The most important AI providers compared

The following table shows ten of the best-known AI providers in direct comparison. The percentage values are deliberately kept simple and describe a comprehensible assessment based on four criteria: how easily can the offering be used without prior knowledge? How privacy-friendly is it from the perspective of a German user? What is the price-to-performance ratio? And how extensive are the features (text, image, voice, video and more)?

These values are a simplified, editorial assessment for rough orientation – not a scientific measurement and not legal advice. For business use with sensitive data, the respective provider article and the current contract terms should always be reviewed.

What the four criteria mean

Ease of use

How quickly can someone without technical knowledge get the hang of it? Most major providers perform similarly well here, because the basic principle – type in a question, get an answer – barely differs between them.

Data protection/GDPR friendliness

This is where the biggest differences show up. What matters most is where a company is headquartered and where its servers are located: providers based in the European Union, such as Mistral AI, don’t need to rely on international supplementary agreements, while US providers depend on the EU-US Data Privacy Framework or Standard Contractual Clauses. For providers with servers outside the EU or the USA, for example in China, particular caution is advisable with personal or business-sensitive data.

Price-performance

How much functionality do you get for the money spent? Free or very inexpensive offerings such as Meta AI or DeepSeek naturally score well here, even if they require compromises elsewhere.

Feature scope

How broad is the offering – pure text answers, or additionally image, voice, video, and the ability to independently handle multi-step tasks?

The ten providers in brief

OpenAI – ChatGPT

The best-known name in the field of artificial intelligence. ChatGPT covers text, image, voice and video in a single product and is developed further by OpenAI at short intervals. All the details on pricing and data protection are in the provider article on OpenAI; the individual model versions are explained on the ChatGPT models page.

Anthropic – Claude

Anthropic was founded by former OpenAI employees with a particular focus on safety. Claude is considered especially reliable with long texts and documents. More on this in the provider article on Anthropic and on the Claude models page.

Google – Gemini

Gemini is deeply embedded in Gmail, Google Docs and Search, and is especially practical for anyone already living in the Google ecosystem. Details in the provider article on Google and on the Gemini models page.

Microsoft – Copilot

Copilot is built directly into Windows, Word, Excel, Outlook and Teams, making it especially strong in classic everyday office work. More in the provider article on Microsoft and on the Copilot models page.

xAI – Grok

Grok is closely integrated with the platform X, making it especially current when it comes to up-to-the-minute topics. Details in the provider article on xAI and on the Grok models page.

Meta – Meta AI and Llama

Meta AI is built directly into WhatsApp, Instagram and Facebook for free and is financed through advertising rather than a subscription. Its technical foundation, Llama, can also be self-hosted. More in the provider article on Meta and on the Llama models page.

DeepSeek

The Chinese provider impresses with very low prices and strong performance on logic and programming tasks, but is critically reviewed by German data protection authorities because of its server location in China. Details including all models (V3, R1, V4) in the article on DeepSeek.

Mistral AI

The French company is Europe’s most important own answer to the US and Chinese AI corporations and scores especially well on data protection, since servers within the EU can be chosen. More, including all models (Large, Medium, Small), in the article on Mistral AI.

Perplexity AI

Perplexity works more like an answer engine than a classic chatbot: every statement is backed up with a source citation. Details in the provider article on Perplexity and on the Perplexity models page.

Moonshot AI – Kimi

Kimi is the chatbot from the Chinese company Moonshot AI and became internationally known through the openly available model Kimi K2, which performs especially strongly on programming and agentic tasks. As with DeepSeek: with standard use, the data ends up on servers in China. Details in the provider article on Kimi and on the Kimi models page.

Which provider suits whom?

Frequently asked questions about AI models

What is the difference between an AI provider and an AI model?

The provider is the company, such as OpenAI or Google. The model is the actual technology behind it, which powers one or more products – at OpenAI, the product is called ChatGPT, while the underlying models carry their own names such as GPT-5.6.

Which AI is the best?

That can’t be answered across the board. Depending on the task, budget and data protection requirements, different providers perform differently well – the comparison table above serves as initial orientation, while the linked individual articles provide the details.

Are all these AI providers allowed in Germany?

