Unpacking Gemini 2.5: Enhanced Capabilities Through Reasoning, Multimodality, and Advanced Tool Use

Unpacking Gemini 2.5: Enhanced Capabilities Through Reasoning, Multimodality, and Advanced Tool Use

As of April 25, 2025, the Gemini 2.5 family of AI models showcases significant advancements in artificial intelligence, particularly in their ability to "think," understand and process multiple data types simultaneously, and interact with external tools. This article explores the key features of Gemini 2.5, including the capabilities of the Gemini 2.5 Pro and Gemini 2.5 Flash models, supported by 100 illustrative examples.

General Gemini 2.5 Features

A major leap forward for the Gemini 2.5 series is its enhanced reasoning or "thinking" capability. Models like 2.5 Pro and 2.5 Flash can perform internal reasoning steps before generating a final response, leading to more thorough analysis, better breakdown of complex tasks, improved response planning, and higher accuracy, especially on multi-step problems.

Another core feature is Native Multimodality, allowing Gemini 2.5 models to process and understand various input types concurrently, such as text, images, audio, video, and code repositories. While they can process these diverse inputs, their primary output is currently text.

Advanced Tool Use, also known as Function Calling, is a key capability that enables Gemini 2.5 models to interact with external tools and APIs, execute code (like Python), perform web searches for up-to-date information (grounding), and generate structured outputs like JSON. This allows them to complete tasks that require dynamic or external resources.

The Gemini 2.5 models also feature a Massive Context Window, capable of handling up to 1 million tokens of information at once, with plans to expand to 2 million. This allows them to process extremely large amounts of information simultaneously, equivalent to about 1,500 pages of text, extensive codebases, or hours of video/audio. This large context window is crucial for tasks involving long documents, large codebases, or extensive multimedia content, enabling improved accuracy and efficiency by maintaining context over vast amounts of data.

Specific Model Features

Gemini 2.5 Pro (Experimental/Preview) is positioned as the most advanced and intelligent model in the family. It excels in complex tasks requiring deep reasoning and leads on several industry benchmarks in areas like math, science, general knowledge, and multimodal understanding. Its knowledge cutoff is January 2025.

Gemini 2.5 Flash (Preview) is designed for speed and cost-efficiency, serving as a "workhorse" model optimized for low latency and reduced cost while still incorporating the advanced "thinking" capabilities of the 2.5 generation. It is the first fully hybrid reasoning model, allowing developers to control the thinking capability to balance response quality, cost, and latency. It also features a 1 million token input context window and has a knowledge cutoff of January 2025.

Examples of Gemini 2.5 Capabilities

Here are 100 examples illustrating the key features of the Gemini 2.5 models:

Thinking Capability / Enhanced Reasoning (25 Examples)

This capability allows the AI to perform internal reasoning steps and break down complex tasks.

1. Solving a system of equations: Given two equations, find the value of ( x ) and ( y ) that satisfies both.
2. Prime Factorization: Find the prime factorization of a large number like 1260.
3. Geometry problem: Find the area of a triangle given its three side lengths using Heron's formula.
4. Number theory: Determine how many integers between 1 and 100 are divisible by 3 or 5.
5. Algebraic expression: Simplify an expression like ( \frac{3x^2 - 2x + 5}{x-1} ).
6. Combinatorics: How many different ways can you arrange 5 letters from the alphabet where two letters are identical?
7. Modular arithmetic: Solve for ( x ) in the congruence equation ( 7x \equiv 1 \pmod{11} ).
8. Quadratic equations: Find the roots of a quadratic equation like ( x^2 - 5x + 6 = 0 ).
9. Probability: What’s the probability of drawing two aces in a row from a deck of cards without replacement?
10. Sequences: Determine the next number in a Fibonacci sequence.
11. Physics: What is the force exerted by a 5 kg object accelerating at 2 m/s²? (Use Newton’s Second Law, ( F = ma )).
12. Chemistry: What is the molar mass of water (( H_2O ))?
13. Biology: What is the process of photosynthesis, and why is it important for plants?
14. Astronomy: What is the difference between a star and a planet?
15. Thermodynamics: What is the second law of thermodynamics, and how does it apply to heat engines?
16. Electricity and Magnetism: What is Ohm’s law, and how do you calculate the resistance in a circuit?
17. Kinematics: A car is moving at 20 m/s. If it accelerates at 4 m/s² for 10 seconds, what is its final speed?
18. Genetics: How does DNA replication occur in cells?
19. Optics: What is the focal length of a lens, and how does it affect the image formation?
20. Ecology: What role do producers play in an ecosystem?
21. Solving a quadratic equation by factoring, identifying roots, and checking results.
22. Breaking down a word problem to identify variables and apply the correct formula.
23. Reasoning through a logic puzzle to determine relationships and find the solution.
24. Analyzing Sudoku constraints and planning steps to fill the grid.
25. Breaking down a combination problem into smaller steps of choosing and arranging objects.

