithms and generate a corresponding SVG flowchart — then improve that flowchart when presented with a rendered version that contained visual errors. This level of multimodal reasoning enables new workflows that were previously not possible with text-only models.

In another example, developer Sam Witteveen uploaded a simple screenshot of a Las Vegas map and asked what Google events were happening nearby on April 9. The model identified the location, inferred the user’s intent, searched online, and returned accurate details about Google Cloud Next, including date, location, and citation. All of this was done without a custom agent framework, just the core model and built-in search.

In fact, this multimodal input reasoning model goes beyond just looking at it. It suggests what a business’s workflow might look like in 6 months: Upload documents, diagrams, and dashboards, and let the model synthesize, plan, or take meaningful action based on the content.