The GPT lineage: from GPT-4 to GPT-6

OpenAI's headline models form a clear progression. Each release either widened the context window, added a modality, lowered cost, or moved reasoning from an add-on into the base model. Knowing where a given model sits in this timeline is the fastest way to understand what it can and cannot do.

Where the line is now (November 2026): GPT-4 and GPT-4o are on their way out. OpenAI's deprecations page set October 23, 2026 as the shutdown date for base gpt-4, and lists January 14, 2027 for the remaining gpt-4o snapshots. The current lineup is GPT-6 Astra ($10 / $50 per million input/output tokens), GPT-6.1 Sol ($2 / $10), GPT-6.1 Terra ($1 / $5), and GPT-6 Luna ($0.10 / $0.50), all with a 1.05M-token context window. If you read a 'GPT-4o pricing' table today, it is almost certainly stale.

Architecture: what made GPT-4 different from GPT-3

GPT-3 (2020) was a 175-billion-parameter dense model — every parameter runs on every token. GPT-4 is widely believed to be a Mixture-of-Experts model: instead of one giant network, it routes each token through a small subset of specialized 'expert' sub-networks. Reporting (never confirmed by OpenAI) puts it near 1.8 trillion total parameters with only a fraction active per token, which keeps inference cost closer to a much smaller model while preserving the knowledge capacity of a huge one.

• Mixture-of-Experts routing — only a few experts fire per token, so effective compute is far below the total parameter count
• Far stronger reasoning, coding, and instruction-following than GPT-3.5, with large gains on professional and academic exams
• A 32K context window at launch (8x GPT-3.5), later extended to 128K with GPT-4 Turbo
• Trained with large-scale RLHF (reinforcement learning from human feedback) to make outputs helpful and aligned

The practical lesson is that 'bigger' stopped meaning 'one bigger dense network.' From GPT-4 onward, frontier scale has been achieved largely through MoE — the same idea now used by Gemini, DeepSeek, Mixtral, Qwen, and most other frontier systems.

Is GPT-4o MoE or dense? The honest answer: Unknown — OpenAI has never disclosed GPT-4o's architecture, parameter count, or whether it uses expert routing. No technical report exists. What is known: it was roughly 2× faster and half the price of GPT-4 Turbo at launch, which is consistent with either a smaller dense model or a more efficient MoE — speed and price tell you about deployment economics, not architecture. Every "GPT-4o parameter count" you see online is speculation. The 1.8T MoE reporting applies to the original GPT-4 (via SemiAnalysis), and even that was never confirmed.

GPT-4o: the omnimodal leap

Before GPT-4o, multimodality was stitched together: a vision model described an image, a speech model transcribed audio, and a separate text model reasoned over the results. GPT-4o ('omni') was trained end-to-end across text, images, and audio in a single network, so it perceives and responds across modalities without handoffs. That is why GPT-4o could hold near-real-time spoken conversations, interpret screenshots, and reason over diagrams in one pass.

• Native vision — reads charts, screenshots, handwriting, and documents directly
• Native audio — low-latency voice conversations with tone and interruption handling
• Roughly half the price and twice the speed of GPT-4 Turbo at launch, which is what pushed multimodal AI into everyday consumer use
• Set the template every later frontier model followed: one model, many modalities

The reasoning turn and GPT-5

In late 2024 OpenAI split off a separate o-series (o1, then o3) that 'thinks' before answering by spending extra compute on an internal chain of reasoning. GPT-5 (August 2025) merged that capability back into the main model: a router decides per request whether to answer instantly or to reason at length, so users no longer pick a separate 'reasoning model.' GPT-5.5 continued this — adaptive reasoning is built in, and the model is tuned to use fewer tokens for the same result.

Choosing a GPT model today: OpenAI suggests starting with GPT-6 Astra for complex reasoning and coding. GPT-6.1 Sol balances intelligence and cost, GPT-6.1 Terra is the cheaper mid tier, and GPT-6 Luna suits cost-sensitive, high-volume traffic. Match the model to the task rather than defaulting to the most expensive one.

Benchmarks and key specs

Reading these numbers: Benchmark scores are vendor-reported and shift with model versions and test harnesses. The GPT-4o row reflects its 2024 launch capabilities; the GPT-5.4 and GPT-5.5 rows reflect the spring 2026 generation, and the GPT-6 rows show November 2026 list prices without a benchmark claim. Treat any single benchmark as directional and test on your own workload.

Frequently asked questions

Is GPT-4o still available? Only for now. OpenAI has deprecated it, its May 2024 snapshot was scheduled to shut down on October 23, 2026, and the remaining API snapshots are listed for January 14, 2027, with GPT-6 Luna as the recommended replacement. Some platforms may expose legacy access for a while, but new work should start on the GPT-6 family.

What is the difference between GPT-4 and GPT-4o? GPT-4 (2023) was the original frontier model; GPT-4o (2024) is a single natively multimodal model that handles text, image, and audio in one network, runs faster, and cost roughly half as much at launch.

Was GPT-4 really 1.8 trillion parameters? That widely cited Mixture-of-Experts figure has never been confirmed by OpenAI. It is informed speculation, not an official spec — treat it as such.

Which GPT model should I use today? Use GPT-6 Astra for the hardest reasoning and coding, GPT-6.1 Sol as the balanced default, GPT-6.1 Terra when you want a lower price than Sol, and GPT-6 Luna for high-volume traffic.