In principle, yes – there is no general ban on any of the providers named. However, for individual providers with server locations outside the EU, particular caution is advised with personal or business-sensitive data – details on this are in the respective provider article.

Do I always have to pay for good results?

No. Several providers offer usable free versions, such as Meta AI, DeepSeek, Kimi, or the basic versions of ChatGPT, Gemini and Copilot. For regular or more demanding use, an inexpensive entry-level subscription is usually worthwhile.

What does this have to do with Lukinski?

As a real estate company, we ourselves work intensively with artificial intelligence – for example with Lukinski AI, our own AI assistant for real estate questions, hosted in Germany. This cluster additionally puts the large, general-purpose AI providers into context, so that you can make an informed decision independent of our own offering.

From research model to heavyweight: the Kimi K2 series

Kimi K2: the technical foundation

The original Kimi K2 version was introduced in July 2025 and described in detail in a technical report (“Kimi K2: Open Agentic Intelligence”). Technically, it is a so-called Mixture-of-Experts (MoE) model: instead of a single, continuously active neural network, the model consists of many specialized sub-networks (“experts”), of which only a small portion is actually activated per request. For Kimi K2, according to Moonshot AI, there are a total of around one trillion parameters, of which around 32 billion are actively used for any single request. The model is spread across 384 experts, of which 8 are specifically selected per text segment, supplemented by an always-active “shared” expert.

This structure explains why Kimi K2, despite its enormous overall size, can be operated comparatively economically: the entire trillion parameters do not need to be computed for every request, only a small, selected part. According to Moonshot AI, the model was trained using a specially developed optimization method (MuonClip), intended to improve training stability for such large MoE models. The original base version offered a context window of 128,000 tokens; the Instruct version (for dialogue and instructions) released at the same time was expanded to 256,000 tokens.

Kimi K2 Thinking: thinking and acting in one model

In autumn 2025, according to consistent reports from November 2025, Moonshot AI introduced “Kimi K2 Thinking,” a variant that additionally incorporates extensive, multi-step thinking before the actual answer and, while still in the thinking process, can already call on tools (such as a web search or a calculator). According to available benchmark figures, the model achieved a score of around 71 percent on the well-known programming test “SWE-Bench Verified” and, in test scenarios, was able to independently carry out several hundred consecutive tool calls to solve complex, lengthy tasks. This deliberately positioned Moonshot AI in the field of so-called agent models, which don’t just answer but independently plan and execute multi-step tasks.

Further development in 2026: K2.5, K2.6 and K3

Over the course of 2026, Moonshot AI released several further tiers. According to consistent, though not always uniform, sources, K2.5 appeared, followed in April 2026 by K2.6 with a context window expanded to 256,000 tokens and improved programming capabilities, followed by a variant specialized in programming tasks called K2.7 Code. Finally, in July 2026, Kimi K3 followed, which, according to the manufacturer, comes with a context window enlarged to around 1 million tokens and native image processing (vision) – according to Moonshot AI, four times the size of K2.6’s context window. Various sources give differing figures for K3’s exact parameter size (some in the range of roughly two to three trillion total parameters); a single, officially definitive figure cannot currently be confirmed with complete certainty, which is why we deliberately speak of an order of magnitude rather than an exact number here.

What makes the Kimi models technically and strategically distinctive?

Open weights with a commercial condition

Moonshot AI releases the model weights for K2 and its successors under a self-formulated, modified MIT license. At its core, this is a very permissive open-source license that also allows commercial use. One special feature: anyone operating a product or service based on the models with more than 100 million monthly active users or more than 20 million US dollars in monthly revenue must visibly display the notice “Kimi K2” in their own interface. For the vast majority of smaller and medium-sized use cases – including in the real estate industry – this clause practically never applies, but it shows that “open” at Kimi is not meant entirely unconditionally.

Agentic capabilities and a large context window

The strategic focus of the Kimi models is clearly on so-called agentic capabilities: the ability to independently handle multi-step tasks with tool use, web access and code execution, rather than just answering individual questions. Combined with a very large context window – around one million tokens for K3 – this makes it possible to process very extensive document collections in a single pass, such as several hundred pages of contract documents or a larger collection of property documents.

