GPT models 2026 explained: From GPT-1 to GPT-5.6

GPT is one of the most searched model families in AI, and also one of the most confusingly named. OpenAI has shipped dozens of versions, tiers, and product surfaces since 2018 — GPT-1 through GPT-5.6, Instant through Pro, and now Sol, Terra, and Luna. But with so many names stacked on top of each other, how do you actually tell them apart?

This guide maps the whole lifecycle — the history, the tiers, the timeline, and how GPT compares to the alternatives. The simplest way through it: GPT is the model family. ChatGPT is the product.

What is GPT?

GPT stands for Generative Pre-trained Transformer. It is OpenAI’s family of large language models, developed using the Transformer architecture and trained to generate and understand language and other token-based inputs. GPT models power products such as ChatGPT and are also available through the OpenAI API.

The three parts of the name describe the technology:

• Generative: The models generate outputs rather than simply classifying inputs.

• Pre-trained: A model first learns broad patterns from large datasets before being adapted for tasks such as instruction following, reasoning, coding and tool use.

• Transformer: The models use the Transformer neural-network architecture and attention mechanisms to model relationships between tokens.

Modern GPT systems are considerably more capable than the early language models from which the family originated. Improvements have included multimodal capabilities, stronger reasoning, tool use, coding, agentic workflows and more sophisticated post-training. For a broader explanation of the terminology, see Toloka’s guide to the difference between AI, ML, LLM and generative AI.

GPT versus ChatGPT

GPT is the underlying model family. ChatGPT is the product that lets people use those models with added features like reasoning controls, files, web access and tools.

A developer can call a GPT model through the OpenAI API without using ChatGPT. A ChatGPT user, meanwhile, interacts with a product experience that may include models, reasoning controls, files, web access, tools and plan-based limits.

This distinction becomes especially important when looking at ChatGPT models explained versus an OpenAI models list. A model available through the API is not necessarily a model that a ChatGPT user can select.

A useful way to think about the difference is:

A generation is not a product plan. A capability tier is not a reasoning setting. And an API model is not automatically a ChatGPT model.

A short history of GPT

GPT-1 — 2018

OpenAI introduced its first GPT model in 2018 with a new approach to language understanding: generative pre-training followed by task-specific fine-tuning. The model was first trained on large amounts of unlabeled text, allowing it to learn broad language representations before being adapted to individual language-understanding tasks.

The significance of GPT-1 was that it demonstrated how a generatively pre-trained language model could transfer what it had learned to a range of downstream tasks, establishing the basic pre-training approach that later GPT generations would scale dramatically.

GPT-2 — 2019

GPT-2 demonstrated what happened when the GPT approach was scaled. Its largest version reached 1.5 billion parameters and generated unusually coherent text, demonstrating how much the approach could improve when scaled. OpenAI initially staged the model’s release because of concerns about potential malicious uses of increasingly capable language generation.

GPT-2 also showed that a single language model could perform tasks such as question answering, summarisation and translation directly from patterns learned during pre-training, without task-specific training. This helped establish scaling and increasingly general-purpose language generation as a central direction for the GPT family.

GPT-3 — 2020

GPT-3 represented another major increase in scale, reaching 175 billion parameters. Its most important contribution was demonstrating how scaling could improve few-shot learning: GPT-3 could perform new tasks from instructions or examples supplied in the prompt, without gradient updates or task-specific fine-tuning.

This approach is now commonly described as in-context learning. Instead of training a separate model for every task, users could specify the task through natural-language instructions and examples within the prompt itself. GPT-3 therefore marked an important step toward the general-purpose language models that would later underpin conversational systems such as ChatGPT.

ChatGPT and GPT-3.5 — 2022

ChatGPT brought GPT-based conversational AI to a mass audience. Its November 2022 launch transformed public awareness of generative AI and established ChatGPT as the product through which millions of people first encountered GPT technology. It also created a naming problem that still exists today: GPT and ChatGPT began being used as though they were interchangeable terms, which is not the case.

