Which AI models are there in 2026?

AI models are the core of modern AI tools such as ChatGPT, Claude and Gemini. This page gives you an up-to-date overview of the main AI models that matter for professional work, their versions as of October 2026 and which model suits which kind of work.

The field moves fast. Releases since September 2026 include Gemini 3.8 Flash (2 September 2026), GPT-6 Astra (3 September 2026), Grok 4.7 (21 September 2026), Claude Opus 5.5 (22 September 2026) and Claude Haiku 5.5 (7 October 2026). If you choose one AI model now, you are not choosing for ever: the choice that makes sense today will be up for debate again in six months.

What are AI models?

AI models are the underlying systems that generate language, images or code from a prompt. An AI model is not an app or a website. It is a trained system that has learned patterns from large amounts of data and uses them to predict the next word, image or piece of code. ChatGPT is not an AI model; ChatGPT is a tool built on top of OpenAI’s GPT model. The same applies to Claude.ai (the tool) versus Claude Opus 5.5 (the model).

The AI models on this page are all large language models, trained on text and therefore strong at writing, summarising, translating and analysing. Image generation models such as DALL-E and Midjourney are AI models too, but fall outside this overview; how image generation works is explained under AI art.

AI models versus AI tools

The difference between AI models and AI tools confuses many people, yet it matters for your choice. An AI model is the engine; an AI tool is the car built around it. One AI model can be used by several tools, and one tool can use several models under the bonnet.

Microsoft Copilot is a good example. Copilot is an AI tool that, depending on the task, uses OpenAI models, Microsoft’s own models and Anthropic models through a direct partnership. The user sees one interface, but under the bonnet the models change.

For professionals, this means the question “which AI tool do I use” is not the same as “which AI model do I use”. The tool determines the working environment and the integrations; the AI model determines the quality of the output.

Closed source and open source AI models

The AI models professionals use every day fall into two groups: closed source models from commercial providers and open source models that you can, in principle, host yourself. Closed source means the model’s weights and architecture are not public; you use the model through the provider’s interface or API. Open source means you can download the model, run it on your own infrastructure and adapt it where needed.

Closed source AI models give professionals the most convenience: a polished interface, integrations with existing software and little technical setup. Open source AI models give you more control over data and hosting, but more rests on your own shoulders. For most knowledge workers, closed source is the logical place to start. For organisations with strict privacy requirements or large API volumes, open source becomes a serious option because of data location and lower variable costs.

This page covers both groups, with the focus on specific LLMs. Broader AI categories such as machine learning, deep learning and the split between narrow and general AI are covered in the explanation of artificial intelligence as an umbrella term, so they are left out here.

The major AI models for business use

The major AI models for business use are the commercial LLMs from OpenAI, Anthropic, Google and xAI, plus Microsoft’s own models available through Copilot. Professionals all over the world use these models, and they are the first choice if you want to start working with AI quickly without technical setup. For each AI model, the sections below give the current version as of October 2026, its key characteristic and when the model makes sense.

The table sets the current top version from each provider side by side, with the type of model and its strongest point for business use. Correct as of 9 October 2026, based on the providers’ release notes and model documentation.

GPT models from OpenAI

GPT models from OpenAI are the best-known AI models in the world and power ChatGPT. The model OpenAI recommends for complex reasoning and coding is GPT-6 Astra, introduced on 3 September 2026. Since 7 October 2026 ChatGPT has used GPT-6 Sol on paid plans, and since 8 October GPT-6 Luna on Free and Go. According to OpenAI, the earlier GPT-5.5 (23 April 2026) was the first fully retrained base version since GPT-4.5.

GPT’s strength is its breadth: for general writing tasks, brainstorming, quick questions and agentic workflows, GPT is a logical choice. My switch from ChatGPT to Claude had to do with qualities other than this breadth. If you are just starting with ChatGPT or mainly have short conversations, the model often remains the most accessible starting point.

If you have worked with GPT before: to me, GPT-5.5 felt more like a refinement than a breakthrough compared with GPT-5.4. GPT-5.5 is a little faster and handles context a little better, but the basic level of reasoning and self-checking has not changed in any real way. That nuance often gets lost in release announcements. In the ChatGPT Course you build a fixed way of working around GPT models, with checkpoints that help you judge the differences between versions in your own work.

