ChatGPT Models Compared: Differences, Strengths and Choosing the Right One

Anyone who works with ChatGPT on a regular basis will sooner or later run into a confusing number of model names and options. Behind the seemingly simple chat interface there is now an entire family of different models that differ significantly in speed, cost, and above all in their “thinking depth.” For everyday use, a rough understanding of the main differences is often enough – but anyone who wants to deliberately choose the right model for a specific task will benefit from a closer look. That is exactly what this article provides.

How the ChatGPT models fundamentally differ

Speed versus thinking depth

The most important difference between the available models is not “more knowledge,” but the ratio of speed to care. Fast, inexpensive models respond almost instantly and are well suited to simple, clearly defined tasks such as short summaries or quick wording help. So-called reasoning or “thinking” models, on the other hand, deliberately take more time: they break a task down into several intermediate steps, check their own logic, and thereby deliver significantly more reliable results for complex questions – such as multi-step calculations, legal considerations, or the analysis of longer documents. The price for this is a longer response time and higher computational cost.

Context window: how much a model can “keep in mind”

Another decisive difference is the so-called context window, i.e. the amount of text a model can take into account simultaneously in a single pass. Current OpenAI models offer a context window of up to one million text units (so-called tokens), which corresponds to roughly several hundred pages of text. In practical terms, this means a capable model can, for example, read in a complete lease along with several years of utility bill statements at the same time and answer questions about it, while an older or simpler model could only process excerpts.

Multimodality: text, image and speech in one model

Modern ChatGPT models are multimodal, meaning they process not only text but also images and spoken language within the same model. A photo of an invoice or a floor plan can be uploaded directly and discussed in the same conversation, without any separate tools.

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The current model family: GPT-5.6 with the tiers Sol, Terra and Luna

Since July 2026, OpenAI has been relying on a new model concept: instead of releasing completely new model generations with their own name every year, GPT-5.6 introduces three permanent performance tiers called Sol, Terra and Luna, which can be developed further independently of one another without the underlying name changing again.

Sol – the flagship for demanding tasks

Sol is the most capable and at the same time most expensive tier. It is suited to tasks where maximum care matters: complex text analyses, multi-layered calculations, or checking longer contracts for contradictions. Sol can also work with different “thinking levels,” ranging from a very fast setting to a particularly thorough one, in which the model takes noticeably more time for its answer.

Terra – the balanced everyday companion

Terra is the most sensible choice for most everyday tasks. This tier offers a good compromise between quality, speed and cost, and costs roughly half as much as Sol. For standard tasks such as writing emails, summarizing documents, or general research, Terra delivers, in practice, results barely worse than Sol – but considerably faster.

Luna – fast, inexpensive and sufficient for simple tasks

Luna is the fastest and cheapest tier in the family and is aimed at very simple, clearly defined requests where speed matters most, such as short wording suggestions or simple translations. Luna is less suited to deeper analysis.

Automatic selection (Auto mode)

For users who don’t want to deal with these subtleties, ChatGPT offers an automatic selection in the paid subscriptions: the software itself decides, depending on the question, which model tier is appropriate in the background. For everyday use, this automation is usually entirely sufficient; anyone who regularly solves particularly demanding tasks can also select the model deliberately.

Model versions at a glance: from GPT-4o to GPT-5.6

Model version	Approximate period	Key feature
GPT-4o and older reasoning models (o series)	until summer 2025	Separate models for fast answers and for thorough “thinking,” users often had to switch manually
GPT-5	from August 2025	First merging of fast response and reasoning into a single unified system
GPT-5.1	November 2025 to March 2026	Fine-tuning of tone and reliability, since superseded by newer versions
GPT-5.5	approx. spring 2026	Significantly faster voice mode, among other things introduced into car and assistant systems
GPT-5.6 (Sol, Terra, Luna)	since July 2026, current status	Three permanent performance tiers instead of individual model names, context window of up to one million tokens

Rule of thumb: you don’t need to know the model history in detail – what matters is simply that “more thinking time” generally means “better answer for complex questions,” and a “faster model” is perfectly sufficient for simple everyday questions.

Practical examples for everyday life and work

Example 1: Checking an exposé text for completeness and clarity

Anyone wanting to sell or rent out a property can paste the draft of a listing exposé into ChatGPT and specifically ask whether important information is missing, whether the language is understandable for laypeople, or whether phrasing is repetitive. A mid-tier model is generally sufficient for this, since it is a manageable, clearly defined text task.

Example 2: Summarizing and explaining a utility bill statement in plain language

A multi-page utility bill statement with many line items can be uploaded and summarized in simple terms: which items have gone up, which have gone down, and whether there are unusual deviations from the previous year. With several years of statements and attachments, a large context window is an advantage here, so that the model genuinely takes all pages into account at once rather than only the most recently pasted excerpt.

Example 3: A first assessment of a loan offer

A loan offer with an interest rate, repayment schedule and special repayment options can be run through ChatGPT as a first, non-binding orientation and explained in plain language – for example, how the monthly installment changes with a certain special repayment. For a reliable calculation with several conditions, the more thorough thinking tier is worthwhile here, but a final decision should always be additionally coordinated with the bank or independent advice.

Example 4: Trip planning and everyday organization

For planning a weekend trip, creating a packing list, or drafting a notice of termination, the fast, inexpensive model tier is entirely sufficient – here a quick answer matters most, not maximum thinking depth.

Example 5: Multilingual communication with business partners

For translating and stylistically adapting business correspondence into several languages, the mid-tier model is well suited, since both a feel for language and a certain reliability are required here, without the need for particularly long thinking time.

Comparison table: which tier for which task

Task type	Recommended tier	Reasoning
Short everyday questions, simple wording	Luna (fast, inexpensive)	Task is clearly defined, speed matters more than depth
Emails, summaries, general research	Terra (balanced)	Good compromise between quality and speed for most everyday tasks
Contract review, multi-step calculations, long documents	Sol (flagship)	Maximum care and greatest thinking depth for complex contexts

Frequently asked questions about the ChatGPT models

Which model should I use as a beginner?

For getting started, the automatic selection in Auto mode is generally sufficient; it decides itself between the performance tiers depending on the question and delivers good results for most everyday tasks.

What does “reasoning” mean for ChatGPT models?

Reasoning describes a model’s ability to mentally break a task down into several intermediate steps instead of formulating an answer immediately. This increases reliability for complex questions but costs more time and computing power.

Why do OpenAI’s model names change so often?

OpenAI releases improvements at short intervals and renames larger version jumps. However, since GPT-5.6, the company has deliberately switched to permanent tier names such as Sol, Terra and Luna, which are meant to keep developing independently without the underlying designation changing again.

Can I also solve complex tasks with a simple model?

In principle, yes – however, with complex, multi-step tasks, the risk of reasoning errors or overlooked details increases with a simple model. For important decisions, the more thorough model tier is recommended.

How large can a document be that I want to give to ChatGPT?

Current top models process up to around one million text units simultaneously, which corresponds to several hundred pages of text. For most private use cases such as contracts, statements or listing exposés, this is more than sufficient.

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