Rule of thumb: the large context window makes it possible to read in a great many documents at once – but this does not replace careful review of the output, since even a large context window does not protect against errors or omissions in the result.

Practical examples of use

1. Automated summarizing of large file collections

A case worker at a property management company could have several hundred utility bill statements or minutes from owners’ meetings read in via the API in a single pass, in order to automatically flag anomalies – such as unusually high cost items. The large context window of K2.6 or K3 makes this technically possible without first having to split the documents into many small chunks.

2. Drafting and laying out a listing exposé

In agent mode, a real estate agent can have a first, multi-page draft version of a property exposé created, including a simple web presentation, which is then reviewed editorially by hand and cross-checked against the actual, legally binding property data.

3. Programming support for internal tools

Since the Kimi models perform particularly strongly on programming benchmarks, some IT departments at real estate companies use Kimi to help build small internal automation scripts, for example to reconcile rent lists between two software systems.

4. Research and market monitoring

Via the web-browsing feature of agent mode, a model can be used to continuously monitor publicly accessible market data, for example to summarize interest rate trends or publicly accessible real estate market reports – it’s important here to work exclusively with publicly available, non-personal sources.

5. Multilingual communication

For international real estate portfolios, a financial case worker can use Kimi to translate standard letters or summaries between several languages – though the restriction on personal content described above still applies here as well.

Rule of thumb: the larger the volume of data a model processes in one pass, the more important the question becomes of exactly where that data resides during processing – performance and data protection are two separate questions, and both need to be answered.

FAQ about the Kimi models

What is the difference between Kimi K2 and Kimi K2 Thinking?

Kimi K2 is the original base model; Kimi K2 Thinking is a variant introduced in November 2025 that incorporates more extensive, multi-step thinking with tool use before answering, and thereby performs better on complex tasks.

How large is the context window of the current models?

The models in the K2 series generally offer 256,000 tokens of context; Kimi K3, introduced in July 2026, is stated to have around one million tokens.

Are the Kimi models really open source?

The model weights are openly accessible and released under a modified, very permissive MIT license. One special feature is the attribution requirement for very large commercial applications (over 100 million users or 20 million US dollars in monthly revenue).

Are the models suited to programming tasks?

Yes, the Kimi K2 series, and especially the specialized K2.7 Code variant, perform comparatively strongly on public programming benchmarks and are frequently used for coding agents.

Can I run the models myself instead of using the Kimi website?

Yes, since the weights are released openly, they can be run via various cloud providers or on your own hardware. However, this requires technical expertise and sufficient computing capacity, since these are very large models.

Basic information about the company Moonshot AI, pricing and data protection can be found on our provider page. A comparison of all ten AI providers in the cluster is available on the overview page.

The Grok model family at a glance

Grok 3 – the previous generation

Grok 3 was xAI’s main model for a long time and now forms the basis for the free tier. It handles the basic tasks of a chatbot – answering questions, drafting texts, simple research – but offers a considerably smaller context window and less accuracy for complex, multi-step tasks than the newer models.

Grok 4 and Grok 4 Heavy

Grok 4 was the next big step and brought considerably better results in logical reasoning and more complex questions. The Grok 4 Heavy variant goes a step further: for difficult tasks, it uses several parallel thinking processes and compares the results before giving an answer. This makes Grok 4 Heavy slower and considerably more expensive, but also more reliable for tricky tasks – such as mathematical calculations or multi-step analyses.

Grok 4.1 Fast – fast and inexpensive

For tasks where speed and low cost matter most – such as automatically answering many short inquiries in an app – xAI offers Grok 4.1 Fast, a stripped-down but very fast and inexpensive variant. Its depth of content is lower, but that is usually sufficient for simple, repetitive tasks.

Grok 4.20 – a huge context window and multi-agent technology

With Grok 4.20, xAI introduced a model in early 2026 that internally works with several specialized “roles” that cross-check each other before an answer is given – put simply: one role coordinates, one checks facts, one assesses technical details, one provides creative phrasing. For users, this mainly shows up as more reliable answers for longer, more complicated queries. In addition, there is a very large context window of around 2 million text units (tokens) – equivalent to several thousand pages of text that the model can keep “in mind” at once, for example for analyzing longer contracts or extensive data collections.