Native Multimodality (25 Examples)

This feature allows the AI to process and understand multiple data types simultaneously.

26. Given a photo of a dog with a caption saying, "This is a Golden Retriever," the AI can recognize the breed and describe the dog.
27. A picture shows a soccer player kicking a ball with the caption, “The ball is being passed,” and the AI can understand that it's referring to a sporting action.
28. A picture shows a person smiling with the text, "I am so happy today!" The AI can understand the person's emotion and correlate it with the text.
29. A graph shows sales data over time with a caption “Sales have been increasing steadily,” and the AI can analyze the trend and confirm the increase.
30. A picture of a classroom shows a blackboard, desks, and students. The AI can identify and describe the classroom setup based on the visual data.
31. A picture shows a busy street with cars, people walking, and tall buildings. The AI can identify the scene as a city street and describe its key elements.
32. A photo of a plant and the text “This is an example of photosynthesis in action” – the AI understands the process of photosynthesis in plants.
33. An image of the moon landing with the text "This was the first human landing on the moon," and the AI connects the event with the historical context of the Apollo 11 mission.
34. A picture of a bird with a text description: "This is a Bald Eagle." The AI can identify the bird from its features in the image.
35. A medical scan of a lung and a description of a tumor, where the AI identifies the tumor's location and size in the image.
36. Analyzing an image of a landscape and a text caption to identify elements like terrain, plants, and weather conditions.
37. Processing an audio recording and transcribing it into text while understanding the topic of discussion.
38. Summarizing a video about a specific topic based on the video content and a text description.
39. Debugging code by analyzing a screenshot of the code and error message along with a text description of the problem.
40. Identifying a flower type by analyzing a photo and a text caption asking for identification.
41. Analyzing the sentiment of an audio clip based on the speaker's tone and a text description of their emotional state.
42. Explaining a code snippet discussed in a tutorial video by processing both the video and the code.
43. Identifying an issue in code by comparing it to a flowchart provided as an image and a text description of the problem.
44. Combining an audio story, an image of the setting, and a text description of the plot to provide a contextual summary.
45. Recommending a product based on a video showcasing it, a text review, and an image of the product in use.
46. Understanding a recipe by processing an image of the finished dish and the text instructions.
47. Analyzing a historical event by examining a historical photograph and reading accompanying text.
48. Interpreting a musical piece by processing an audio recording and the sheet music.
49. Understanding a scientific experiment by watching a video of the experiment and reading the experimental procedure.
50. Providing feedback on a design by analyzing an image of the design and a text description of the requirements.

Advanced Tool Use / Function Calling (25 Examples)

This capability allows the AI to interact with external tools and APIs.

