Skip to main content

Performance Benchmarking

Gemini model from Google has shown to be one of the leading large language models (LLMs) in the world, which is evidenced by its performance in a variety of benchmarks. In this study, we will be exploring Gemini’s benchmark results which will show the model’s capabilities and compare its dimensionality with other successful models.

Overview of Gemini

Google DeepMind is the creator of Gemini, the LLM which processes and generates text, images, audio, and video. It is a multimodal model. The structure of this innovative AI makes it capable of dealing with difficult tasks like code generation and reasoning. It is therefore a versatile instrument in the AI domain.

Benchmark Performance

Benchmarking performance of large language models
Benchmarking performance of large language models

Several benchmarks were used to evaluate the performance of Gemini in areas of language understanding, reasoning, mathematics, coding, and multimodal tasks.

Massive Multitask Language Understanding (MMLU)

The MMLU standard strengthens a model’s competence across 57 subjects. Gemini Ultra reached 90%, which is better than the human expert’s performance of 89.8% and also GPT-4 which scored 86.4%.

Multimodal Multitask Machine Understanding (MMMU)

The MMMU will test the model’s capabilities in task-switching between multi-modal tasks that require reasoning. Gemini Ultra got the highest score of 59.4% which among the other models so far was not.(outperformed-rewrite).

Coding Benchmarks

Gemini Ultra in Python coding, got a success rate of 74.4%, to GPT-4’s 67%.

Reading Comprehension

On the skills and competencies questions, Gemini Ultra got 82.4, while GPT-4 only got 80.9.

Mathematical Reasoning

Through the MATH benchmark, which involves solving already-shown problems in the given subjects like algebra, geometry, and pre-calculus, Gemini 1.5 Pro got an 86.5% mark which indicates the high mathematical reasoning skills.

Code Generation

In the Natural2Code benchmark measuring the performance of code generation in a few programming languages, Gemini 1.5 Pro was able to score 85.4%, which according to this is a representation of how good the system is at writing accurate code snippets.

Multilingual Translation

In the language translation track of WMT23, Gemini 1.5 Pro attained a score of 75.1 due to its capabilities in the reliable multilingual translation.

Comparative Performance

TEXT:

The table below presents Gemini Ultra’s (text model) relative performance in different benchmarks to GPT-4:

Gemini Benchmarking
Performance Benchmarking: Gemini Ultra vs GPT-4

MULTIMODAL:

The table below presents Gemini multimodal model’s relative performance in different benchmarks to GPT-4:

Gemini Multimodal Benchmarking
Multimodal Performance benchmarking: Gemini vs GPT-4V

Google’s Gemini model is the newest star in the sky of AI, it has proven itself to be the champion in almost all benchmarks, often leaving models such as GPT4 far behind. Its multimodal features and enhanced reasoning play a major role in solidifying its position in the AI domain. With the advancement of AI, newer models such as Gemini, which exhibit both strength and flexibility, are setting the bar for the rest to follow.

 

0
    0
    Your Cart
    Your cart is emptyReturn to Courses