Google is doubling down on AI reasoning capabilities with the launch of Gemini 2.5 Pro Experimental—a new multimodal model designed to “think” before answering. This approach, which deliberately adds latency and computational overhead, allows the model to perform deeper reasoning, fact-checking, and more nuanced problem-solving. It’s part of a broader industry shift toward agentic AI systems capable of autonomous, complex task execution.
Gemini 2.5 Pro is available via Google AI Studio and in the Gemini app for users subscribed to the company’s $20-per-month Gemini Advanced plan. Google states that all of its future models will include reasoning as a core capability, positioning Gemini 2.5 as the standard-bearer for this next phase of AI development.
What sets Gemini 2.5 apart is not just its reasoning engine, but its impressive 1 million token context window—equivalent to about 750,000 words. This capacity will soon double to 2 million tokens, allowing for massive in-context comprehension that surpasses the length of most novels and technical documentation combined.
On performance benchmarks, Gemini 2.5 shows notable strengths and some trade-offs. It scored 68.6% on Aider Polyglot, a benchmark that tests code editing capabilities, outperforming leading models from OpenAI, Anthropic, and DeepSeek. In software development evaluations like SWE-bench Verified, it achieved 63.8%, beating OpenAI’s o3-mini and DeepSeek’s R1 but trailing Anthropic’s Claude 3.7 Sonnet, which led with 70.3%.
In more interdisciplinary testing, Gemini 2.5 also performed competitively. On Humanity’s Last Exam, a challenging multimodal benchmark covering math, humanities, and natural sciences, it reached 18.8%, outscoring most flagship models.
Google emphasizes the model’s strength in creating visually compelling web applications and agentic coding systems—use cases where multimodal reasoning and sustained context are critical. That focus reflects broader industry trends, where AI models are no longer just chat interfaces but core engines for building semi-autonomous digital agents.
The move follows similar developments across the field, with major players like Anthropic, xAI, and DeepSeek releasing their own reasoning-capable models. These systems sacrifice raw speed for better accuracy and richer understanding, particularly in complex domains like software engineering and scientific analysis.
Google has not yet disclosed Gemini 2.5 Pro’s API pricing, promising more details in the coming weeks.
Full article from TechCrunch by Maxwell Zeff can be found here.