51. Calculating the factorial of 15 by running Python code.
52. Searching the web for the current price of Bitcoin.
53. Fetching real-time weather information for Tokyo using a weather API.
54. Generating a JSON object representing a person with name, age, and email.
55. Converting 100 USD to EUR using a currency conversion API.
56. Finding all employees in the Sales department by executing an SQL query on a database.
57. Accessing a financial API to retrieve the latest stock price of Tesla.
58. Resizing an image to 800x600 using an external image processing tool.
59. Scraping the latest movie listings from a popular movie database.
60. Booking a flight from New York to Paris for next week using a travel booking API.
61. Calculating the area of a circle with radius 7 by running Python code.
62. Getting the latest news on a specific topic by searching the web.
63. Fetching the current weather in New York using a weather service API.
64. Checking the current price of Tesla stock using an API.
65. Analyzing a dataset to find the average sales per month by running Python code.
66. Creating a JSON object that represents a person with a name, age, and address.
67. Finding the exchange rate between two currencies using an API.
68. Retrieving information from a database by executing SQL queries.
69. Generating a simple HTML webpage with a header and a paragraph.
70. Modifying or enhancing images, like resizing or applying filters, via external image processing tools.
71. Fetching live sports scores from an ongoing game via an API.
72. Sending automated emails by integrating with an email service.
73. Scraping data from a website, such as fetching product prices from an online store.
74. Converting a CSV file into a JSON format.
75. Running simulations or mathematical models using Python or other programming languages.

Massive Context Window (25 Examples)

This feature allows the AI to process and understand large amounts of information simultaneously.

76. Analyzing and summarizing a 500-page contract to identify key clauses and risks.
77. Analyzing multiple court rulings (over hundreds of pages) to find connections and provide legal insights.
78. Processing a thousand-page collection of research papers on a specific topic, summarizing key findings and making comparisons.
79. Analyzing a PhD thesis of 200 pages, extracting conclusions, and cross-referencing with existing literature.
80. Processing a 500-page novel and creating a cohesive summary of themes, plot points, and character development.
81. Tracking character relationships and motivations over hundreds of pages in a long novel.
82. Reading an entire large software project (e.g., 50,000 lines of code) and identifying bugs or areas for improvement.
83. Generating automated documentation for an entire codebase, explaining interactions across dozens of files.
84. Analyzing a report of multiple clinical trials (over 1,000 pages) and synthesizing conclusions about drug efficacy and safety.
85. Processing a large dataset of genetic sequences (thousands of entries) and identifying patterns related to diseases or traits.
86. Transcribing a 1-hour video lecture, extracting key points, and summarizing complex concepts.
87. Transcribing and summarizing a 3-hour podcast on economics, providing key insights.
88. Analyzing millions of rows of financial data, detecting trends, anomalies, and forecasting future conditions.
89. Processing thousands of customer reviews to summarize common complaints or areas for improvement.
90. Reading through a month’s worth of news articles (thousands of pages) and summarizing key events, correlations, or trends.
91. Summarizing multiple journal articles on climate change, extracting conclusions and suggesting research areas.
92. Processing an entire company’s annual report (hundreds of pages), identifying performance metrics, growth areas, and potential risks.
93. Analyzing a large market research report (thousands of pages) to identify consumer trends and competitive advantages.
94. Analyzing a comprehensive set of patient records (dozens of documents), recognizing patterns to suggest diagnoses.
95. Analyzing hours of public health data across multiple regions, identifying trends related to disease spread.
96. Processing a large technical manual to answer specific questions about a device or software.
97. Reading through a collection of historical letters to understand the context of a specific time period.
98. Analyzing a large architectural plan to identify potential structural issues.
99. Processing a vast amount of environmental data to identify long-term patterns and changes.
100. Reviewing a complete codebase for security vulnerabilities across all files.

Related Features & Availability

Gemini 2.5 Pro users can access Veo 2 Video Generation, Google's latest video generation model, to create short videos from text prompts. Deep Research, a feature in Gemini Advanced powered by models like 2.5 Pro, can analyze hundreds of web sources in real-time to generate comprehensive research reports.

Gemini 2.5 Pro is available in preview via Google AI Studio, Vertex AI, and for Gemini Advanced users. Gemini 2.5 Flash is available in preview via the Gemini API (Google AI Studio, Vertex AI) and in the Gemini app.

In conclusion, the Gemini 2.5 models, with their enhanced reasoning, native multimodality, advanced tool use, and massive context window, represent a significant step forward in AI capabilities, enabling them to handle complex tasks and process vast amounts of information more effectively.