Comparison between GPT-4, GPT-3, and GPT-3.5 – What is the difference?

OverviewIntroduced in 2020, GPT-3 has 175 billion parameters, making it one of the largest language models to dateLaunched in March 2022, GPT-3.5 is an advancement of GPT-3 with improvements in sentiment analysis and elimination of toxic outputIntroduced in March 2023, GPT-4 is based on GPT-3.5 with modifications to improve performance and steerability
Parameter Counts175 billion175 billionNot specified, but improved from GPT-3.5
CapabilitiesCan generate human-like text, translate, summarize, code, write poems, and answer questionsImproved sentiment analysis with RLHF (reinforcement learning with human feedback) during fine-tuning, enabling multi-tasking abilityMore reliable, creative, and capable of handling nuanced instructions, improved steerability, and ability to refuse illegal commands
DatasetsLarge dataset (17 gigabytes) for accuracy in text generationNot specified, but includes fine-tuning with human feedback for sentiment analysisNot specified
ApplicationsText generation, coding, language translation, summarization, customer managementSentiment analysis, text generation, coding, language translation, summarizationNot specified
PerformanceScored around the bottom 10% in bar examNot specified, but improved performance compared to GPT-3.5Scored in the top 10% in bar exam, improved steerability and ability to refuse illegal commands
Visual InputsAcceptance of image inputs (for research purposes) along with text inputsNot specifiedAcceptance of image inputs (for research purposes) along with text inputs
Other FeaturesNot specifiedNot specifiedSurpassed existing large language models and state-of-the-art models in benchmarks
Famous ApplicationChatGPT, launched in November 2022ChatGPT, launched in November 2022Not specified

Overview of OpenAI’s GPT-3

Here is an overview of OpenAI’s GPT-3:

GPT-3 Parameter Counts:

Generative Pre-Training Transformer-3 (GPT-3) is a large language model with a staggering 175 billion parameter counts, making it one of the largest language models ever created.

This is a significant increase compared to its predecessor, GPT-2, which had 1 billion parameters.

GPT-3 Datasets:

The large parameter count of GPT-3 suggests that it was trained on multiple and extensive datasets, including a substantial portion of Wikipedia and books, totaling about 17 gigabytes of data.

The large dataset used for training contributes to the accuracy and capabilities of the model.

GPT-3 Capabilities:

GPT-3 is a deep learning model that is capable of generating human-like text based on predictions of forthcoming words in a sentence or phrase.

It can generate texts, translate languages, summarize text, generate code, write poems, and answer questions. The model’s capabilities are vast and versatile, making it suitable for a wide range of applications.

GPT-3 Applications:

Due to its large dataset and high parameter count, GPT-3 has found applications in various areas such as text generation, coding, language translation, summarization, and customer management.

It has been used in industries such as content generation, customer service, language processing, and more, leveraging its advanced language generation capabilities to deliver accurate and high-quality outputs.

Overall, GPT-3 is a groundbreaking language model developed by OpenAI, known for its massive parameter count, extensive training on large datasets, and wide-ranging applications in text generation and other language-related tasks.

Understanding GPT-3.5

Here are the key points about GPT-3.5:

  1. Launch date and advancements: GPT-3.5 was launched in March 2022 as an evolution of its predecessor, GPT-3. It included advancements aimed at making the model work more like a human brain and better understand human sentiments.
  2. Elimination of toxic output: One of the limitations of GPT-3 was the generation of toxic or harmful content. GPT-3.5 addressed this issue by incorporating measures to eliminate toxic output.
  3. Reinforcement Learning with Human Feedback (RLHF): GPT-3.5 utilized RLHF during fine-tuning of the large models. RLHF involves incorporating human feedback into the training process to help the model understand and evolve based on that feedback.
  4. Incorporation of knowledge and expertise: The main goal of RLHF in GPT-3.5 was to incorporate human knowledge and expertise into the model, resulting in more accurate sentiment analysis and improved multi-tasking ability.
  5. Impact on ChatGPT: ChatGPT, a popular application built on top of GPT-3.5, relies on the fine-tuning of GPT-3.5 to perform various tasks simultaneously with increased accuracy.

Overall, GPT-3.5, launched in March 2022, builds upon the strengths of its predecessor, GPT-3, and includes advancements such as RLHF to improve its ability to understand human sentiments, eliminate toxic output, and perform multi-tasking with higher accuracy.

Introduction to OpenAI’s GPT-4

OpenAI introduced GPT-4 in March 2023 as a successor to the GPT family, building on the functionality of GPT-3.5 with never-before-introduced modifications.

GPT-4 Performance:

According to OpenAI, GPT-4 has achieved significant improvements in performance compared to GPT-3.5.

It now scores in the top 10% of test aspirers on the bar exam, whereas GPT-3.5 scored around the bottom 10%. GPT-4 is designed to work in a “aligned” model where it better understands and follows users’ intentions, resulting in more accurate and less biased outputs.

