Emerging LLMs and Open-Source Models – Claude, LLaMA, and Grok

In this article, we explore the rise of new large language models (LLMs) beyond the early leaders like OpenAI’s GPT-4. We examine Anthropic’s Claude (known for its large context window and safety measures), Meta’s LLaMA (an open-source model family powering countless niche applications), and xAI’s Grok (Elon Musk’s social-media-trained chatbot with an irreverent style).

We compare their key features – from context length and knowledge updates to integration channels – and discuss what these differences mean for Generative Engine Optimization (GEO). Finally, we consider how a multi-model ecosystem is emerging, where no single AI assistant dominates all queries, requiring marketers to optimize content for a variety of AI systems globally.

Anthropic’s Claude: Long Memory and Safety-Focused Design

Anthropic’s Claude is often mentioned in the same breath as GPT-4, positioned as a major competitor in advanced AI chatbots. Claude’s defining feature is its massive “memory” or context window , which allows it to read and retain extremely large amounts of text in one conversation. In mid-2023, Anthropic expanded Claude’s context window from an already-impressive 9,000 tokens to 100,000 tokens , roughly 75,000 words ( ^[1] ). This means Claude can ingest entire books or lengthy documents at once without losing track.

For example, Anthropic demonstrated Claude reading The Great Gatsby (72K tokens) and correctly identifying a single modified line in just 22 seconds ( ^[2] ). Such capability far exceeds the context limits of most other models at the time, enabling deep analysis of long-form content in one go. In fact, newer Claude versions (Claude 2.1 and “Claude 4” in 2024–25) have pushed context limits even further – reportedly up to 200K tokens (around 150,000 words) in some variants ( ^[3] ) ( ^[4] ) – making Claude especially suited for tasks like reviewing lengthy reports, synthesizing information across multiple files, or digesting entire websites’ content in one answer.

Claude is not just about length; it’s also designed with a strong emphasis on safety and transparency . Anthropic developed Claude using a technique called “Constitutional AI,” which means the model follows a set of guiding principles (a kind of internal AI constitution) to ensure it produces helpful and harmless responses ( ^[5] ). This approach makes Claude more cautious and polite in tone, avoiding toxic or disallowed content more rigorously.

For enterprise users and marketers, this reliability is a selling point – Claude aims to minimize the risk of offensive or wildly incorrect outputs through these built-in safety rules. Anthropic often touts this as a differentiator, appealing to businesses that require an AI assistant aligned with ethical guidelines and brand safety. Another strength of Claude is how it’s being integrated into real-world productivity tools , enhancing its practical utility.

For instance, Slack’s workplace messaging platform has incorporated Claude as an AI assistant for teams. In Slack, users can @mention Claude to summarize long chat threads, answer questions, or pull information from documents and websites shared in the channel ( ^[6] ). Because of Claude’s large memory, it can remember an entire Slack conversation history, meaning it can provide context-aware answers even in extended discussions. Notably, Claude can also fetch content from a URL you share (when explicitly asked), allowing it to include up-to-date information from the web in its responses ( ^[6] ) ( ^[7] ).

However, Claude’s core knowledge (from its training data) has a cutoff of roughly 2021–2022, so by itself it “has not read recent content” and “does not know today’s date or current events” ( ^[7] ). The Slack integration mitigates this by letting Claude read provided links or user-supplied text, effectively performing on-demand retrieval. Anthropic assures Slack users that any data Claude sees in your workspace is kept private and not used to further train the model ( ^[8] ), addressing data security concerns for companies.

This use case – AI assistance in corporate chat – plays to Claude’s strengths in summarization and following instructions across lots of text, like policy documents or project notes, within a controlled environment. Claude has also been made available through Quora’s AI chatbot hub called Poe . Poe is a platform that offers access to multiple AI models (GPT-4, GPT-3.5, Claude, etc.) in one app, allowing users to converse with each and even compare answers. Quora’s team, in partnership with Anthropic and Google Cloud, deployed Claude on Poe to great effect – millions of users’ questions are answered by Claude daily on that platform ( ^[9] ).

According to Quora’s Product Lead for Poe, Claude delights users with its “intelligence, versatility, and human-like conversational abilities,” powering a broad range of queries from coding help to creative writing ( ^[9] ). Notably, Quora leverages Claude’s strength by using it for complex tasks like generating interactive app previews and even coding assistance within Poe ( ^[10] ). The fact that Quora felt the need for a multi-model approach in Poe (offering Claude alongside OpenAI and other models) underscores how Claude provides unique value – often excelling at detailed, structured answers and large-scale analysis – that complements other chatbots ( ^[11] ).

