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GPT-4 is a large language model developed by OpenAI and released in March 2023 as part of the Generative Pre-trained Transformer series. It accepts text input and produces text output, with a context window of 8,192 tokens, and its training data has a knowledge cutoff of April 2023. GPT-4 was designed to improve on earlier GPT models in areas such as instruction following, contextual understanding, and factual accuracy across a wide range of topics.

GPT-4o is a multimodal language model developed by OpenAI, released in May 2024. The "o" stands for "omni," reflecting its ability to accept any combination of text, audio, and image as input and generate any combination of those same modalities as output. It has a 128,000-token context window and a training data cutoff of October 2023.

GPT-5.4 is a text generation model developed by OpenAI, released in March 2026 as their flagship model for professional and enterprise use. It is available in three variants — standard, Thinking, and Pro — and features a context window of 1 million tokens, the largest OpenAI has offered. The model is designed not only to plan complex tasks but to complete them reliably, with built-in computer use capabilities for orchestrating multi-step agentic workflows.

GPT-5.4 Pro is a text generation model developed by OpenAI, released in March 2026 as part of the GPT-5.4 family. It is one of three variants in that family — alongside the standard GPT-5.4 and GPT-5.4 Thinking — and is specifically optimized for deep analytical work through multi-path reasoning evaluation. The model supports a context window of 1 million tokens, the largest OpenAI has offered, enabling it to process extensive documents, codebases, and multi-step workflows within a single session.

Compare GPT 5.4 and GPT 5.4 Pro across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.

Compare GPT 5.5 and GPT 5.4 across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.