GPT-4 — 2023

GPT-4 represented a major upgrade in capability over GPT-3.5. It was designed to accept text and image inputs and produce text outputs, although image-input capability was initially presented as a research preview rather than being broadly available at launch. GPT-4 also demonstrated how the GPT family was moving beyond simple text generation toward more capable multimodal systems.

GPT-4o — 2024

GPT-4o pushed OpenAI’s multimodal direction further. The “o” refers to omni, reflecting a model designed to work across combinations of text, audio, image and video inputs and to support richer multimodal interaction. GPT-4o represented an important shift from thinking about AI primarily as a text interface toward treating it as a general-purpose multimodal system.

The o-series — 2024–2025

OpenAI also developed a separate line of reasoning-focused models. The o-series, beginning with o1 and later including models such as o3 and o4-mini, focused on giving models more deliberate computation for difficult problems. This created a distinction between:

• General-purpose GPT models.

• Multimodal models such as GPT-4o.

• Reasoning-focused o-series models.

The later GPT-5 generation moved toward a more unified experience that could combine fast responses, deeper reasoning and routing.

GPT-5 — 2025

GPT-5 marked a move toward a unified system rather than requiring users to navigate an ever-growing collection of separate model families. OpenAI described GPT-5 as a system incorporating a fast model, a deeper reasoning model and a real-time router. Importantly, “unified” does not mean that every capability is necessarily produced by one single neural network. The user experience is unified while different models and routing mechanisms can operate underneath (GPT-5 system card).

GPT-5.1 and GPT-5.2 — 2025

GPT-5.1 continued the GPT-5 generation, with improvements to the ChatGPT experience and greater customisation of how the model communicates. GPT-5.2 expanded the GPT-5 generation across ChatGPT, the API and specialised coding workflows. The generation subsequently continued through GPT-5.3-Codex, GPT-5.4 and GPT-5.5.

GPT-5.3-Codex — 2026

GPT-5.3-Codex combined the coding capabilities of GPT-5.2-Codex with the reasoning and professional-knowledge capabilities of GPT-5.2, running roughly a quarter faster than its predecessor. At launch, OpenAI described it as the “most capable agentic coding model to date.” It was designed for complex, agentic software-development workflows involving coding, research and tool use. GPT-5.3-Codex should not be treated simply as another general-purpose GPT number — it is part of OpenAI’s coding-focused Codex product and model line.

GPT-5.4 — 2026

GPT-5.4 brought together advances in reasoning, coding and agentic workflows. It incorporated frontier coding capabilities developed in GPT-5.3-Codex and extended the GPT-5 generation across ChatGPT, Codex and the API. Smaller GPT-5.4 mini and nano variants, released separately on March 17, 2026, extended the generation toward production workloads where throughput and cost matter.

GPT-5.5 — April 2026

GPT-5.5 was introduced as a model for complex real-world work, including coding, research, data analysis, document creation, software operation and tool use. It continued the broader GPT-5 direction toward models that can do more than generate an answer: they can participate in longer, multi-step workflows.

GPT-5.6 — June and July 2026

GPT-5.6 entered limited preview in June 2026 and became generally available on July 9, 2026. The generation introduced three durable capability tiers:

• Sol: The flagship tier.

• Terra: A balanced tier for everyday and professional work.

• Luna: A fast, cost-efficient tier for high-volume workloads.

This changed how GPT names communicate information. The number identifies the generation, while the name identifies the capability tier within that generation. OpenAI describes Sol, Terra and Luna as durable tiers that can advance independently rather than simply being three fixed versions of the same model.

How GPT-5.6 names work

GPT-5.6, Sol, Instant and Pro do not describe the same thing. GPT-5.6 is a generation, Sol is a capability tier, Instant is a ChatGPT response mode and Pro is a higher-compute product option.

Current GPT lineup

The Instant and Thinking levels

As of August 2026, ChatGPT presents GPT-5.6 through a combination of response modes and reasoning levels rather than separate model generations. These controls determine how ChatGPT balances response speed against the amount of reasoning applied to a request.