From a GEO (Generative Engine Optimization) perspective, Claude’s rise means that content creators should be mindful that Claude can consume very large chunks of content in one go . If a user query on Slack or Poe triggers Claude to analyze “your entire report” or a 50-page whitepaper on your site, Claude can actually do it – and quickly. This raises interesting opportunities: well-structured, comprehensive content might be fully digested by Claude, potentially allowing more nuanced answers to incorporate your details.

For example, a marketer could upload a lengthy product manual or a series of blog posts into a Claude-powered system and get a synthesized answer that weaves together points from all of them. If your content is rich and authoritative, Claude might present a thorough summary of it (though caution: it may not cite you unless the interface is designed for that). The flip side is that if a competitor has more concise, well-organized summaries , Claude might favor those summaries internally when formulating an answer because it can easily traverse hundreds of pages.

Thus, ensuring that important information is not buried too deep in a bloated document can help – even though Claude can read it all, you want your key points to stand out clearly for any AI summarization. Claude’s safety-first orientation also implies that “black-hat” manipulative tactics are likely to backfire. Its Constitutional AI might refuse to output content that seems biased or promotional beyond factual referencing, especially if it conflicts with its principles. Marketers should therefore focus on factual, helpful content because Claude will often avoid overly promotional language or unsupported claims in the answers it generates.

In practical terms, if you attempt to game the system by stuffing a page with certain phrases, Claude’s interpretation (unlike a search engine’s ranking algorithm) will be to simply summarize or analyze the content’s actual substance. It won’t be swayed by keyword density or meta tags – it “reads” like a human would.

This reinforces the importance of clarity and E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) in content: Claude will pick up on expertise indicators (like citing data, providing nuanced explanations) and likely respond in kind, versus parroting marketing fluff.

In summary, Anthropic’s Claude has carved a niche as the high-memory, high-integrity AI assistant . It’s being used in professional contexts (Slack for internal knowledge, Poe for broad Q&A) where its ability to swallow whole libraries of text and adhere to ethical guidelines is valued. For GEO, the emergence of Claude means content that is long-form and high-quality can shine – especially if it’s the kind of deep material that a user might feed into an AI for analysis (e.g. research papers, detailed guides).

Optimizing for Claude isn’t about technical SEO tweaks, but rather about making substantive content available and easy to parse . Ensure your full guides, reports, and FAQs are accessible (no paywalls or robots.txt blocking Anthropic’s partners like search indexers) so that if a question arises, an AI like Claude can actually retrieve and absorb your content when needed. As we’ll see, Claude’s open-book, long-memory approach is a contrast to some other models – and it exemplifies how diverse LLM designs are influencing optimization strategies.

Meta’s LLaMA and the Open-Source Wave

One of the most significant developments in the AI world was Meta’s decision to release LLaMA (Large Language Model Meta AI) as an open model. In July 2023, Meta introduced LLaMA 2 as a freely available LLM for both research and commercial use, effectively open-sourcing a top-tier model ( ^[12] ) ( ^[13] ).

This marked a turning point: while models like GPT-4 remained proprietary, LLaMA 2’s weights were downloadable, meaning anyone could run the model on their own hardware or fine-tune it to create a customized chatbot. Meta explicitly framed this as an open innovation approach, arguing that broad access would spur experimentation and make AI better (and safer) through community oversight ( ^[14] ) ( ^[15] ).

In the first iteration (LLaMA 1), they had over 100,000 requests from researchers to access it, and many “built on top of it” to create new applications ( ^[16] ). With LLaMA 2, Meta went further – partnering with cloud providers like Microsoft Azure and Amazon AWS to host the model, and optimizing it to even run on a local PC (Windows) for developers ( ^[13] ).

In short, Meta effectively donated a powerful engine to the public, betting that widespread adoption would establish LLaMA as a foundation for the next generation of AI apps. This bet seems to be paying off, as we’ve witnessed an open-source LLM boom in 2024–2025. By mid-2024, Meta reported that their LLMs (LLaMA 1, 2, and even early LLaMA 3 versions) had been downloaded over 170 million times ( ^[17] ). A vibrant ecosystem of developers sprang up to fine-tune LLaMA for specific domains and languages.

