Gemini 2.5 is a thinking model, designed to tackle increasingly complex problems. Gemini 2.5 Pro Experimental, leads common benchmarks by meaningful margins and showcases strong reasoning and code capabilities. Gemini 2.5 models are thinking models, capable of reasoning through their thoughts before responding, resulting in enhanced performance and improved accuracy.

Intended Use

• Multimodal input

• Text output

• Prompt optimizers

• Controlled generation

• Function calling (excluding compositional function calling)

• Grounding with Google Search

• Code execution

• Count token

Performance

Google’s latest AI model, Gemini 2.5 Pro, represents a major leap in AI performance and reasoning capabilities. Positioned as the most advanced iteration in the Gemini lineup, this experimental release of 2.5 Pro is now the top performer on the LMArena leaderboard, surpassing other models by a notable margin in human preference evaluations.

Gemini 2.5 builds on Google’s prior efforts to enhance reasoning in AI, incorporating advanced techniques like reinforcement learning and chain-of-thought prompting. This version introduces a significantly upgraded base model paired with improved post-training, resulting in better contextual understanding and more accurate decision-making. The model is designed as a “thinking model,” capable of deeply analyzing information before responding — a capability now embedded across all future Gemini models.

The reasoning performance of 2.5 Pro stands out across key benchmarks such as GPQA and AIME 2025, even without cost-increasing test-time techniques. It also achieved a state-of-the-art 18.8% score on “Humanity’s Last Exam,” a benchmark crafted by experts to evaluate deep reasoning across disciplines.

In terms of coding, Gemini 2.5 Pro significantly outperforms its predecessors. It excels in creating complex, visually rich web apps and agentic applications. On SWE-Bench Verified, a standard for evaluating coding agents, the model scored an impressive 63.8% using a custom setup.

Additional features include a 1 million token context window, with plans to extend to 2 million, enabling the model to manage vast datasets and multimodal inputs — including text, images, audio, video, and code repositories.

Limitations

• Context: Google Gemini 2.5 Pro may struggle with maintaining context over extended conversations, leading to inconsistencies in long interactions.

• Bias: As it is trained on a large corpus of internet text, Google Gemini 2.5 Pro may inadvertently reflect and perpetuate biases present in the training data.

• Creativity Boundaries: While capable of creative outputs, Google Gemini 2.5 Pro may not always meet specific creative standards or expectations for novel and nuanced content.

• Ethical Concerns: Google Gemini 2.5 Pro can be used to generate misleading information, offensive content, or be exploited for harmful purposes if not properly moderated.

• Comprehension: Google Gemini 2.5 Pro might not fully understand or accurately interpret highly technical or domain-specific content, especially if it involves recent developments post-training data cutoff.

• Dependence on Prompt Quality: The quality and relevance of the model’s output are highly dependent on the clarity and specificity of the input prompts provided by the user.

Citation

https://cloud.google.com/vertex-ai/generative-ai/docs/gemini-v2#2.5-pro