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GPT-4 is a deep learning model developed by OpenAI that is capable of accepting both image and text inputs and emitting text outputs. It has achieved human-level performance on various professional and academic benchmarks, although it is less capable than humans in many real-world scenarios.

GPT-4 is a large multimodal model that has been trained on a vast corpus of data in order to learn to generate coherent and contextually appropriate text in response to various inputs.

It has been designed to be more reliable, creative, and able to handle much more nuanced instructions than its predecessor, GPT-3.5. GPT-4's capabilities have been tested on a variety of benchmarks, including simulating exams that were originally designed for humans.

GPT-4 has also been evaluated on traditional benchmarks designed for machine learning models, where it considerably outperforms existing large language models alongside most state-of-the-art models.

GPT-4's text input capability is currently available via ChatGPT and the API, with the image input capability being prepared for wider availability through collaboration with a single partner.

OpenAI has also open-sourced OpenAI Evals, a framework for automated evaluation of AI model performance, to allow anyone to report shortcomings in their models to help guide further improvements.


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Dec 31, 2023
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GPT-4 was manually vetted by our editorial team and was first featured on March 14th 2023.
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25 alternatives to GPT-4 for Large Language Models

Pros and Cons


Accepts both image and text inputs
Emits text outputs
Achieves human-level performance on benchmarks
Large multimodal model
Trained on vast corpus of data
Generates coherent and contextually appropriate text
Capable of nuanced instruction handling
Capabilities tested on human-designed exams
Outperforms existing large language models
Available via API
Open-sourced evaluation framework
Improved reliability from predecessor
Exhibits enhanced creativity
Built for scalability
Capable of simulating professional tests
Tested on traditional machine learning benchmarks
Better performance on low-resource languages
Used internally for support, sales, content moderation
Significant reduction in hallucination
Improved scores on adversarial factuality evaluations
Scored highly on TruthfulQA benchmark
Can handle complex tasks
Augmentable with test-time techniques
Gives more accurate prediction in training performance
Supports customization of user experience
Outperforms state-of-the-art models on benchmarks
Shows impressive capability in multiple languages


Limited reliability
Hallucinates facts
Prone to reasoning errors
Strictly requires careful usage
Limited in high-stakes contexts
Issues with hallucinations
Inaccurate in factual evaluation
Poor performance on TruthfulQA


What is GPT-4?
What is the difference between GPT-4 and its predecessor, GPT-3.5?
What is the performance of GPT-4 on professional and academic benchmarks?
Can GPT-4 process both image and text inputs?
What is the ChatGPT and API in context of GPT-4's capability?
Is GPT-4 available for wider availability?
What is the role of OpenAI Evals in GPT-4's development?
What is a 'multimodal model' in reference to GPT-4?
How was GPT-4 trained?
What exams has GPT-4 simulated?
What is GPT-4's relationship with traditional machine learning benchmarks?
How does GPT-4 handle nuanced instructions?
What are the improvements in GPT-4 over previous models?
Is GPT-4 being used in real-world applications?
What about GPT-4's image input capability?
How does GPT-4's performance compare on English vs other language benchmarks?
What is the role of Azure in GPT-4's development?
How does GPT-4 handle conversation steering?
What is the function of system messages in GPT-4?
What limitations does GPT-4 have?


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