Faster, better AI-powered code reviews. Start your free trial!  
Faster, better AI-powered code reviews.
Start your free trial!

Get high quality AI code reviews

Gemini: Google’s Next Leap in Generative AI Models

Gemini AI Model

Table of Contents

Google has recently introduced Gemini, a groundbreaking generative AI model that signals a significant evolution in artificial intelligence technology. This AI model isn’t just a singular entity but a family of models, each tailored for specific applications and computational capabilities. Here’s a comprehensive overview of Gemini, its distinct versions, and its potential impact.

The Gemini Family

Gemini comes in three versions:

1- Gemini Ultra: This is the flagship model of the Gemini family, designed for highly complex tasks. It’s the most advanced version, demonstrating superior capabilities in handling nuanced information across various modalities, including text, images, audio, and code.

2- Gemini Pro: A lighter version of Gemini, it still packs considerable power and is the backbone of Bard, Google’s ChatGPT competitor. As of now, Gemini Pro operates in English within the U.S. and primarily focuses on text-based tasks. It has been integrated into Vertex AI, Google’s fully managed machine learning platform, and is set for broader deployment in Google’s suite of products, including Search and Chrome.

3- Gemini Nano: This version is optimized for mobile devices, with two model sizes targeting different memory capacities. Gemini Nano is set to power features in Android devices, starting with the Pixel 8 Pro, providing functionalities like summarization in the Recorder app and suggested replies in messaging apps.

Capabilities and Performance

Gemini models exhibit a range of capabilities, from summarizing content and brainstorming to writing and reasoning. Comparatively, Gemini Pro outperforms previous models like OpenAI’s GPT-3.5 in various benchmarks. The models are “natively multimodal”, meaning they are trained to understand and generate content across different modalities seamlessly.

Gemini Ultra, in particular, stands out for its advanced capabilities in understanding complex subjects, especially in math and physics. Its training involved a large set of codebases, texts in multiple languages, and audio-visual materials.

Benchmarks Against GPT-4

TEXT

CapabilityBenchmark

(Higher is better)
DescriptionGemini UltraGPT-4
GeneralMMLURepresentation of questions in 57 subjects (incl. STEM, humanities, and others)90.0%

CoT@32
86.4%

5-shot (reported)
ReasoningBig-Bench HardDiverse set of challenging tasks requiring multi-step reasoning83.6%

3-shot
83.1%

3-shot (API)
DROPReading comprehension (F1 Score)82.4

Variable shots
80.9

3-shot (reported)
HellaSwagCommonsense reasoning for everyday tasks87.8%

10-shot
95.3%

10-shot (reported)
MathGSM8KBasic arithmetic manipulations (incl. Grade School math problems)94.4%

maj1@32
92.0%

5-shot CoT (reported)
MATHChallenging math problems (incl. algebra, geometry, pre-calculus, and others)53.2%

4-shot
52.9%

4-shot (API)
CodeHumanEvalPython code generation74.4%

0-shot (IT)
67.0%

0-shot (reported)
Natural2CodePython code generation. New held out dataset HumanEval-like, not leaked on the web74.9%

0-shot
73.9%

0-shot (API)

MULTIMODAL

Gemini surpasses SOTA performance on all multimodal tasks.

CapabilityBenchmarkDescription

(Higher is better unless otherwise noted)
GeminiGPT-4V

(Previous SOTA model listed when capability is not supported in GPT-4V)
ImageMMMUMulti-discipline college-level reasoning problems59.4%

0-shot pass@1
Gemini Ultra (pixel only*)
56.8%

0-shot pass@1
GPT-4V
VQAv2Natural image understanding77.8%

0-shot
Gemini Ultra (pixel only*)
77.2%

0-shot
GPT-4V
TextVQAOCR on natural images82.3%

0-shot
Gemini Ultra (pixel only*)
78.0%

0-shot
GPT-4V
DocVQADocument understanding90.9%

0-shot
Gemini Ultra (pixel only*)
88.4%

0-shot
GPT-4V (pixel only)
Infographic VQAInfographic understanding80.3%

0-shot
Gemini Ultra (pixel only*)
75.1%

0-shot
GPT-4V (pixel only)
MathVistaMathematical reasoning in visual contexts53.0%

0-shot
Gemini Ultra (pixel only*)
49.9%

0-shot
GPT-4V
VideoVATEXEnglish video captioning
(CIDEr)
62.7

4-shot
Gemini Ultra
56.0

4-shot
DeepMind Flamingo
Perception Test MCQAVideo question answering54.7%

0-shot
Gemini Ultra
46.3%

0-shot
SeViLA
AudioCoVoST 2 (21 languages)Automatic speech translation
(BLEU score)
40.1

Gemini Pro
29.1

Whisper v2
FLEURS (62 languages)Automatic speech recognition
(based on word error rate, lower is better)
7.6%

Gemini Pro
17.6%

Whisper v3

*Gemini image benchmarks are pixel only—no assistance from OCR systems.

Innovation and Limitations

A key innovation of the Gemini models is their native multimodality. Unlike conventional multimodal models that train separate components for different modalities, Gemini is designed to integrate these modalities inherently. This design enables it to perform complex conceptual and reasoning tasks more effectively.

However, Gemini also faces challenges. For instance, like other AI models, it is not immune to “hallucinating” or confidently generating incorrect information. Moreover, there are concerns regarding bias, toxicity, and the handling of non-English queries. Gemini Ultra, while advanced, only marginally outperforms existing models like GPT-4 in some benchmarks.

Environmental and Ethical Considerations

The training of large AI models like Gemini raises environmental concerns due to their significant carbon footprint. Google has not fully disclosed the environmental impact of training Gemini, nor has it addressed issues related to the creators’ rights and compensations for the training data used.

Future Prospects and Challenges

Gemini’s launch signifies Google’s stride in the generative AI race, albeit with a sense of urgency that might have compromised its full potential at the outset. While the model promises impressive multimodal capabilities and efficiency, its full capabilities, particularly in Gemini Ultra, are yet to be completely understood and utilized.

Google’s approach with Gemini highlights the complexities and challenges in developing state-of-the-art generative AI models. It remains to be seen how Gemini will evolve and how it will compete with existing models like GPT-4 in both performance and ethical considerations.

Conclusion

Google’s Gemini represents a significant step in the evolution of AI models, particularly in its approach to multimodality. While it showcases promising advancements, it also faces challenges and uncertainties that will shape its development and application in the future.

Furqan

Furqan

Amar Goel

Amar Goel

Amar is the Co-founder and CEO of Bito. With a background in software engineering and economics, Amar is a serial entrepreneur and has founded multiple companies including the publicly traded PubMatic and Komli Media.

Written by developers for developers

This article was handcrafted with by the Bito team.

Latest posts

PEER REVIEW: A New Video Podcast by Engineers, for Engineers

How Can AI Handle My Large Codebase?

Elevate Code Quality with AI: Write Clean, Maintainable Code

Identifying and Fixing Scalability Issues in Pull Requests

Identifying Security Flaws During Pull Request Reviews with AI

Top posts

PEER REVIEW: A New Video Podcast by Engineers, for Engineers

How Can AI Handle My Large Codebase?

Elevate Code Quality with AI: Write Clean, Maintainable Code

Identifying and Fixing Scalability Issues in Pull Requests

Identifying Security Flaws During Pull Request Reviews with AI

From the blog

The latest industry news, interviews, technologies, and resources.

Get Bito for IDE of your choice