GPT Models Cheat Sheet

Updated 2026-04-28

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GPT (Generative Pre-trained Transformer) models are large language models developed by OpenAI that use transformer architecture to generate text, analyze images, process audio, and perform complex reasoning. The lineup has expanded dramatically: the current flagship, GPT-5.5 (released April 2026), delivers 1M-token context and native computer-use capabilities, while the GPT-4.1 family offers a 1M-token non-reasoning alternative, and open-weight models (gpt-oss) are now available under Apache 2.0. OpenAI's new Responses API has replaced Chat Completions as the recommended primitive for agent-style workloads, bringing stateful context, built-in tools, and MCP server support into a single call.

What This Cheat Sheet Covers

This topic spans 14 focused tables and 140 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Core ArchitectureTable 2: Model Variants and SpecificationsTable 3: Tokenization and ContextTable 4: API Parameters and ConfigurationTable 5: API Endpoints and IntegrationTable 6: Agentic Tools and Built-in FeaturesTable 7: Multimodal CapabilitiesTable 8: Fine-tuning and CustomizationTable 9: Decoding and Generation StrategiesTable 10: Model Selection and Use CasesTable 11: Performance and OptimizationTable 12: Pricing and Cost ManagementTable 13: Open-Weight ModelsTable 14: Limitations and Considerations

Table 1: Core Architecture

Component	Example	Description
Transformer architecture	Multi-head self-attention + feed-forward layers	Foundation of all GPT models — processes token sequences in parallel, enabling efficient long-range dependency modeling.
Self-attention mechanism	Query-Key-Value matrices compute token relationships	• Each token attends to all others, assigning importance weights based on semantic relevance • enables context-aware representations.
Autoregressive generation	Predicts token $t_{n+1}$ given $t_1, t_2, ..., t_n$	Generates text one token at a time, conditioning each prediction on all previously generated tokens for coherent outputs.
Multi-head attention	8–96 attention heads process different aspects simultaneously	Parallel attention mechanisms allow the model to focus on multiple representation subspaces (syntax, semantics, entities) at once.
Pre-training + fine-tuning	Unsupervised learning on web text → RLHF alignment	Two-stage process: model learns language patterns from massive datasets, then human feedback refines outputs for helpfulness and safety.

Table 1: Core Architecture

Component	Example	Description
Transformer architecture	Multi-head self-attention + feed-forward layers	Foundation of all GPT models — processes token sequences in parallel, enabling efficient long-range dependency modeling.
Self-attention mechanism	Query-Key-Value matrices compute token relationships	• Each token attends to all others, assigning importance weights based on semantic relevance • enables context-aware representations.
Autoregressive generation	Predicts token $t_{n+1}$ given $t_1, t_2, ..., t_n$	Generates text one token at a time, conditioning each prediction on all previously generated tokens for coherent outputs.
Multi-head attention	8–96 attention heads process different aspects simultaneously	Parallel attention mechanisms allow the model to focus on multiple representation subspaces (syntax, semantics, entities) at once.
Pre-training + fine-tuning	Unsupervised learning on web text → RLHF alignment	Two-stage process: model learns language patterns from massive datasets, then human feedback refines outputs for helpfulness and safety.

GPT Models (GPT-4, GPT-5) Cheat Sheet

Table 1: Core Architecture

GPT Models (GPT-4, GPT-5) Cheat Sheet

Table 1: Core Architecture