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GPT

/dʒiː piː tiː/noun (acronym)

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In brief · quick answer

GPT stands for Generative Pre-trained Transformer. It is a family of LLMs developed by OpenAI that popularized the modern AI boom, starting with GPT-1 in 2018 and evolving through GPT-4o and beyond.

§ 1 Definition

GPT (Generative Pre-trained Transformer) is a series of large language models created by OpenAI that established the modern paradigm of pre-training a Transformer on web-scale text, then fine-tuning for specific tasks. The acronym breaks down as: Generative (produces text), Pre-trained (trained on unlabeled data before task-specific tuning), Transformer (the neural network architecture based on self-attention). GPT-3 (2020) was a watershed moment that demonstrated scaling laws: bigger models with more data and compute predictably outperformed smaller ones. GPT-3.5 powered the original ChatGPT (2022), triggering the mainstream AI explosion. GPT-4 added multimodal capabilities and improved reasoning. GPT-4o (2024) unified text, vision, and audio processing. The GPT lineage has been the most influential model family in modern AI, though competitors (Claude, Gemini, Llama) have closed the gap significantly.

§ 2 The GPT Lineage

GPT-1 (2018): 117M parameters. Proved the viability of generative pre-training for NLP tasks. GPT-2 (2019): 1.5B parameters. OpenAI initially withheld release due to safety concerns. Demonstrated that bigger models produce qualitatively better text. GPT-3 (2020): 175B parameters. Showed scaling laws in action. Zero-shot and few-shot learning emerged as capabilities not explicitly trained for. The API product launched. GPT-3.5 / InstructGPT (2022): Fine-tuned with RLHF. Powered ChatGPT. Made LLMs useful for everyday tasks. GPT-4 (2023): Multimodal (text + image input). Improved reasoning, longer context. Passed the bar exam in the 90th percentile. GPT-4o (2024): Native multimodality (text, vision, audio). Faster, cheaper, with voice conversation capabilities.

§ 3 Architecture Details

GPT models use a decoder-only Transformer architecture. Unlike encoder-decoder models (like the original Transformer for translation), GPT has only the decoder stack. It uses causal (masked) self-attention, meaning each token can only attend to tokens before it, not after. This makes it autoregressive: it generates one token at a time, left to right. The architecture choices massive scale, decoder-only, autoregressive generation have been widely adopted beyond OpenAI, including by Meta (Llama), Mistral, and others.

§ 4 GPT as a Platform

GPT is accessible via OpenAI's API (chat completions, embeddings, assistants) and through ChatGPT (consumer product). The API follows a pay-per-token model with different pricing tiers for different models. Enterprise features include fine-tuning, custom models, data privacy guarantees, and dedicated capacity. The ecosystem includes plugins, GPTs (custom versions of ChatGPT), and a growing library of third-party integrations.

§ 5 In code

import openai

response = openai.ChatCompletion.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a technical writer."},
        {"role": "user", "content": "Explain what GPT stands for."}
    ],
    temperature=0.7,
    max_tokens=200
)
print(response.choices[0].message.content)

§ 6 Common questions

Q. Is GPT free to use?: A. ChatGPT has a free tier with limited features and rate limits. The API is pay-per-use. GPT-4o access requires a ChatGPT Plus subscription ($20/month) or API credits. There is no fully free high-performance GPT.
Q. What is the difference between GPT and ChatGPT?: A. GPT is the underlying model. ChatGPT is the consumer-facing product that uses GPT models under the hood. Think of GPT as the engine and ChatGPT as the car.
Q. Are there open-source alternatives to GPT?: A. Yes. Meta's Llama series, Mistral, Qwen, and DeepSeek are competitive open-weight models. They may not match GPT-4o on every benchmark but offer self-hosting, lower cost, and data privacy advantages.

Key takeaways

GPT = Generative Pre-trained Transformer. The most influential LLM family.
Decoder-only Transformer architecture with causal attention.
Scaling laws drove progress: more parameters, data, and compute predictably improved performance.
GPT-4o is the current flagship with native multimodal capabilities.

How Atomic Glue helps

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# GPT

GPT stands for Generative Pre-trained Transformer. It is a family of LLMs developed by OpenAI that popularized the modern AI boom, starting with GPT-1 in 2018 and evolving through GPT-4o and beyond.

Category: Ai

Author: Atomic Glue Editorial Team

## Definition

[GPT](/glossary/gpt) (Generative Pre-trained Transformer) is a series of [large language models](/glossary/large-language-model) created by OpenAI that established the modern paradigm of pre-training a Transformer on web-scale text, then fine-tuning for specific tasks. The acronym breaks down as: Generative (produces text), Pre-trained (trained on unlabeled data before task-specific tuning), Transformer (the neural network architecture based on self-attention). GPT-3 (2020) was a watershed moment that demonstrated scaling laws: bigger models with more data and compute predictably outperformed smaller ones. GPT-3.5 powered the original ChatGPT (2022), triggering the mainstream AI explosion. GPT-4 added multimodal capabilities and improved reasoning. GPT-4o (2024) unified text, vision, and audio processing. The GPT lineage has been the most influential model family in modern AI, though competitors (Claude, Gemini, Llama) have closed the gap significantly.

## The GPT Lineage

**GPT-1 (2018):** 117M parameters. Proved the viability of generative pre-training for NLP tasks. **GPT-2 (2019):** 1.5B parameters. OpenAI initially withheld release due to safety concerns. Demonstrated that bigger models produce qualitatively better text. **GPT-3 (2020):** 175B parameters. Showed scaling laws in action. Zero-shot and few-shot learning emerged as capabilities not explicitly trained for. The API product launched. **GPT-3.5 / InstructGPT (2022):** Fine-tuned with RLHF. Powered ChatGPT. Made LLMs useful for everyday tasks. **GPT-4 (2023):** Multimodal (text + image input). Improved reasoning, longer context. Passed the bar exam in the 90th percentile. **GPT-4o (2024):** Native multimodality (text, vision, audio). Faster, cheaper, with voice conversation capabilities.

## Architecture Details

## GPT as a Platform

## In code

import openai

response = openai.ChatCompletion.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a technical writer."},
        {"role": "user", "content": "Explain what GPT stands for."}
    ],
    temperature=0.7,
    max_tokens=200
)
print(response.choices[0].message.content)

## Common questions

Q: Is GPT free to use?

A: ChatGPT has a free tier with limited features and rate limits. The API is pay-per-use. GPT-4o access requires a ChatGPT Plus subscription ($20/month) or API credits. There is no fully free high-performance GPT.

Q: What is the difference between GPT and ChatGPT?

A: GPT is the underlying model. ChatGPT is the consumer-facing product that uses GPT models under the hood. Think of GPT as the engine and ChatGPT as the car.

Q: Are there open-source alternatives to GPT?

A: Yes. Meta's Llama series, Mistral, Qwen, and DeepSeek are competitive open-weight models. They may not match GPT-4o on every benchmark but offer self-hosting, lower cost, and data privacy advantages.

## Key takeaways

GPT = Generative Pre-trained Transformer. The most influential LLM family.
Decoder-only Transformer architecture with causal attention.
Scaling laws drove progress: more parameters, data, and compute predictably improved performance.
GPT-4o is the current flagship with native multimodal capabilities.

## Related entries

Last updated July 2026. Permalink: atomicglue.co/glossary/gpt