Google Gemini 2.5 Pro explained: Everything you need to know

What is Gemini 2.5 Pro?

Gemini 2.5 Pro is an LLM developed by Google DeepMind. When it debuted in March 2025, it was Google's most advanced AI model, surpassing the capabilities and performance of prior iterations of Gemini.

As with Gemini 2.0, Gemini 2.5 Pro is a multimodal LLM, meaning it's not simply for text. It processes and analyzes text, images, audio and video. The model also has strong coding capabilities, surpassing prior Gemini models.

The Gemini 2.5 Pro model is the first in the Gemini series to be purpose-built as a "thinking model" with advanced reasoning functionality as a core capability. In some respects, the Gemini 2.5 Pro model is built on a version of Gemini 2.0, Flash Thinking, which provides limited reasoning capabilities. Advanced models such as Gemini 2.5 Pro use more time reasoning through, or "thinking" through, the required steps to execute a prompt, surpassing mere chain-of-thought prompting to enable more nuanced output, often with greater depth and accuracy.

Google applied advanced techniques, including reinforcement learning and enhanced post-training, to boost Gemini 2.5 Pro's performance over earlier models. The model launched with a one million-token context window, with plans to expand to 2 million tokens.

What's new in Gemini 2.5 Pro?

Gemini 2.5 Pro's new capabilities and enhanced functionality elevate the Google Gemini LLM family.

Key improvements include the following:

• Enhanced reasoning. Gemini 2.5 Pro's headline feature is its enhanced reasoning capability. According to Google, Gemini 2.5 Pro outperforms OpenAI o3, Anthropic Claude 3.7 Sonnet and DeepSeek R1 on reasoning and knowledge benchmarks, including Humanity's Last Exam.
• Advanced coding capabilities. According to Google, Gemini 2.5 Pro also surpasses prior iterations in terms of coding capabilities. Similar to its predecessors, this model generates and debugs code and creates visually appealing applications. The model supports code generation and execution, enabling it to test and refine its solutions. Gemini 2.5 Pro scored 63.8% on SWE-Bench Verified, an industry standard for agentic code evaluations, with a custom agent setup exceeding OpenAI GPT-4.5's but marginally trailing Claude 3.7 Sonnet.
• Advanced math and science skills. Google also claims to improve math and science capabilities. On the AIME 2025 math benchmark, Gemini 2.5 Pro scored 86.7%; on the GPQA diamond science benchmark, it managed 84%. Both scores surpassed their rivals.
• Native multimodality. Building on family strengths, Gemini 2.5 Pro maintains native multimodal capabilities, enabling comprehension of and working with text, audio, images, video and entire code repositories.
• Real-time processing. Despite increased capabilities, the model maintains reasonable latency, making it suitable for real-time applications and interactive use cases.

How does Gemini 2.5 Pro enhance Google?

The Gemini 2.5 Pro model improves Google's services – and its standing among peers – in the following ways:

Competitive leadership

The highly competitive LLM market features top global competitors – Meta's Llama family, OpenAI's GPT-4o and o3, Anthropic's Claude and xAI's Grok, plus DeepSeek from China – all vying for market share. At its release, Gemini 2.5 Pro immediately rocketed to the top of the LLM arena leaderboard for AI benchmarking, enhancing its position as a leading LLM developer for organizations to consider.

Better results across Google applications

At launch, Gemini 2.5 Pro was not integrated across Google's product suite, including search and Google Workspace applications. However, its successful integration promises to improve multiple services. For Google Search, enhanced reasoning capabilities provide more nuanced and accurate responses to complex queries. In Google Docs and other Workspace applications, the model's improved understanding of context enables more sophisticated document analysis and content generation.

Developer focus

The model's advanced code execution and generation abilities also strengthen Google's position in developer tools and services, improving function calling and workflow automation across Google's cloud services.

Uses for Gemini 2.5 Pro

Gemini 2.5 Pro supports a variety of tasks, including:

• Question and answer. Gemini is a resource for fundamental Q&A knowledge interactions, drawing upon Google's training data.
• Multimodal content summarization. As a multimodal model, Gemini 2.5 Pro summarizes long-form text, audio or video content.
• Multimodal question answering. The model combines information from text, images, audio and video to answer questions spanning multiple modalities.
• Text content generation. Similar to its predecessors, Gemini 2.5 Pro handles text generation.
• Complex problem solving. With its advanced reasoning capabilities, Gemini 2.5 Pro manages tasks that require logical reasoning, such as mathematics, science and structured analysis.
• Deep research. The model's extended context window and reasoning capabilities make it ideal for analyzing lengthy documents, synthesizing information from multiple sources and conducting in-depth research.
• Advanced coding tasks. Gemini 2.5 Pro generates and debugs code that supports application development tasks.
• Agentic AI. The model's advanced reasoning, function calling and tool use underpin its value as part of an agentic AI workflow.

What platforms accept Gemini 2.5 Pro integration?

Following in the Gemini family footsteps, Gemini 2.5 is set for integration across a series of Google services, including:

• Google AI Studio. At launch, the new model is available using Google AI Studio, a web-based tool that enables developers to try out models directly in the browser.
• Gemini app. In the drop-down model selection menu, subscribers to the Gemini Advanced service can access the model through the Gemini app on desktop and mobile platforms.
• Vertex AI. Google plans to make Gemini 2.5 Pro available with its Vertex AI platform, enabling enterprises to use the model for larger-scale deployments.
• Gemini API. Though unavailable at launch, all past versions of Gemini were available using an application programming interface that enables developers to integrate the model directly into their applications.

Sean Michael Kerner is an IT consultant, technology enthusiast and tinkerer. He has pulled Token Ring, configured NetWare and been known to compile his own Linux kernel. He consults with industry and media organizations on technology issues.