Grok 4.5 – the current focus on complex and technical tasks

Grok 4.5, released in July 2026, is currently xAI’s most demanding available model. It was specifically trained for longer, demanding work sessions – originally mainly for software development, but now also for other complex, multi-step tasks such as comprehensive research or the analysis of larger data sets. Its context window is around 500,000 tokens, so smaller than Grok 4.20’s, but the model works more precisely on difficult reasoning tasks. Via the programming interface, Grok 4.5 costs around 2 euros per million input text units and around 6 euros per million output text units.

Grok 5 – what is known so far

A successor model with the working name Grok 5 is, according to xAI, in the training phase on an expanded Colossus data center. There is no official release date as of August 2026; according to rumors, the model is expected to be considerably larger than Grok 4 and to be introduced later in 2026. Until the official announcement, Grok 4.5 remains the most powerful generally available model.

Rule of thumb: the higher the version number, the more powerful and usually also more expensive the model – but not every task needs the strongest model. For simple everyday questions, the fast, inexpensive variant is often enough.

Comparison table of the Grok versions

Real-time data access via X

One feature shared by all current Grok models is direct access to public posts on the platform X. This allows Grok to react to events that are only a few minutes old – a clear difference from models whose knowledge is limited to a certain training cutoff date. The downside: posts on social media are not automatically verified or reliable, which is why answers based on current X posts should always be read with a healthy dose of caution, especially for controversial or unclear topics.

Image and video generation with Aurora and Grok Imagine

For generating images, xAI uses its own system called Aurora, now marketed under the product name Grok Imagine. It generates images from text descriptions and, by now, also short videos a few seconds long in various formats. In the paid SuperGrok tier, the full feature set of Grok Imagine is included; the free tier offers only a limited basic version. Via the programming interface, video generation is billed by the number of seconds generated, which makes it comparatively inexpensive for short clips.

Examples of use from everyday life and business

Example 1 – everyday life: A user is planning a trip and has Grok summarize, based on current posts on X, what the weather and traffic conditions currently look like at the destination – something a model without real-time access cannot deliver.

Example 2 – small business: A case worker at a small trades business uses Grok 4.1 Fast to pre-sort incoming customer inquiries by email and draft reply suggestions, which are then reviewed by a human.

Example 3 – real estate and finance: A real estate office wants to get a first impression of the current sentiment around a particular location – for example, whether there has recently been more discussion about rising rents or a new construction project in a city. Thanks to its access to current X posts, Grok can summarize such discussions and provide initial clues. Importantly: such a sentiment analysis does not replace a robust market analysis based on official data, but merely provides a quick, supplementary impression.

Example 4 – reviewing a long document: A case worker uploads a longer lease or an extensive tender document into Grok 4.20 and has the key clauses, deadlines and figures summarized – thanks to the large context window, the model can take the entire text into account at once, instead of having to break it into sections.

Example 5 – a technical task: A developer uses Grok 4.5 to analyze an existing software application across several files and get suggestions for improving the program code – a task the model was specifically trained for.

Rule of thumb: the longer and more branched the task, the more important the context window becomes – for short everyday questions, on the other hand, it hardly matters.

Practical example: estimating the cost of the programming interface

A real estate office wants to have around 50 property descriptions automatically drafted per month with Grok 4.5. Each description requires an estimated 1,000 tokens of input (property data, conditions) and 1,500 tokens of output (finished text).

• Input: 50 x 1,000 = 50,000 tokens, at 2 euros per million tokens that comes to around 0.10 euros
• Output: 50 x 1,500 = 75,000 tokens, at 6 euros per million tokens that comes to around 0.45 euros
• Total cost per month: around 0.55 euros

This calculation example shows: the pure text costs via the programming interface are very low for manageable text volumes – the actual costs usually come from developing and maintaining the integration, not from the individual requests themselves.

Frequently asked questions (FAQ)

Which Grok model should I use as a beginner?

For getting started, the standard model in the free or the cheapest paid tier is generally sufficient. Switching to more powerful models is only worthwhile once you hit limits with complex or long tasks.

What does “context window” mean in simple terms?

The context window describes how much text a model can keep “in mind” at once. A large context window is important if you want to have long documents analyzed at once, but it hardly matters for short questions.

