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What is GPT-4 and how does it compare to GPT-3 and other language models?

In artificial intelligence, the latest breakthroughs in language modeling have brought forth a new contender: GPT-4. Building upon the success of its predecessor, GPT-3, GPT-4 promises to push the boundaries of natural language processing even further. But what exactly is GPT-4, and how does it stack up against GPT-3 and other language models? In this article, we will learn what is GPT-4, explore its advancements, and capabilities, and how it compares to its predecessors and other leading language models.

Language models are computer programs designed to generate natural language text based on a given input, such as a single word, a phrase, or a prompt. These models have found wide application in various fields, including chatbots, text summarization, machine translation, and content creation.

What is GPT-3?

Since its release in May 2020, GPT-3 has garnered significant acclaim as a breakthrough in the field of natural language processing (NLP). With a staggering 175 billion parameters, which determine how the neural network processes input and output, GPT-3 holds the distinction of being the largest language model ever created. In fact, it surpasses its predecessor, GPT-2, which had 1.5 billion parameters, by more than 100 times.

GPT-3 boasts the ability to generate coherent and diverse text on nearly any topic when provided with a suitable prompt. It can also perform various NLP tasks, including answering questions, writing essays, composing emails, creating headlines, and even generating computer code. However, GPT-3 is not without limitations and challenges. Some of these include:

  1. Computing Power and Energy Consumption: Training and running GPT-3 requires substantial computational resources and energy.

  2. Inaccurate or Misleading Output: If the input is vague or biased, GPT-3 may produce inaccurate or misleading information.

  3. Offensive or Harmful Content: When presented with a malicious or inappropriate prompt, GPT-3 may generate offensive or harmful content.

  4. Lack of Common Sense or Factual Knowledge: If the underlying data it learned from is incomplete or inconsistent, GPT-3 may lack common sense or factual knowledge.

  5. Control and Interpretability: Due to its black-box nature, it can be challenging to control or interpret GPT-3's output.

These challenges have prompted researchers and developers to seek ways to enhance language models, addressing their drawbacks. One highly anticipated development in this regard is GPT-4, the upcoming version of GPT.

What is GPT-4?

On March 14, 2023, OpenAI made a groundbreaking announcement with the introduction of GPT-4, a remarkable multimodal language model. Unlike its predecessors, GPT-3 and GPT-3.5, GPT-4 expands its scope beyond text by incorporating image inputs, enabling a more comprehensive understanding of data.

As the latest milestone in OpenAI's continuous efforts to advance deep learning and create increasingly sophisticated language models, GPT-4 sets new benchmarks for performance and capability. It demonstrates human-level proficiency in various professional and academic domains, including achieving impressive scores on simulated bar exams, outperforming a significant portion of test takers.

Built on the same deep learning foundation as GPT, GPT-2, and GPT-3, GPT-4 employs the transformative power of the Transformer neural network architecture. However, GPT-4 stands out with its substantial size and enhanced capabilities, boasting an astounding 1.5 trillion parameters (compared to GPT-3's 175 billion). This tremendous capacity allows GPT-4 to process both text and image inputs, revolutionizing its versatility.

GPT-4 operates by generating outputs, be it text or images, based on the inputs it receives. It excels at a myriad of tasks, ranging from creative and technical writing collaborations to describing humor in images, summarizing text from screenshots, and even answering exam questions with diagrams.

Central to GPT-4's success is its utilization of self-attention, a technique that enables the model to discern relevant information across different input and output components. By focusing on the most pertinent details, GPT-4 generates coherent and consistent outputs. Additionally, GPT-4 employs autoregressive generation, generating outputs token by token, using prior tokens as context for each subsequent generation step.

Notable Pros and Features of GPT-4:

GPT-4 brings forth several remarkable benefits and features, including:

  1. Enhanced Reliability and Creativity: GPT-4 surpasses its predecessor, GPT-3.5, in terms of reliability and creativity, enabling it to handle more nuanced instructions effectively.

  2. Aligned and Safer Approach: With increased human feedback and collaborations with experts in AI safety and security, GPT-4 demonstrates improved alignment and safety compared to GPT-3.5.

  3. Versatility and Flexibility: GPT-4's multimodal capability and expanded general knowledge empower it with broader problem-solving abilities, making it a versatile and flexible language model.

  4. Accessible and Collaborative: Through the release of its text input capability via ChatGPT and the API (with a waitlist), along with collaboration with a single partner for image input capability, GPT-4 fosters accessibility and collaborative potential.

GPT-4 marks a significant milestone in the evolution of language models, combining text and image understanding to unlock new possibilities in natural language processing. With its groundbreaking capabilities and advancements, GPT-4 is poised to shape the future of AI-driven language processing and empower diverse industries and applications.

The Difference: GPT-3 vs GPT-4





175 billion

Trillions (estimated)


Unimodal (Text only)

Multi-modal (Text and Images)

Data Sources

Text from the internet

Text, images, videos, audio, and structured data from the internet


Good at generating coherent and diverse texts, but prone to errors, biases, and inconsistencies

Better at generating accurate, creative, and reliable texts, with improved reasoning and learning abilities


Can perform various NLP tasks, such as answering questions, writing essays, composing emails, creating headlines, and generating code

Can perform more complex and diverse tasks that require combining text and image modalities, such as captioning, summarizing, or translating images


Uses a transformer architecture with an attention mechanism to learn from a large corpus of text data

Uses a similar architecture but with more human feedback and guidance to fine-tune the model for specific domains and tasks


Scores poorly on professional and academic benchmarks, such as a simulated bar exam or a biology olympiad

Scores well on the same benchmarks, exhibiting human-level performance or better


Has potential applications and implications for various domains and scenarios, but also poses risks and challenges for ethics and safety

Has more potential applications and implications for various domains and scenarios, but also poses more risks and challenges for ethics and safety

GPT-4 vs other Languages Models

GPT-4 is based on the same deep learning approach as its predecessors, GPT, GPT-2, and GPT-3, which use a neural network architecture called Transformer to learn from large amounts of text data. However, GPT-4 is much larger and more powerful than its predecessors, with 1.5 trillion parameters (compared to 175 billion for GPT-3) and the ability to process both text and image inputs.

How does GPT-4 compare with other languages in 2023?

One of the main challenges of developing large language models (LLMs) is to make them work well across different languages and domains. Most of the machine learning data and information on the internet today is in English, so training LLMs in other languages can be challenging.

GPT-4 is better at understanding languages that are not English than GPT-3.5 and other LLMs. According to a study by OpenAI researchers, GPT-4 exceeds the English-language performance of GPT-3.5 and other LLMs (Chinchilla, PaLM) in 24 of the 26 languages examined, including low-resource languages like Latvian, Welsh, and Swahili.

GPT-4 is also better at adapting to different domains than GPT-3.5 and other LLMs. According to another study by OpenAI researchers, GPT-4 outperforms GPT-3.5 and other LLMs (BART, T5) on domain adaptation tasks such as summarizing news articles, generating product reviews, and answering trivia questions.

GPT-4 is not perfect, however. It still struggles with some aspects of natural language understanding, such as common sense reasoning, world knowledge, and linguistic diversity. It also faces some ethical and social challenges, such as bias, fairness, privacy, and misuse.

GPT-4 is a remarkable achievement in the field of artificial intelligence, but it is not the end of the road. OpenAI plans to continue improving GPT-4 and making it more accessible and collaborative for users. It also hopes to inspire more research and innovation in multimodal models and language understanding.



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