Claude models from Anthropic

Claude models from Anthropic are GPT’s direct competitor and have built a strong position among professionals who process longer documents or write code. According to Anthropic’s model documentation (October 2026), Claude from Anthropic consists of four lines: Fable 5.1 for the heaviest reasoning work, Opus 5.5 as the recommended default model, the faster Sonnet 5.5 and the lightweight Haiku 5.5 (7 October 2026). Fable arrived on 9 June 2026 as a new tier above Opus.

Claude’s key characteristic is that the model handles long context well and follows instructions more strictly than other AI models. In my own work with Claude, I notice that the model bluffs less often when facts are uncertain and is more honest about what it cannot answer. GPT, by contrast, is more inclined to give a convincing but incorrect answer. Choosing between the Claude models is a trade-off between Opus for the heaviest analyses, Sonnet as the value-for-money choice for daily work and Haiku for tasks where speed matters more than reasoning power.

When I moved from Opus 4.6 to 4.7, I noticed that the answers became longer and wordier, and that the model went along with the user more readily than 4.6 did. That version-specific observation does not turn up in every tool comparison, but it makes a difference if you use Claude every day. A new model version is not automatically a better model version. The Claude Course teaches you to work with Projects, Artifacts and system prompts, using a way of working that still holds if Anthropic releases a new model version tomorrow.

Gemini models from Google

Gemini models from Google are the third major player and have strengthened their position considerably in 2026. The top model of Google Gemini is Gemini 3.8 Flash (2 September 2026), which Google describes as its most intelligent workhorse; the only current Pro model in the API is Gemini 3.1 Pro, still in preview. Gemini stands out on three points: a context window of 1 million tokens, native multimodality (text, image, audio and video) and attractive API pricing compared with GPT and Claude.

Gemini’s strongest position is in the Google Workspace ecosystem. If you work in Gmail, Docs and Sheets every day, Gemini is available right inside that environment for summarising, analysing and drafting. For spreadsheet analysis in Google Sheets I sometimes use Gemini, because the integration with the data works without extra steps.

Microsoft models and Copilot

Microsoft has its own AI models under the names Phi (small models for local use) and MAI (larger models for general work). In practice, end users reach these models through Microsoft Copilot, the AI layer in Microsoft 365. Copilot itself is not a separate AI model but an orchestration tool that switches between Microsoft models, OpenAI models and Anthropic models depending on the task.

Copilot’s positioning is clear: it is the AI layer inside Microsoft 365. If your organisation works with Word, Excel, Outlook and Teams, Copilot is already in your existing working environment. You then need neither a separate tool nor separate training. The downside is that Copilot stands out less than ChatGPT or Claude on general chat tasks; for demanding work a dedicated AI model is often a better choice.

Grok models from xAI

Grok models from xAI, Elon Musk’s AI company, are the fifth major commercial player. The latest version is Grok 4.7, which xAI announced on 21 September 2026 as its strongest model for coding and knowledge work. Grok from xAI stands out on two points: direct integration with real-time data from X (formerly Twitter) and a less filtered conversational style than other AI models.

Grok has had limited testing for LearnLLM, to understand how it works and where it fits in a professional tool stack. For business use Grok remains a niche choice: the real-time X integration only matters if your work depends on trending content on that platform, and the less filtered style is more of a complication than an advantage in most professional contexts.

Open source AI models

Open source AI models are AI models whose weights and architecture are publicly available, so anyone can download, use and adapt them. For professionals, open source means two things: you have more control over your data because you can host the AI model yourself, and you pay no token costs to a commercial provider. The downside is that you need more technical setup and that performance on most tasks still lags slightly behind the very top of closed source.

The three main open source AI models for professional use are Llama from Meta, Mistral from France and DeepSeek from China. In addition, Google offers Gemma, an open model family that runs on your own hardware. If you want to weigh up open source and closed AI models on data, cost and performance, that article compares the two camps directly.

Llama from Meta

The best-known open source AI model is Llama from Meta, which many developers use as the base for derived models. The latest generation is Llama 4, released on 5 April 2025, with the variants Scout (17 billion active parameters, context window of up to 10 million tokens) and Maverick (17 billion active parameters with 128 experts, 400 billion in total). Since 8 April 2026 the Meta AI assistant no longer runs on Llama but on Muse Spark, a closed model; Meta’s open alternative is now Muse Glimmer (10 August 2026, Apache 2.0).