Is Grok 4.20 the same as Grok 4?

No. Grok 4.20 is its own, later model generation with a considerably larger context window and an internal multi-role check that was not yet present in Grok 4.

When is Grok 5 coming?

There is no official date as of August 2026. xAI has confirmed that a successor model is in the works, without naming an exact date.

Can I get reliable market data for real estate decisions with Grok?

Grok can summarize current discussions and sentiment, but it does not replace verified official data or a professional market analysis. It is best suited as a quick supplement, not as the sole basis for a decision.

An overview of the company xAI itself, its pricing tiers and the data protection assessment is available on our xAI provider page. Anyone wanting to see the other major AI providers compared instead will find the overview on the AI models comparison page.

Why “Copilot” is not just “Copilot”

Microsoft has deliberately turned the name Copilot into an umbrella brand, similar to how other companies do with brands for entire product families. Behind the surface, however, the variants differ considerably: in the data they have access to, in the language model used, and in the security and privacy commitments that apply to each.

Copilot in Windows

The variant integrated into the taskbar is primarily a general-purpose chat for everyday questions, research and simple writing tasks. It has no direct access to internal company documents or emails and is therefore functionally closer to a classic chatbot than a genuine office assistant.

Microsoft 365 Copilot (Business and Enterprise)

This variant is the actual business version. It is connected, via the so-called Microsoft Graph interface, to the documents, emails, calendar entries and chats that the respective user already has access to. This allows it, for example, to create a summary that is actually based on a team’s real project files, rather than merely reflecting general world knowledge. This connection is the decisive technical difference from all other Copilot variants.

Microsoft 365 Premium (successor to Copilot Pro)

The tier intended for private users and self-employed people brings full Copilot features into personal Office applications, but without the deep connection to a company network with many users that the Business variant offers. It is aimed at individuals, not teams.

GitHub Copilot: its own product line

For completeness, it should be mentioned that GitHub Copilot is a standalone product for software developers that assists with writing program code. It shares only the brand name and part of the underlying technology with the other Copilot variants, but is aimed at a completely different target group and is mentioned here only for context.

Which AI models are really behind it

For a long time, practically the entire Copilot family ran exclusively on models from OpenAI. That changed in 2026. In July 2026, Microsoft and OpenAI announced that the new model GPT-5.6 would become the preferred model for Microsoft 365 Copilot in Word, Excel, PowerPoint and the new Copilot workspaces. The model is specifically optimized for knowledge work and is meant to work more efficiently than its predecessors on more complex, multi-step tasks.

At the same time, Microsoft has broadened its strategy and, since 2025, has also offered models from Anthropic in Microsoft 365 Copilot. In practice, administrators in many enterprise environments can now select or restrict which model is used for which application. This is an important difference from pure single-model providers: technically, Copilot works more like a switchboard that mediates between several models in the background, rather than like a single, unchanging language model.

Anyone who asks “which model does Copilot use” no longer gets a simple answer in 2026. The more accurate question is: which model is Copilot currently using for this particular task, and can that be changed in the settings.

Comparison table of the Copilot variants

Practical examples of use from everyday office life

Example 1: Handling an email flood in Outlook

A case worker returns from a two-week absence and finds over 200 unread emails. Copilot summarizes long threads, flags emails requiring direct action, and suggests ready-made reply drafts for routine inquiries. Instead of opening every message individually, the employee gets an overview within a few minutes.

Example 2: Meeting summary in Teams

After a one-hour project meeting, Copilot automatically creates a summary with the most important decisions, open questions, and a list of concrete tasks including the person responsible for each. Anyone who couldn’t attend doesn’t have to watch a recording, but can read the summary in a few minutes.

Example 3: Building a yield spreadsheet in Excel

An example with a real estate angle: a property manager wants to calculate the gross rental yield for several possible purchase prices for an apartment building. He describes to Copilot in Excel, in plain language, which columns he needs, such as purchase price, annual rent, operating costs and the resulting yield in percent. Copilot builds a basic structure with the appropriate formulas from this, which the manager then adapts to the actual figures for his property. Importantly: Copilot provides a usable calculation template, but the substantive review of the formulas and the values used remains the manager’s own responsibility.

