GPT models explained: ChatGPT models from GPT-1 to GPT-6.1

Updated October 2026

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-6.1, Instant through Pro, and now Astra, Sol, Terra and Luna. 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 current ChatGPT models, how the GPT-6 tiers compare on independent benchmarks, 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, computer use, 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, and even inside ChatGPT, the Chat tab, ChatGPT Work and Codex offer different model lists.

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, GPT-5.5 and GPT-5.6.

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.

OpenAI refined the ChatGPT setup on August 6, 2026: a single updated GPT-5.6 Sol powered 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 made 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, along with a Think button that gives Luna more reasoning time on harder questions.

Independent testing told a more mixed story. Toloka’s study GPT-5.6 got smarter. Then it kept acting. found that GPT-5.6 reasoned better than GPT-5.5 on agentic tasks but made more unnecessary state changes along the way.

GPT-6 Astra — September 2026

OpenAI introduced GPT-6 Astra on September 3, 2026 as its new flagship, describing it as its most capable and most aligned model, with leading results claimed in coding, math and operating computers and web browsers. Access started with a small set of organizations before widening to paid users.

Astra was also the first OpenAI model to reach the “Critical” cybersecurity threshold in OpenAI’s safety evaluations, so the version available in ChatGPT refuses some requests, such as certain cybersecurity tasks. In the API, Astra is priced at $10 per million input tokens and $50 per million output tokens, several times above the rest of the family.

GPT-6 Sol and GPT-6 Luna — September 2026

On September 22, 2026, OpenAI released GPT-6 Sol and GPT-6 Luna, trained with methods similar to Astra but tuned for a better balance of cost and capability. Sol targets complex professional work and coding with higher usage limits, while Luna is the cheapest option for high-volume tasks.

API prices were set at $2 / $10 for Sol and $0.10 / $0.50 for Luna per million input / output tokens, half of what the equivalent GPT-5.6 models cost. Both arrived first in ChatGPT Work, Codex and the API, and reached the main Chat tab in October.

GPT-6.1 Sol — September 2026

GPT-6.1 Sol followed on September 29, 2026, at OpenAI’s DevDay. OpenAI positions it as near-Astra intelligence for a fifth of the price, with gains in coding, computer use, professional work and factual accuracy, at the same $2 / $10 API price as GPT-6 Sol (OpenAI’s GPT-6 model guide).

A GPT-6.1 version of Astra had been expected the same day but was not released. According to press reports, it did not meet OpenAI’s internal safety bar.

GPT-6 in ChatGPT and Intelligent UI — October 2026

On October 7, 2026, GPT-6 came to the ChatGPT Chat tab. Plus, Pro, Business and Enterprise users got GPT-6 Sol, and Free and Go users got GPT-6 Luna from October 8.

The release also introduced Intelligent UI: GPT-6 can compose answers with interactive elements such as buttons, forms, charts and small tools when they are more useful than plain text, and falls back to text when it isn’t.

GPT-6.1 Terra — October 2026

GPT-6.1 Terra, released October 26, 2026, replaces GPT-5.6 Terra as the middle tier between Sol and Luna. It is built for everyday computer work: reading the screen, clicking, typing and moving between applications to enter invoices, fill in forms or draft documents.

API pricing is $1 / $5 per million input / output tokens, half the price of GPT-5.6 Terra. It is rolling out in ChatGPT Work and Codex for Plus, Pro, Business, Enterprise and Edu users. OpenAI rates Terra Critical for cybersecurity as well and ships it with the same safeguards as Astra.

How GPT model names work

GPT-6 kept the naming system GPT-5.6 introduced and added a tier on top. The number identifies the generation (GPT-6) or its point release (GPT-6.1). The name identifies the tier: Astra is the flagship, Sol is the high-capability workhorse, Terra is the balanced tier and Luna is the low-cost tier.

Tiers update independently, which is why the current lineup mixes GPT-6 Astra and GPT-6 Luna with GPT-6.1 Sol and GPT-6.1 Terra. And GPT-6.1, Sol, Instant and Pro still describe different things: a point release, a capability tier, a ChatGPT response mode and a higher-compute product option.

Current GPT lineup

As of late October 2026, the GPT lineup has four tiers across two version numbers. Which one you get depends on where you use it: the ChatGPT Chat tab, ChatGPT Work, Codex or the API.

The Instant and Thinking levels

ChatGPT presents GPT-6 through response modes and reasoning levels rather than separate model choices. Paid users chat with GPT-6 Sol and pick a reasoning option, from Instant through Extra High, in the model picker, and Intelligent UI works at every one of those levels. Since September 14, 2026, ChatGPT no longer switches automatically from Instant to Thinking for Plus and Pro users, so the choice is now explicit.

Free and Go users get GPT-6 Luna. The options available depend on the plan and, for managed workspaces, on workspace settings.

Pro

Pro is not another GPT generation, it’s a higher-compute ChatGPT configuration. Since the GPT-6 rollout, the Pro reasoning option in Chat runs on GPT-6 Astra, which makes it the main way to use Astra inside ChatGPT. It doesn’t support Intelligent UI. OpenAI updated GPT-6 Pro on October 16, 2026 with faster responses, better tool use and parallel agents, and a separate Pro 500 plan adds Astra Ultrafast.

The GPT-6 frontier: Astra, Sol, Terra and Luna

These aren’t four quality settings on the same model. They are distinct models designed to occupy different points on the capability, speed and cost curve. Astra is the frontier model for the hardest work. Sol carries near-Astra capability for complex professional work and coding. Terra is the balanced tier for everyday and computer-use work, and Luna is the efficient tier for cost-sensitive, high-volume workloads.