Steerability is another area where GPT-4 has improved, allowing it to adjust its behavior based on users’ requests.

For example, it can change the output’s style, voice, and font according to user commands. It also has the capability to refuse illegal commands, showing better judgment and ethical decision-making.

GPT-4 Capability:

GPT-4’s capabilities shine when the complexity of the task reaches a certain threshold. OpenAI states that GPT-4 is more reliable, creative, and capable of handling nuanced instructions compared to GPT-3.5.

It has been trained on various sets of exam papers designed for humans, such as Olympiads and other question sets, and has demonstrated 40% higher efficiency compared to GPT-3.5.

GPT-4 has also been evaluated against traditional benchmarks for machine models and has surpassed existing large language models and state-of-the-art models in the field.

GPT-4 Visual Inputs:

One of the recent developments in large models is the acceptance of image inputs (for research purposes only) along with text inputs.

GPT-4 has the ability to generate text outputs based on interspersed text and image inputs, depicting a range of domains such as screenshots, pictures, or diagrams.

Overall, GPT-4 represents a significant advancement in the GPT family, showcasing improved performance, capability, and the ability to handle visual inputs, making it a powerful tool for a wide range of language generation tasks.

Analyzing the capabilities of GPT-4 and GPT-3 models

Here are the capabilities of GPT-4:

  1. Accepts visual and text inputs: GPT-4 has the ability to accept both visual and text inputs for generating textual output. This means it can analyze and interpret visual information, such as images or videos, in addition to processing text-based data.
  2. Aligned perspective for truth-oriented texts: GPT-4 is designed to avoid generating falsified information and prioritize delivering truth-oriented texts. This implies that it has been trained to provide accurate and reliable outputs, taking into consideration the alignment of perspectives with factual information.
  3. Adjusts depending on user’s command: GPT-4 has the capability to adjust its responses based on the specific commands or instructions given by the user. This allows for more personalized and tailored outputs, depending on the desired outcome or context provided by the user.
  4. Stays within guardrails to improve authenticity: GPT-4 is programmed to refuse to generate outputs that may go outside the established ethical, legal, or social boundaries. This helps in maintaining authenticity and preventing the generation of inappropriate or illegal content.
  5. Polyglot with multilingual capabilities: GPT-4 is capable of processing multiple languages, with an accuracy rate of 85% in English and the ability to speak 25 languages, including Mandarin, Polish, Swahili, and others. This makes it a versatile language-processing model for various linguistic requirements.
  6. Processing longer texts with higher context lengths: GPT-4 has the ability to process longer texts compared to previous versions, thanks to its higher context lengths. This allows it to better understand and generate text outputs that require a deeper understanding of the input data.

Overall, GPT-4 builds upon the capabilities of GPT-3 and GPT-3.5, incorporating improvements in handling visual inputs, aligning perspectives for truth-oriented texts, adjusting to user commands, staying within ethical boundaries, supporting multiple languages, and processing longer texts with higher context lengths.

Token limits in GPT-4 and GPT-3

The token limits in GPT-4 and GPT-3 determine the maximum number of tokens that can be used in a single API request. Tokens are considered as broken pieces of word processes before delivering the output.

In GPT-3, users were allowed to use a maximum of 2,049 tokens with a single API request. However, GPT-4 has different context lengths or window sizes that determine the limits of tokens that can be used in a single API request.

The GPT-4-8K window allows up to 8,192 tokens, while the GPT-4-32K window has a limit of up to 32,768 tokens, which is equivalent to approximately 50 pages of text.

This increased token limit in GPT-4 allows for more efficient generation, summarization, and translation of longer text inputs, making it suitable for handling larger documents or extensive text processing tasks.

It provides users with more flexibility and capabilities to work with longer texts and extract meaningful insights from them using the ChatGPT API.

Input types in GPT-4 and GPT-3

Here are the key differences in input types between GPT-4 and its predecessors, GPT-2, GPT-3, and GPT-3.5:

  1. Text-based input: Similar to its predecessors, GPT-4 still processes text-based input as its primary input type. This includes input in the form of sentences, paragraphs, or other text-based formats.
  2. Visual input: Unlike its predecessors, GPT-4 has introduced the capability to process visual input, such as pictures, screenshots, graphs, memes, and other visual elements. This allows GPT-4 to incorporate visual information into its model to generate more accurate textual outputs.
  3. Combined input types: GPT-4 has the ability to process both text-based and visual input types simultaneously. This means that GPT-4 can interpret visual input alongside text-based input, combining both types of information to generate its textual output.
  4. Research preview access: While visual input is accessible for research preview, it may not be publicly available for general usage. It indicates that OpenAI is actively exploring and experimenting with the integration of visual information into the GPT-4 model and its potential applications.
  5. Enhanced visual interpretation: GPT-4’s capability to recognize, understand, and interpret visual inputs allows it to deliver more accurate textual information based on the combined input types. Examples provided by OpenAI highlight the potential of GPT-4 to leverage visual input for improved text generation.