Example 4: Revising a listing exposé text in Word

A real estate agency has written a factual but linguistically very dry property description for a listing exposé. Copilot in Word can, on request, give the text a friendlier, more readable tone, suggest shorter sentences, or remove repetition, without changing the actual facts about the property. The human remains responsible for the factual accuracy of the information; Copilot merely handles the linguistic fine-tuning.

Example 5: A first slide outline in PowerPoint

For an internal presentation on quarterly results, Copilot delivers a first slide outline, complete with suggestions for suitable chart types, based on a short bullet-point text. This mainly saves the tedious first half hour of structuring; the actual substantive elaboration remains manual work.

What the model switch concretely means for users

For daily work in Word, Excel or Outlook, most users hardly notice the switch from one model to the next, since Microsoft largely controls model selection automatically in the background. The topic becomes relevant mainly for IT managers at companies, who can determine in the admin settings which models are even permitted for their organization at all, for example for data protection reasons. For private users, generally only the choice of subscription tier remains, not the individual model selection.

Frequently asked questions about the Copilot models

Is GPT-5.6 available in every Copilot variant?

No. GPT-5.6 is used mainly in Microsoft 365 Copilot and in Microsoft 365 Premium. The free Windows variant sometimes uses simplified or older model versions.

Can I, as a user, choose which model Copilot uses myself?

In enterprise environments, administrators can restrict or enable model selection. Private users generally have no direct model selection, only the choice of subscription tier.

Why does Microsoft use Anthropic models alongside OpenAI?

Since 2025, Microsoft has deliberately pursued a multi-model-provider strategy, so it can deploy suitable models for different tasks and not be dependent on a single provider.

Is GitHub Copilot the same as Microsoft 365 Copilot?

No, GitHub Copilot is a standalone product for software development with its own models and its own pricing structure, sharing only the brand name with the office version.

Are Copilot’s calculation results in Excel reliable?

Copilot provides a good basic structure and suitable formula suggestions, but the substantive check of the values and formulas must still be carried out by a human, especially for business-relevant calculations.

An overview of the individual pricing tiers, data protection questions, and the general assessment of Microsoft Copilot is available on the provider page Microsoft Copilot overview. Anyone wanting to see the other eight providers compared instead will find the overview on the main page comparison of all AI models.

The basic principle: web search plus language model instead of an answer from memory

Why a pure language model alone is not enough

A large language model, such as the kind behind classic chatbots, was trained with huge amounts of text at a certain point in time. After that, it has a great deal of general knowledge, but no automatic access to new events, current figures, or freshly published studies. If you ask such a model about the current base interest rate or a change in the law passed yesterday, it can either honestly say it doesn’t know, or – in the worse case – invent something false that sounds plausible. This is exactly the problem Perplexity solves with a two-stage process.

The interplay: retrieval meets text generation

In the first stage, so-called retrieval, Perplexity searches current web pages, news articles, trade portals and, in some cases, scientific databases for content matching the question asked. These results are filtered, sorted by relevance and trustworthiness, and passed on as a basis to a language model. Only in the second stage does the language model condense these results into an understandable, coherent answer and attach a footnote to every statement pointing to the respective source. You can picture this like a very fast research assistant who first reads ten trade articles and then dictates a summary with source citations – except this process takes only a few seconds.

It’s not that the language model “knows” the answer to a current question – it reads it out of freshly found sources in real time and translates it into understandable language.

What models are specifically behind this?

Proprietary Sonar models for standard queries

For most everyday queries, Perplexity uses its own, internally developed models from the so-called Sonar series. These are specially trained to efficiently evaluate web search results and quickly turn them into a clean, source-backed answer. The advantage of proprietary models lies mainly in speed and cost efficiency – for simple factual questions, the most elaborate available model doesn’t need to be called on.

Access to external top-tier models depending on subscription

For more complex questions, longer analyses, or on explicit request, Perplexity additionally draws on external language models from major providers – depending on availability and subscription tier, for example current models from OpenAI’s (GPT), Anthropic’s (Claude) and Google’s (Gemini) lineups. Perplexity itself does not develop these models but integrates them via programming interfaces and combines them with its own search technology. Pro users can often specifically choose in the advanced settings which model should formulate an answer, while the free version makes this choice automatically in the background.

