GPT-4 and GPT-4o: OpenAI's Flagship Model Family

What GPT-4 and GPT-4o Are

GPT-4 and GPT-4o are large language models developed by OpenAI that sat at the top of the company's product lineup during the mid-2020s. GPT-4, described in the GPT-4 Technical Report published on arXiv on March 14, 2023, was a Transformer-based, multimodal model that could accept both image and text inputs and produce text outputs. It represented a step up in reasoning, factuality, and instruction-following over the earlier GPT-3.5 generation that powered the first version of ChatGPT.

GPT-4o, announced on May 13, 2024, was the flagship successor in the same family. The 'o' stands for 'omni', signaling that a single model handled text, vision, and audio together. Within OpenAI's lineup, GPT-4o became the default general-purpose model in ChatGPT and a widely used API model until newer generations arrived.

As of 2026 both models had been superseded in ChatGPT by the GPT-5 family, though the broader GPT-4 line remains an important reference point for understanding how multimodal LLMs evolved. Concrete figures below are dated explicitly because model availability and pricing change frequently.

• GPT-4: released March 2023, text and image input, text output.
• GPT-4o: released May 2024, native text, vision, and audio.
• Both are proprietary models served through ChatGPT and the OpenAI API, not open-weight.

From GPT-4 to GPT-4o: The Omni Multimodal Leap

The original GPT-4 was multimodal in a limited sense: it could read images and text but could only respond in text. Voice features in ChatGPT at that time were assembled from separate components, typically a speech-to-text model feeding GPT-4 and a separate text-to-speech model voicing the reply. That pipeline added latency and discarded information such as tone, multiple speakers, or background sound.

GPT-4o changed the architecture by training one model end-to-end across text, vision, and audio, so the same neural network processes and generates each modality. According to OpenAI, GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average around 320 milliseconds, comparable to human conversational response time. This native design is what distinguishes GPT-4o from its predecessor and gives it the 'omni' label.

• GPT-4: image plus text in, text only out.
• GPT-4o: text, audio, and image in; text, audio, and image out.
• Single end-to-end model removes the multi-step voice pipeline used with GPT-4.

Key Capabilities: Reasoning, Multilingual Support, Vision, Voice

GPT-4o matched GPT-4-class performance on text and code while improving substantially on vision, audio, and non-English languages. Public benchmarks reported by OpenAI and summarized on Wikipedia placed GPT-4o around 88.7 on MMLU, a standard knowledge-and-reasoning benchmark, slightly above the earlier GPT-4 figure of roughly 86.5.

Multilingual coverage was a notable gain: GPT-4o was reported to support more than 50 languages, which OpenAI described as covering roughly 97% of speakers, alongside improved tokenization efficiency for many non-English scripts. Vision capabilities let it interpret charts, screenshots, photographs, and documents, while the native audio path enabled real-time, interruptible voice conversation in ChatGPT's Advanced Voice Mode.

• Strong text and code reasoning, broadly comparable to GPT-4.
• Vision input for images, documents, charts, and screenshots.
• Real-time voice with low latency through native audio.
• Broad multilingual support across 50-plus languages.

Context Windows and Model Variants

GPT-4 launched with two context sizes, commonly cited as roughly 8K and 32K tokens. GPT-4o expanded the standard context window to 128K tokens with a knowledge cutoff of October 2023. A smaller sibling, GPT-4o mini, arrived on July 18, 2024 with the same 128K context window and a maximum output of 16,384 tokens, positioned as a low-cost replacement for GPT-3.5 Turbo.

Alongside the GPT-4o line, OpenAI introduced a separate o-series of reasoning models, beginning with o1 and continuing with o3 and o4-mini. These models use reinforcement-learned chain-of-thought: rather than answering immediately, they spend additional inference time reasoning step by step, which improves performance on math, coding, and science at the cost of higher latency. The o-series is best understood as a complementary line for hard reasoning tasks rather than a direct replacement for GPT-4o's fast, multimodal, general-purpose behavior.

• GPT-4: roughly 8K and 32K token context variants.
• GPT-4o and GPT-4o mini: 128K token context (as of release).
• o-series (o1, o3, o4-mini): reasoning models that 'think' before answering.