On eligible paid plans, ChatGPT can automatically use more reasoning for complex requests while Instant is selected. Users can also choose a reasoning level directly from the model picker, although the options available depend on the plan and, for managed workspaces, workspace settings. Free and Go users receive GPT-5.6 Luna and do not have access to GPT-5.6 Sol in standard ChatGPT.

OpenAI refined this setup on August 6, 2026: a single updated GPT-5.6 Sol now powers both Instant responses and deeper reasoning for Plus and Pro users, with a reasoning-effort slider replacing separate model choices. OpenAI reported the updated Sol makes about 68 percent fewer factual errors than GPT-5.5 Instant on fact-dependent prompts. Free and Go users got GPT-5.6 Luna as their default model in the same rollout, along with unlimited text chats and a Think button that gives Luna more reasoning time on harder questions. Notably, Think raises Luna’s effort; it does not route free users to Sol.

Pro

Pro is not another GPT generation — it’s a higher-compute ChatGPT configuration. Sol is the capability tier. Pro is the product option, and Sol Pro is the specific configuration that powers it, designed for difficult tasks and longer-running workflows.

The GPT-5.6 frontier: Sol, Terra and Luna

Sol is the flagship tier, OpenAI’s frontier model for complex professional work, including reasoning and coding. Terra is the balanced tier, designed to provide strong performance at a lower cost. Luna is the efficient tier, optimized for cost-sensitive, high-volume workloads.

These aren’t three quality settings on the same model. They are distinct GPT-5.6 models within one generation, designed to occupy different points on the capability, speed and cost curve.

Terra and Luna aren’t selectable in standard ChatGPT conversations. In ChatGPT Work, eligible paid plans can access Sol, Terra and Luna. Codex provides Terra to Free and Go users, while eligible paid plans can access all three. The OpenAI API exposes all three models.

API variants: Mini and nano

The OpenAI API exposes a broader catalogue than ChatGPT, including GPT-5.6 Sol, Terra and Luna alongside GPT-5.5, GPT-5.4, GPT-5.4 mini, GPT-5.4 nano, GPT-5.3-Codex and older or deprecated models. For direct access, the OpenAI API model directory is the authoritative, current source for deployment decisions.

Mini and nano are smaller, deployment-oriented variants tied to specific generations rather than automatic GPT-5.6 tiers. GPT-5.4 mini is designed for coding, computer use and subagent workloads, while GPT-5.4 nano is aimed at simpler, high-volume tasks. Model names, prices, context limits and retirement dates change often, so check the live catalogue before deployment rather than hard-coding a price or specification into production documentation.

Codex

Codex is OpenAI’s coding-focused product and model line, built for software-engineering and agentic coding workflows where a model reasons through a problem, inspects a codebase, uses tools and works through multiple steps.

GPT-5.3-Codex was a major step in this direction. GPT-5.4 subsequently brought advanced coding capability into the general GPT line, while Codex remains a distinct coding-focused product and workflow. Codex therefore shouldn’t be read as simply another GPT number — it is a specialised product and model line in its own right.

How the GPT tiers compare

Don’t read this as one simple ranking. Instant through Extra High describe the ChatGPT experience and reasoning effort; Pro is a higher-compute ChatGPT configuration; Sol, Terra and Luna are distinct GPT-5.6 capability tiers; mini and nano are smaller API variants tied to specific generations; and Codex is a separate coding-focused product line.

The right choice depends on the task’s need for capability, reasoning depth, tool use, reliability, latency, cost and how much human review the output will need. The cheapest model per call isn’t necessarily the cheapest to operate if its output requires significantly more correction.

GPT versus other model families

No single model family wins every workflow. GPT can be a strong fit where a team needs a broad product ecosystem, coding support, tool use and integration across ChatGPT, Codex and the API. Claude, Gemini, Grok and open-weight models may be preferable depending on writing quality, long-context behaviour, multimodal capability, deployment control, cost or data-governance needs.

The frontier is closely contested, and model performance can vary substantially by task and evaluation. A claim that one model is “best” is meaningful only when the exact model version, task, benchmark, configuration and date are specified. This is often framed as a GPT vs Claude or GPT vs Gemini question, but a winner-take-all answer isn’t useful without that context.