Overall, GPT-4 has expanded its input capabilities by incorporating visual input alongside text-based input, providing new opportunities for deep learning artificial intelligence and opening up possibilities for enhanced interpretation and generation of textual outputs.

Establishing the context of a conversation between GPT-4 and GPT-3

When establishing the context of a conversation between GPT-4 and GPT-3, it’s important to note the prominent difference in their capabilities to determine tone, behavior, and style based on user commands.

The newest member of the OpenAI models, GPT-4, has the ability to adjust its tone, style, and behavior depending on the command given to it. This is achieved through the “system” that governs its interactions and allows it to generate user-oriented text within certain boundaries. These boundaries ensure that GPT-4 can refuse to participate in tasks that are not allowed or are illegal.

For example, OpenAI shared a picture where GPT-4 refused to directly answer a math problem, instead encouraging users to think and solve it naturally. This showcases the model’s ability to understand context and provide responses that align with its intended purpose and ethical guidelines.

When setting the context for a conversation between GPT-4 and GPT-3, it’s important to consider the differences in their capabilities, including GPT-4’s ability to adjust its tone, behavior, and style based on commands, and its adherence to ethical boundaries. This can help guide the conversation and ensure that the models’ responses align with the desired tone and purpose of the conversation.

Cost comparison of GPT-4 and GPT-3 usage

The cost comparison between GPT-4 and GPT-3 depends on the tokens used, and it can be complex to estimate due to the varying costs of prompt tokens and completion tokens.

The cost of different GPT models is as follows:

GPT-3: $0.0004 to $0.02 per 1000 tokens GPT-3.5-Turbo: $0.002 per 1000 tokens GPT-4 with 8K context window: $0.03 per 1000 prompt (input) tokens $0.06 per 1000 completion (output) tokens

GPT-4 with 32K context window: $0.06 per 1000 prompt (input) tokens $0.12 per 1000 completion (output) tokens

Fine-tuning of OpenAI models:

During the fine-tuning process, large models like GPT-4 learn to solve specific tasks such as question answering, sentimental analysis, text generation, document summarization, etc. by training on vast examples to refine their tone, style, behavior, and personalize them for specific applications.

Once fine-tuned, the model does not require prompts and can save costs on using prompts. Currently, fine-tuning is available for GPT-3 based models in OpenAI.

Limitations and errors:

Despite being an extraordinary model with advanced capabilities, GPT-4, like its predecessors, has certain limitations and errors that need to be addressed. Some of these limitations include:


GPT-4 can generate erroneous texts and scored 40% higher than GPT-3.5 in terms of errors.

Limited information:

GPT-4, launched in 2023, does not have knowledge of events that occurred after September 2021, similar to its predecessors.

Limited changes:

GPT-4 has not changed significantly compared to GPT-3 and GPT-3.5, as stated in the product research documentation.

Key differences between GPT-3 and GPT-4: GPT-4 has advanced features such as visual input, guardrails, alignment, longer context, etc., which users can utilize.

However, it is still not suitable for everyone due to its higher cost and requirement for higher parameter sets for accurate responses.

For basic tasks, GPT-3 with smaller parameter sets may still be the preferred choice.

Here are some final thoughts on the comparison between GPT-3 and GPT-4:

  1. Improved capabilities: GPT-4, the newest version of OpenAI’s language model, includes visual inputs for better understanding and delivers text-based output. It has the ability to generate, interpret, and translate longer texts compared to the previous versions, such as GPT-3, which had limitations with shorter texts.
  2. Advancements but not revolutionary: While GPT-4 has more advanced capabilities and performs better than its predecessors, it does not necessarily represent a revolution in artificial intelligence. It still has limitations that GPT-3 and GPT-3.5 also had, but these limitations are considered fixable errors that can potentially improve with time.
  3. Higher cost of prompt and completion tokens: One factor that may limit the widespread adoption of GPT-4 is its higher cost of prompt and completion tokens. This may impact its accessibility for certain use cases or budgets compared to GPT-3, which may still be suitable for basic problems due to its lower cost.
  4. Complementary use with previous versions: While GPT-4 offers enhanced capabilities, it may not necessarily replace the previous versions of GPTs. Instead, it can be used in conjunction with GPT-3, with GPT-3 being suitable for basic problems and GPT-4 being used for more extensive problems that require its advanced features.
  5. Improved performance: GPT-4 has shown promising results in addressing the issue of “hallucinations,” with a 40% higher score compared to GPT-3.5, as per OpenAI’s internal benchmark. This indicates potential improvements in generating more accurate and reliable outputs.

In conclusion, GPT-4 represents an advancement in the capabilities of OpenAI’s language models compared to previous versions like GPT-3, but it still has limitations and considerations such as higher cost. It can be used in conjunction with previous versions, and its performance improvements make it a promising option for certain use cases.

However, it’s important to assess its suitability based on specific requirements and constraints before fully adopting it.

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