How GPT-4o Was Trained and What 'Multimodal Native' Means

OpenAI has not published full architectural or parameter details for GPT-4o, and the original GPT-4 Technical Report explicitly withheld information about model size and training data. What OpenAI did state is that GPT-4o was trained end-to-end across text, vision, and audio, meaning all inputs and outputs pass through the same model rather than a chain of specialized systems.

'Multimodal native' refers to this unified training. Because audio and images are represented in the same model as text, GPT-4o can preserve signals that a transcription-based pipeline would lose, such as emotional tone, laughter, or the presence of multiple speakers, and it can generate audio and images directly. This contrasts with 'bolted-on' multimodality, where separate models are stitched together around a text-only core.

• Architecture and parameter counts are not officially disclosed.
• End-to-end training across modalities is the defining design choice.
• Native handling preserves tone, timing, and visual detail across inputs and outputs.

The GPT-4.1 and GPT-4.5 Bridge and the Move to GPT-5

Between GPT-4o and GPT-5, OpenAI shipped several intermediate models. GPT-4.5, code-named Orion, launched as a research preview on February 27, 2025 and was described by OpenAI as its largest model to that point; it was priced at a premium of 75 US dollars per million input tokens and 150 US dollars per million output tokens, far above GPT-4o's 2.50 and 10 dollars. On April 14, 2025, OpenAI released GPT-4.1, GPT-4.1 mini, and GPT-4.1 nano in the API, all supporting up to 1 million tokens of context, a large jump from the 128K of GPT-4o.

OpenAI then introduced the GPT-5 family and, in August 2025, made GPT-5 the default model in ChatGPT. The change drew user pushback from people who preferred GPT-4o's conversational style, and OpenAI temporarily restored GPT-4o for paying subscribers before retiring it. As of 2026, GPT-4o, GPT-4.1, GPT-4.1 mini, and OpenAI o4-mini were retired from ChatGPT on February 13, 2026, while remaining available for a time in the API. The current flagship is the GPT-5 family: later point releases such as GPT-5.1, GPT-5.2, and GPT-5.5 (the flagship as of mid-2026) continued to roll out through 2026. Readers should treat specific version names and dates as subject to change.

• GPT-4.5 'Orion': research preview, February 2025, premium pricing.
• GPT-4.1 family: April 2025, up to 1 million token context, API-first.
• GPT-5 became the ChatGPT default in 2025; GPT-4o was retired from ChatGPT on February 13, 2026 but stayed in the API.

Limitations: Hallucination, Cutoffs, and Cost or Latency Tradeoffs

Like all large language models in this family, GPT-4 and GPT-4o can produce confident but incorrect statements, a behavior commonly called hallucination. The GPT-4 Technical Report noted improvements in factuality over earlier models while cautioning that the model still fabricates facts and makes reasoning errors and should not be relied on without verification in high-stakes settings.

Both models also carry a fixed knowledge cutoff, October 2023 for GPT-4o, so they lack awareness of later events unless given current information through tools or retrieval. There are practical tradeoffs across the family: smaller variants such as GPT-4o mini reduce cost and latency but lose some capability, while reasoning-heavy o-series models improve accuracy on hard problems at the expense of speed and price. Choosing a model means balancing quality, latency, and cost for the task at hand.

• Hallucination remains a known failure mode requiring verification.
• Static knowledge cutoff (October 2023 for GPT-4o) limits recency.
• Larger and reasoning models cost more and respond slower than mini variants.

GPT-4o in Practice: APIs, ChatGPT, and Memory or RAG Applications

In practice, GPT-4o reached users through two main channels: ChatGPT, where it served as the default assistant and powered Advanced Voice Mode, and the OpenAI API, where developers called it for chat completions, vision tasks, and structured outputs. Its 128K context window made it suitable for summarizing long documents, analyzing images alongside text, and powering conversational agents.

A common pattern is retrieval-augmented generation (RAG), where an application retrieves relevant text from a knowledge base and supplies it to the model as context, helping address the knowledge-cutoff and hallucination limitations described above. AI memory and second-brain tools such as MemX apply this idea by storing documents, photos, and voice notes and retrieving them with semantic search so a model can answer questions in plain language. As of 2026, builders increasingly target the GPT-5 family for new work, though GPT-4o remained available in the API for existing integrations.

• Delivered via ChatGPT (including voice) and the OpenAI API.
• Well suited to long-document, vision, and conversational workloads.
• Frequently paired with RAG to ground answers in current, source-specific data.