For a broader comparison, see Toloka’s LLM leaderboard overview. For live standings under exactly those controlled conditions, Toloka Arena tracks GPT-5.6 tiers alongside Claude and Gemini models on private, non-contaminated agentic benchmarks, scored by pass^5 reliability and cost per task — because the tasks are never in any model’s training data, the standings show how these families behave on unseen work rather than on memorized tests. For the strategic trade-offs between proprietary and open models, see the guide to open-source versus closed-source LLMs. For a deeper look at Anthropic’s model family, see the Claude models article [link once live — Claude family article not yet published].

What’s next for GPT

GPT development is likely to continue toward longer-running tasks, more tool use and increasingly complex professional and agentic workflows. The GPT-5.6 family already separates capability tiers into Sol, Terra and Luna, allowing OpenAI to position models differently for capability, cost and scale. Whether future GPT generations retain this naming structure is not known and should not be treated as an announced product plan.

Where the data comes from

Every frontier model, GPT included, depends on human data: RLHF and preference data for alignment, expert annotation for hard domains, and human judgment for evaluation and red-teaming. As models become increasingly capable, the quality of training and evaluation data becomes an important part of turning raw model capability into dependable performance.

Toloka’s role is providing expert human data for training, fine-tuning and evaluating models of any size — building representative evaluation sets, collecting expert judgements and supporting AI safety work like red-teaming, across large language models and other modalities. Teams that need evaluation material models have never seen can also license non-contaminated RL and evaluation datasets across industry domains. This article makes no claim that Toloka worked on any specific vendor’s model — publicly announced models are discussed on the basis of public information only.

Conclusion

The GPT lineage has come a long way from the simple GPT-1 → GPT-4 sequence. Today it has several distinct layers: GPT-5.6 identifies the generation; Sol, Terra and Luna identify the three models within that generation; Instant through Pro describe the ChatGPT experience and reasoning configuration; mini and nano describe smaller API variants tied to specific generations; and Codex is a separate coding-focused product line. Once those distinctions are clear, the lineup stops being confusing.

The useful question isn’t “what’s the newest GPT model” — it’s which model, capability level and reasoning configuration fit the work at hand. And for any team building on top of these models, the model is only part of the answer: high-quality human data and evaluation are what turn raw capability into dependable performance.

Frequently asked questions

What is the GPT model family?

The GPT model family is OpenAI’s lineage of Generative Pre-trained Transformer models, part of the broader history of large language models. It runs from GPT-1 through GPT-2, GPT-3, GPT-4, GPT-5 and GPT-5.6, with each generation containing different model variants, product integrations and capability tiers.

What is the difference between GPT and ChatGPT?

GPT is the underlying model family; ChatGPT is the product people use to interact with those models, alongside tools and other features. Developers can call GPT models through the OpenAI API without using ChatGPT at all, while ChatGPT users interact with a model chosen according to their plan and the control they select.

What are Instant, Medium, High, Extra High and Pro in ChatGPT?

These are ChatGPT response modes and reasoning configurations, not separate GPT generations. On eligible paid plans, Instant uses GPT-5.6 Sol, while Medium, High and Extra High use GPT-5.6 Sol at increasing reasoning effort. Pro uses GPT-5.6 Sol Pro. Free and Go users receive GPT-5.6 Luna, including for Think.

What do Sol, Terra and Luna mean in GPT-5.6?

They’re GPT-5.6’s three model tiers: Sol is the flagship model for complex professional work, Terra balances intelligence and cost, and Luna is optimized for cost-sensitive, high-volume workloads. The names describe model positioning within the GPT-5.6 generation, not ChatGPT subscription plans.

How does GPT compare to Claude and Gemini?

No model family wins every task. Meaningful comparisons require the exact model, date, benchmark, configuration and source behind the claim. Test representative workloads from your own use case (accuracy, reliability, latency, cost, tool use and required human review) rather than relying on a general ranking. Toloka Arena’s domain standings are a practical starting point: they compare GPT, Claude and Gemini models on private tool-use tasks by pass^5 reliability, and rankings differ by domain, so check the domain closest to your own workload rather than the composite score alone.