Llama holds a special position because Meta makes the model available free of charge under a licence that permits commercial use up to a certain scale. For organisations that want to host models themselves, build AI features into their own products or avoid depending on American cloud providers, Llama is a serious option.

Mistral from France

The French provider Mistral offers the most important European open source AI model and positions itself explicitly as an alternative to American and Chinese providers. The newest model is Mistral Large 4, in public preview since 6 October 2026: a mixture-of-experts model with around 1 trillion parameters, 52 billion of them active, with open weights that Mistral is releasing at the end of October. Below it sits Mistral Medium 3.5, aimed at agents and coding work. Mistral Large 3, released on 4 December 2025, has 675 billion total parameters and 41 billion active parameters through a mixture-of-experts architecture. Mistral Large 3 was released under the Apache 2.0 licence, which allows full commercial use.

For European organisations, including Dutch ones, Mistral is relevant because of its European origin. Data location within the European Union is easier to guarantee with Mistral than with American or Chinese providers, and the company actively positions itself on compliance with the EU AI Act.

DeepSeek from China

China’s DeepSeek made a forceful entry into the open source market in 2025 and 2026 with aggressive pricing and strong benchmark scores. DeepSeek V4 appeared on 24 April 2026 as a preview in two variants: V4-Pro with 1.6 trillion parameters and V4-Flash with 284 billion, both with open weights and a context window of 1 million tokens. On coding tasks, V4 is among the strongest open source AI models of 2026.

The downside is geopolitical and privacy-related. For European organisations that work with personal data or commercially sensitive information, a Chinese model through a Chinese cloud is a serious limitation, whatever its technical performance. If you host DeepSeek yourself on your own infrastructure you avoid the privacy problem, but then you are back to the open source question: can your organisation handle the technical setup?

Which AI model should you choose for which work?

The best AI model for you depends on the type of work you do, not on an overall “winner”. Many overviews rate AI models on abstract benchmarks, whereas for a professional’s daily work four questions count most:

• What is the nature of your work?
• Which software environment do you work in?
• How strict are your privacy requirements?
• How quickly do your use cases change?

Below is the AI model that makes sense for each type of work.

Writing and long documents

For writing and processing long documents, Claude is the strongest AI model in my experience. Claude loses less context in long documents than ChatGPT and follows tone and style instructions more strictly, which saves rounds of correction. If you regularly analyse contracts, annual reports or long client briefs, that difference soon outweighs the slightly higher price.

GPT is a good second here and in many cases sufficient. The difference shows mainly in longer conversations or with very specific writing preferences. If you are just starting to write with AI, GPT is a logical entry point; if you use it intensively every day and want to take the next step, Claude is usually the natural alternative. A full Claude vs ChatGPT comparison on reasoning quality, context window, price and privacy will help you decide better than two paragraphs here can.

Working in Microsoft 365 or Google Workspace

If you work entirely in one office environment, the logical choice is the AI model integrated into that environment. Microsoft 365 users get AI in Word, Excel, Outlook and Teams through Copilot, without having to open a separate tool. Google Workspace users get Gemini in Gmail, Docs and Sheets with the same close integration.

Here the ecosystem matters more than model quality as such. A marginally better model in a separate tab can deliver less value in practice than a slightly weaker model that sits right in your existing working environment. For most office professionals that is a bigger factor than benchmark scores.

Research and source verification

For research tasks where current sources and verification matter, standard chat models often fall short. ChatGPT, Claude and Gemini have knowledge with a cut-off date and cannot find new sources on their own unless they use a dedicated web search function.

If you do a lot of research that compares sources, you could consider a research-oriented tool such as Perplexity, which shows sources directly in the answer. The Perplexity vs ChatGPT trade-off comes down to a choice between current source citations and broader language ability; Perplexity has had limited testing for LearnLLM, to understand how it fits in a tool stack.

