How is Gemini Ultra compared to Gemini Pro? Does it worth the upgrade?

Evaluating Gemini Ultra and Gemini Pro: Is Upgrading Worthwhile for Complex Academic and Content Creation Tasks?

Introduction

As a seasoned user of Advanced Gemini and NoteBookLM, I leverage these AI tools extensively in my professional capacity as a psychologist specializing in Human Sciences. My work involves processing a vast array of academic research, generating content for books, articles, social media, and creating scripts for podcasts and YouTube videos. This multifaceted workflow has traditionally been satisfying, but my evolving needs now demand more sophisticated AI capabilities.

Understanding My Expanded Requirements

Recently, my tasks have grown in complexity. I now regularly analyze complex Excel files, synthesize numerous research papers simultaneously to identify key connections and similarities, and engage in detailed conversations about clinical cases with colleagues. These activities require not only high-level reasoning and extensive contextual understanding but also robust data extraction capabilities from varied document formats.

Initial Experiences and Challenges

Having experimented with ChatGPT Pro for two months, I found it to be somewhat unreliable due to frequent issues. Consequently, I have been exploring alternative solutions like Gemini Ultra, especially now that it is accessible in my region (Italy, within the European Union).

Notably, I have observed differences in performance among different AI tools. For example, o3-Pro demonstrated superior ability in processing PDFs, particularly when extracting specific data from tables. In one instance, I needed to update an Excel sheet with data from a particular column across multiple PDFs—exports from other Excel files composed mainly of tables. While Gemini Pro failed to recognize the target column, claiming it did not exist, o3-Pro successfully identified and read the data, even in scanned PDFs where content was only accessible as images.

Performance in Heavy Non-Code-Based Tasks

This experience raises a critical question for users engaged in research-intensive and data-heavy workflows: How do Gemini Ultra and Gemini Pro compare in handling complex, non-code-related tasks? Specifically, I am interested in their capabilities to process large documents, interpret data from varied formats (especially scanned images), and support long, nuanced conversations related to clinical and academic contexts.

Features That Matter to Me

While I appreciate the additional benefits of the Ultra plan, such as YouTube Premium, Flow, and other features, my primary focus is on selecting an AI model that offers:

• Advanced reasoning skills
• An extensive context window for maintaining coherence over lengthy interactions
• Superior data extraction and processing abilities for tables and scanned documents

Conclusion

In summary, for professionals like myself requiring high-level reasoning and extensive contextual understanding in