Not every tier appears everywhere. Terra isn’t selectable in the standard Chat tab; it lives in ChatGPT Work and Codex. Astra appears in Chat only through the Pro option. The OpenAI API exposes all four.

API variants: Mini and nano

The OpenAI API exposes a broader catalogue than ChatGPT: GPT-6 Astra, GPT-6.1 Sol, GPT-6.1 Terra and GPT-6 Luna alongside earlier models such as GPT-5.6, GPT-5.5, GPT-5.4 mini and nano and GPT-5.3-Codex. API reasoning effort runs from Low through Extra High or Max where supported, and an Ultrafast option speeds up token generation for Astra and GPT-6.1 Sol. 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. 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. In the GPT-6 generation, Luna fills much of that low-cost role. 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, and GPT-5.4 brought advanced coding capability into the general GPT line. In the GPT-6 generation, Codex runs the general models directly: GPT-6 Sol and Luna were added to Codex on September 22, 2026, followed by GPT-6.1 Sol and GPT-6.1 Terra. Codex remains a distinct coding-focused product and workflow rather than another GPT number.

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; Astra, Sol, Terra and Luna are distinct 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.

How GPT-6 models perform on Toloka Arena

Vendor benchmarks show what a model can do under the vendor’s own conditions. Toloka Arena tests models on private, non-contaminated tool-use tasks across 11 domains and scores them by pass^5: a task only counts if the model solves it in all five attempts. The October 27, 2026 standings cover 34 models, including six GPT models.

GPT-6.1 Sol matches Astra at a fraction of the cost

GPT-6.1 Sol and GPT-6 Astra tie at 70.2%, the highest score of any GPT model and tied for fourth of 34 models overall. Astra costs roughly five times more per task, which is consistent with OpenAI’s positioning of Sol as near-Astra intelligence at a fifth of the price. On these tasks, the extra spend on Astra buys no measurable gain in reliability.

GPT-6 Sol did not beat GPT-5.6 Sol

GPT-6 Sol scores 63.2%, slightly below GPT-5.6 Sol at 64.7%. The gap sits inside the confidence intervals, so the safest reading is no improvement in reliability rather than a regression. It echoes what Toloka’s GPT-5.6 study found a generation earlier: a newer model can reason better and still make more mistakes when it changes state across multi-step tasks. GPT-6.1 Sol closes that gap with a 5.5-point gain over GPT-5.6 Sol.

Terra and Luna: Where the low-cost tiers land

GPT-6.1 Terra scores 59.0% at about $1.50 per task. OpenAI’s launch materials report Terra matching Astra on one of its own evaluations at about a tenth of the price; on Arena’s tool-use tasks, Terra trails Astra by about 11 points while costing roughly a ninth as much.

GPT-6 Luna scores 39.1% at around $0.15 per task, which puts it on Arena’s cost-efficiency frontier: no cheaper model in the October standings scores higher. That makes Luna a reasonable fit for high-volume, lower-stakes workflows where human review catches failures.

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. On Toloka Arena’s October 2026 standings, the top composite scores are spread across Anthropic, Google and OpenAI models, and the confidence intervals of the top group overlap. 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 the strategic trade-offs between proprietary and open models, see the guide to open-source versus closed-source LLMs. For a deeper look at the other leading families, see Toloka’s guides to Claude models and Gemini models.

What’s next for GPT

GPT-6 answered one open question: the tier naming survived the generation change, with Astra added on top. Development continues toward longer-running, tool-heavy and computer-use work, with Terra’s computer-use focus and Intelligent UI as the latest signs of that direction.

Safety is now shaping the release schedule as well. Astra launched with restricted access because of its cybersecurity capability, and a planned GPT-6.1 Astra was held back after internal safety testing, according to press reports. When the next Astra update or GPT-7 arrives has not been announced.

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 request our 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 to GPT-4 sequence. Today it has several distinct layers: GPT-6 and GPT-6.1 identify the generation and its point release; Astra, Sol, Terra and Luna identify the models within it; 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.

Independent results add one more lesson: a newer or pricier tier isn’t automatically more reliable. On Toloka Arena, GPT-6.1 Sol matches Astra for a fraction of the cost, and GPT-6 Sol did not beat its predecessor. The main idea is about 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 latest GPT model?

As of October 26, 2026, the newest GPT release is GPT-6.1 Terra, launched on October 26 as a lower-cost tier for everyday computer work. OpenAI’s most capable model is GPT-6 Astra, released in September 2026, while GPT-6.1 Sol, released September 29, offers near-Astra capability at a fifth of the API price. GPT-6 Luna is the cheapest tier. In ChatGPT, paid plans chat with GPT-6 Sol and the Pro option uses Astra, while Free and Go users get GPT-6 Luna.

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, GPT-5.6 and GPT-6, with each generation containing different model variants, product integrations and capability tiers such as Astra, Sol, Terra and Luna.

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, the surface they use (Chat, Work or Codex) and the reasoning option they select.

What do Sol, Terra and Luna mean?

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.

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

These are ChatGPT response modes and reasoning configurations, not separate GPT generations. On Plus, Pro, Business and Enterprise plans, Instant through Extra High use GPT-6 Sol at increasing reasoning effort, and Intelligent UI works at each of those levels. The Pro option uses GPT-6 Astra. Free and Go users get GPT-6 Luna. The options available depend on the plan and, for managed workspaces, on workspace settings.

How does GPT compare to Claude and Gemini?

No model family wins every task. On Toloka Arena’s October 26, 2026 standings, GPT-6.1 Sol and GPT-6 Astra tie at 70.2% composite pass^5, fourth of 34 models, with Claude and Gemini models holding the top three scores and overlapping confidence intervals across the top group. Rankings shift by domain and by cost, so test representative workloads from your own use case and check Toloka Arena’s domain standings for the domain closest to yours.