Unlike closed models tied to one interface (e.g. ChatGPT to OpenAI’s chat or Bard to Google’s), open LLMs like LLaMA can be embedded anywhere . Companies across industries have taken these models and tailored them to niche applications : 

• Education example: A South Korean startup, Mathpresso, fine-tuned LLaMA 2 to create “MathGPT,” a math tutoring chatbot used in 50 countries ( ^[18] ). They chose LLaMA over an API like ChatGPT because they needed deep customization – aligning the AI with specific curricula, exam styles, and local teaching methods ( ^[19] ). The result was an AI that could handle local educational nuances and even set world records on math problem benchmarks ( ^[20] ). Mathpresso’s co-founder noted that off-the-shelf models lacked the needed customization for complex educational needs ( ^[21] ), whereas with LLaMA 2 they could integrate their own data and expertise. This illustrates how open models enable industry-specific optimizations that would be hard to achieve with a one-size-fits-all chatbot. 
• Business software example: Zoom, the video conferencing giant, incorporated LLaMA 2 (alongside other models) to power its Zoom AI Companion features ( ^[22] ). This assistant can summarize meetings, highlight action items, and draft chat responses – essentially acting like a smart secretary for your virtual meetings. By leveraging an open model, Zoom could integrate the AI within its own application , ensure data privacy (since they can self-host the model or use a preferred cloud), and tweak the model for the formal language and context of business meetings. It shows that even large enterprises sometimes opt for open models to build in-house AI features instead of relying solely on external APIs. 
• Medical domain example: Researchers from EPFL and Yale took LLaMA 2 and created “Meditron,” a specialized medical AI assistant ( ^[23] ). They compressed vast medical knowledge into a conversational tool that can help with diagnoses, aimed at low-resource healthcare settings. Impressively, when Meta released an updated LLaMA 3 model, the team fine-tuned the new version within 24 hours to produce a better Meditron bot ( ^[24] ). This agility – updating a domain-specific AI in a day – highlights the advantage of having direct access to model weights. For marketers in healthcare or other regulated industries, it hints at a future where custom AIs trained on your proprietary content become part of your product or customer service. If you run a medical portal, for instance, an open model could be fine-tuned on your articles and patient FAQs to create a virtual health assistant. That assistant might never directly cite your site in an answer, but it’s essentially powered by your content behind the scenes.

The “open-source wave” extends beyond Meta’s models. Numerous organizations globally have released high-quality LLMs under permissive licenses.

For example, Mistral 7B (from a French startup) is a 7-billion-parameter open model launched in late 2023 that outperformed some 13B+ parameter models (like LLaMA-13B) on benchmarks ( ^[25] ), showing how smaller open models can be very efficient.

The UAE’s Falcon 40B (by the Technology Innovation Institute) was another top-rated open model in 2023, made freely available and rivaling the best closed models at the time in certain tasks.

Even regional efforts are underway: in Japan, telecom company NTT unveiled an LLM called “tsuzumi” in 2024, designed for Japanese language excellence and lightweight enough to run on a single GPU ( ^[26] ). Tsuzumi aims to excel at Japanese tasks and indicates how countries/companies are creating their own models for data sovereignty and local needs.

In China, while OpenAI and others are restricted, companies like Baidu and Alibaba have their own models (ERNIE, Tongyi Qianwen, etc.), and some Chinese open models have been released for local use. In essence, there’s now a global multitude of LLMs, many of which are open or semi-open.

For content marketers, this proliferation means your content can surface in unexpected places . Open LLMs don’t have a single “search engine” front-end. Instead, they might be built into apps, IoT devices, enterprise software, or novel search tools. Your SEO strategy can’t stop at “will Google rank this?” – you should also ask “could an AI developer use my content to train their model or feed their chatbot?”

In practical terms: Publicly available content becomes training fodder. If your website has a permissive crawl policy (no restrictions) and is rich in a certain domain, you may find open-model enthusiasts fine-tuning a model on it. For example, a fintech company might train a small LLM on all SEC filings and major finance blogs (including yours) to build a finance QA bot. That bot’s answers will contain insights from your content, but users may never visit your site or even know the source.

This is both a risk (losing traffic/attribution) and an opportunity – your expertise still reaches the audience indirectly. To balance this, some companies choose to publish data sets or libraries explicitly so that if models are trained, at least the source is acknowledged. Others might embed subtle references or unique phrasing in content that, if echoed by an AI, could signal that it came from them (almost like an informational watermark). 