Model Council: comparing several top-tier models at once

For Max subscribers, there is a special feature, often called “Model Council”: Perplexity processes the same question in parallel with several leading language models at once, compares the results, and summarizes where the models agree, where they diverge, and which aspects each model worked out particularly well. For the average user, this is usually unnecessary effort, but for people who want to thoroughly back up a truly important and complex decision, the model comparison can provide valuable additional perspectives.

Deep Research: multi-step, self-directed research

In addition, there is a mode called Deep Research, in which Perplexity does not just perform a single search but independently plans several search steps one after another: first an overview is established, then specific sub-questions are researched further, and in the end a longer, structured report with many source references is produced. This mode takes considerably longer than a normal query, but delivers more thoroughly grounded results for more complex topics such as market analyses.

Modes and tiers compared

Examples of use from everyday life and work

Example 1: A quick factual question in everyday life

A user wants to know the current inflation rate in Germany. The standard search delivers a short answer within a few seconds, including the current figure along with a reference to the corresponding release from the Federal Statistical Office – considerably faster than clicking through several web pages yourself.

Example 2: Researching current mortgage rates with source citations

A case worker in real estate financing wants to create a rough overview of the current trend in mortgage rates for an internal note. He asks about the approximate interest rate level for ten-year mortgage loans and has the underlying sources displayed, such as current reports from financial portals or interest rate statistics. It’s important here: Perplexity provides good initial orientation with traceable evidence, but does not replace binding financing advice or a same-day bank rate inquiry. Still, the benefit is real for a quick, source-based assessment before a consultation.

Example 3: Summarizing a longer market report or lease

A property manager who uploads a long market report or a multi-page lease as a PDF into a Space can then ask targeted questions about it, such as the key takeaways, notice periods, or specific figures. Perplexity draws on both the uploaded document and, additionally, the current web search to put statements into context – but this does not replace a legal review by a professional.

Example 4: Preparing for a professional discussion with several perspectives

Before an important decision, such as a strategic direction for your own business, the Model Council mode can be helpful: three different top-tier models process the same question, and the summary shows where the assessments agree and where they diverge. This does not replace professional advice, but it does provide a broader viewpoint than a single AI answer.

Example 5: Quick translation and contextualization of foreign-language sources

Since many trade articles and studies are only available in English, Perplexity can incorporate foreign-language sources directly into a German-language summary, including a reference to the original. This often saves the detour of using a separate translation tool.

What does this technology mean for the reliability of the answers?

The combination of web search and language model considerably reduces the risk of freely invented statements, since ideally every claim can be traced back to an actual source. However, an error is not entirely ruled out: the underlying web sources themselves can also be outdated, incomplete, or simply wrong, and a language model can lose nuances when summarizing. The golden rule therefore remains: source citations are an enormous help when verifying, but they are no substitute for your own critical eye, especially for decisions with business or financial relevance.

The more important the decision, the more worthwhile it is to look at the original source behind the footnote – not just at the AI summary.

Frequently asked questions (FAQ)

Which AI model does Perplexity actually use?

There is no single fixed model. Perplexity combines its own Sonar models with external top-tier models from various providers and automatically or, on request, selects the appropriate combination depending on the query, mode and subscription tier.

What is the difference between Pro Search and Deep Research?

Pro Search answers a question in a single pass, while Deep Research independently plans several search steps one after another and, in the end, delivers a more detailed, structured report.

Is the Model Council mode worthwhile for private individuals?

Generally not for simple everyday questions, since the extra effort brings hardly any added value. For truly important, consequential questions, however, viewing the topic from several model perspectives can be worthwhile.

Can I blindly rely on the source citations?

No. The footnotes make verification considerably easier, but they do not replace your own assessment of the source, especially when a decision has financial or legal consequences.

Why does Perplexity sometimes give different answers to the same question?

Since different models and search results can be used in the background depending on load, subscription tier, and sources found, slightly different phrasing or emphasis is normal.

An overview of Perplexity AI as a whole product, its pricing, and its data protection assessment is available on the provider overview page: Perplexity AI: the AI search engine with source citations, overview. A comparison with the other major AI providers in the cluster can be found on the overview page: AI models compared.