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GPT-4 and GPT-4o

GPT-4 (2023) was OpenAI's breakthrough large multimodal model that could accept images as well as text, and GPT-4o (2024) made it faster, cheaper, and natively able to handle audio, vision, and text in a single model.

2 min readLast updated

Overview

Together they defined the modern era of ChatGPT.

Deep Dive

GPT-4, released in March 2023, was a major leap over GPT-3.5: it scored in the top percentiles on exams like the bar and AP tests, handled far longer prompts, and could reason about images. GPT-4 Turbo later added a 128k-token context window and cheaper pricing. In May 2024, OpenAI introduced GPT-4o, where the 'o' stands for 'omni', a single model trained end-to-end across text, audio, and vision. Earlier voice mode chained three separate models (speech-to-text, then GPT, then text-to-speech), adding lag; GPT-4o processes audio directly, enabling near-real-time spoken conversation with emotional tone and the ability to be interrupted. It is also roughly twice as fast and half the cost of GPT-4 Turbo via the API, and OpenAI made it available to free ChatGPT users, broadening access dramatically.

Technical Insight

Both are decoder-only Transformer models trained to predict the next token, then refined with reinforcement learning from human feedback (RLHF) to follow instructions and behave safely. The crucial advance in GPT-4o is end-to-end multimodality: instead of routing speech through separate transcription and synthesis models, one network ingests and emits audio tokens directly, preserving tone, timing, and non-verbal cues while slashing latency to roughly conversational speed (a few hundred milliseconds).

Strategic Impact

Vendor strategy

Vendor roadmaps influence what features your team can build next.

Cost and budget

Commercial terms and deployment options affect long-term cost and risk.

Risk and safety

Company incentives shape product defaults, safety posture, and openness.

The Future of GPT-4 and GPT-4o

GPT-4o set the template for fluid, real-time multimodal assistants, and OpenAI's successors are pushing further into reasoning (the o-series 'thinking' models that deliberate before answering), longer context, and agentic tool use. Expect lower costs, richer real-time voice and video interaction, tighter app and device integration, and models that fluidly switch between fast responses and slow, careful reasoning depending on task difficulty. Multimodal generation, producing images and audio natively, will keep expanding.

Real-World Implementation

Having a near-real-time spoken conversation with ChatGPT's Advanced Voice Mode, including interrupting it mid-sentence

Uploading a photo of a refrigerator's contents and asking GPT-4o to suggest recipes

Pasting a long legal contract into the 128k-token context window for summarization and risk-spotting

Using the vision capability to read and explain a chart, handwritten note, or screenshot of an error message

Risks & Guardrails

Launch announcements may outpace stability in real production workflows.

API pricing or policy shifts can break assumptions overnight.

Single-vendor dependency increases lock-in and migration costs.

Implementation Roadmap

1

Evaluate providers using your own tasks and datasets.

2

Review privacy, security, and legal terms before integration.

3

Maintain a fallback plan across models or vendors.

4

Monitor release notes so roadmap changes do not surprise teams.

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Frequently asked questions

What is GPT-4 and GPT-4o?

GPT-4 (2023) was OpenAI's breakthrough large multimodal model that could accept images as well as text, and GPT-4o (2024) made it faster, cheaper, and natively able to handle audio, vision, and text in a single model. Together they defined the modern era of ChatGPT.

What does the 'o' in GPT-4o stand for?

The 'o' stands for 'omni,' reflecting that GPT-4o is a single model handling text, audio, and vision together.

Why is GPT-4o's voice mode faster than GPT-4's earlier voice feature?

Earlier voice mode chained three separate models, adding lag. GPT-4o handles audio end-to-end in a single network, cutting latency to near-conversational speed.

What major new input type did GPT-4 introduce over GPT-3.5 at launch?

GPT-4 was a large multimodal model that could accept image inputs in addition to text, a capability GPT-3.5 lacked.

What context window size did GPT-4 Turbo offer?

GPT-4 Turbo expanded the context window to 128k tokens, allowing much longer documents and conversations.

Which training technique is used to make GPT-4 and GPT-4o follow instructions and behave safely?

After next-token pretraining, the models are refined with RLHF, using human preferences to shape helpful, safe instruction-following behavior.