Coding and automation

For coding work and automating workflows, the top models from OpenAI and Anthropic lead the field in October 2026. Anthropic recommends Claude Opus 5.5 as the starting point for most tasks and Claude Fable 5.1 for long-running agentic work, while Gemini is a cost-efficient alternative for large codebases. Which model wins depends on the type of code and shifts with every release.

In my Python integration with the Claude API, which runs on my own VPS, I notice that Claude handles instructions with multiple steps better and is more honest about edge cases. In production workflows that matters more than a few percentage points’ difference on a coding benchmark. A script that flags errors usually needs less maintenance than one that confidently produces the wrong result.

AI models and EU regulation

Since 2025, AI models you use for business have gradually come under the EU AI Act. The regulation entered into force in August 2024 and applies in stages. The provisions on prohibited practices and AI literacy apply from 2 February 2025 (since 27 July 2026, AI literacy has been an obligation to take measures rather than to achieve a result). The rules for general-purpose AI models (the models on this page) apply from 2 August 2025 and most of the remaining rules from 2 August 2026. The provisions for high-risk AI systems listed in Annex III follow from 2 December 2027. For most office professionals, two topics matter most.

The EU AI Act in practice

Under the EU AI Act, what counts is the risk category of the use, not the type of model. Most uses professionals rely on every day, such as drafting, summarising and analysing text, fall into the minimal or limited risk category and carry no specific compliance obligations. High-risk uses are AI systems that make decisions about people, such as automated handling of job applications, creditworthiness assessments or support for medical diagnosis. That category is subject to an extensive compliance regime, from 2 December 2027 for the uses in Annex III and from 2 August 2028 for AI in products under Annex I.

From my years at Rabobank, ABN Amro and Van Lanschot I bring compliance thinking to AI work: the question is not only whether something is allowed, but whether you can justify why you set it up the way you did. For AI use, that means recording clearly which data goes into a model, which claims the model makes and who has final responsibility for the output.

Privacy and data location

Privacy and data location are the second deciding factor when choosing an AI model. Not every AI model processes your data within the European Union, and not every AI model promises that your input will not be used for future training. If you work with personal data or commercially sensitive information, this is a hard limit, not a detail.

Most commercial AI models offer a business version with stronger privacy guarantees than the consumer version. For ChatGPT that is ChatGPT Enterprise; for Claude, the business version of Claude.ai and the API with the right contractual agreements. With open source models you host yourself, the data stays within your own infrastructure by definition. Which AI tools comply with the GDPR differs by supplier and by type of plan.

European AI models

European AI models are models from European makers that fall under European law, with Mistral from France as the largest provider and GPT-NL as a Dutch initiative. Mistral offers both closed models through an API and open models you host yourself.

GPT-NL is the Dutch language model that the research organisations TNO and SURF and the Netherlands Forensic Institute have been building since the end of 2023. It is funded with €13.5 million from the Dutch Ministry of Economic Affairs. According to its makers, GPT-NL is trained on licensed texts rather than scraped web data; in January 2026 the model won the Dutch Privacy Award. As of September 2026, GPT-NL is still running as a beta at five organisations in sectors including government and finance, and version 1 is planned for autumn 2026. In performance, GPT-NL lags behind models such as Mistral Small.

Claude, ChatGPT and Gemini are American AI models. A business contract with data processing in the EU changes where your data is stored, not the origin of the model or the jurisdiction of the provider.

How do you learn to work with an AI model?

The most important step in learning to work with an AI model is to choose one model and build a fixed way of working around it. Jumping between AI models for different tasks achieves less than getting to know one model well and setting up your own processes around it. Only once your way of working is in place does it make sense to add secondary models for specific tasks.

In my work, the move from one-off prompts to structured workflows did not come from a single moment. It came from errors that reached production, from repeating the same prompts and from realising that clients do not accept inconsistent output. That experience forces you to use fixed checkpoints instead of improvising for each task. The LearnLLM approach is built on that principle: one tool, a fixed way of working, and checkpoints that catch errors before they cause harm.

The LearnLLM e-learning courses are built around that principle. The ChatGPT Course (eight modules) and the Claude Course (nine modules) each build a complete way of working around one AI model, including a final assignment based on your own work. LearnLLM’s courses and approach address the problem most professionals run into: the challenge is not knowing more prompts, but using the same prompt in the same way every day, with output you can stand behind.