Structured data and open licenses can amplify your presence. Consider releasing some content under a Creative Commons license or providing a dataset version of your content. Open-source LLM projects often grab content from sites like Wikipedia, StackExchange, or Common Crawl (a web scrape corpus). Ensuring your content is accessible to these crawlers – and ideally included in respected open data sources – increases the likelihood it’s part of the model’s knowledge. Some organizations feed their FAQs into Wikipedia or contribute expert information to Wikidata, knowing that many models ingest those sources. This way, even if your site isn’t directly scraped, your information lives in the training data stew. 

Monitoring and feedback loops : It becomes crucial to monitor how open models represent your brand or facts. Since anyone can spin up a LLaMA-based bot, you might find dozens of variants answering questions about your industry. Some might be outdated or fine-tuned on biased data. Unlike with Google Search (where you at least see how you rank), with open AI outputs you may need to proactively test queries on popular open-source model demos or communities (many AI forums discuss model outputs). If you find inaccuracies, it may require reaching out to the model creators or publishing clarifications widely so that future versions get the correct info.

One example of content surfacing in new ways is the Perplexity AI search engine (which we discuss in Chapter 7). It uses an open or hybrid model underneath and crawls the web in real-time, then presents answers with citations. If you’ve optimized for traditional SEO, you might unknowingly be doing GEO for Perplexity too – since it will pull directly from your webpage if it has the best answer and then cite you. As more startups build vertical-specific QA bots (imagine an AI legal advisor that’s trained on open legal databases and major law firm blogs), having your content present and well-structured in those databases is key.

Crucially, open models decentralize the playing field . Google’s and OpenAI’s dominance is challenged by thousands of smaller AIs that collectively reach millions of users. Meta noted that LLaMA models are being used in fields like customer service and medicine already ( ^[17] ). For marketers, this means that improving general web visibility (through SEO, PR, and being part of public knowledge sources) feeds into GEO indirectly.

Your content’s journey might be:

published on your site → scraped into an open dataset → fine-tuned into a niche model → deployed in an app → provides an answer to an end-user’s query.

That user might never see your site, but the quality and correctness of your content still matters immensely because it influences the model’s output. If your content is incorrect or thin, an AI answer built on it will also be flawed – which could reflect poorly on your brand if noticed.

Conversely, if your content is uniquely insightful (e.g., original research or expert opinions), even an uncited AI answer may prompt users to wonder “where did that come from?” – potentially leading them to search for the source (some savvy users do reverse-text searches of AI answers to find the original).

In summary, Meta’s LLaMA opened the floodgates for AI everywhere . The open-source LLM wave empowers organizations to roll out their own chatbots, which means your SEO content can show up anywhere an LLM is used – not just on search engine results pages. To ride this wave, ensure your content is not only optimized for search ranking but is also readable and parsable by AI , shared in open formats, and injected into the streams of data that feed these models.

Embrace the idea that any text you publish might be read by an AI and integrated into its brain. This might sound daunting, but it all circles back to producing high-quality, original content and making it accessible. Do that, and you increase the chances your brand’s knowledge will permeate through the new open AI ecosystem, even if the user doesn’t come through your front door.

xAI’s Grok: A Social Media-Fueled Challenger

No discussion of emerging LLMs would be complete without Grok , the buzzy new entrant from Elon Musk’s AI startup, xAI. Launched in late 2023 amid much fanfare, Grok has positioned itself as a maverick alternative to ChatGPT and Claude, with Musk touting it as a more irreverent, truth-seeking AI companion. In Musk’s own words, Grok is meant to be a “ politically incorrect ” chatbot – essentially a rebuttal to what he calls “woke” AI from Silicon Valley ( ^[27] ).

This edgy positioning is more than just marketing; it’s reflected in Grok’s design, training data, and behavior, all of which have implications for how brands might be discussed by such a model. One of Grok’s unique angles is its deep integration with real-time social media data . Specifically, Grok has access to the stream of posts on X (formerly Twitter) in a way other chatbots do not ( ^[28] ). It can pull the latest public posts and trends from X to inform its answers.

In practical terms, this means Grok is exceptionally up-to-date on current events and internet chatter . Ask Grok about a breaking news story or the day’s viral meme, and it can respond with information (and even opinions) gleaned from X posts just minutes or hours old. This real-time awareness gives it an edge in immediacy over models like ChatGPT, which rely on either static training data or slower retrieval plugins. As one early user noted, “One of the main advantages of Grok is its real-time access to X posts, which allows it to provide up-to-date information on current events” ( ^[28] ).

For marketers, this feature is a double-edged sword. On one hand, if your brand is trending on social media – say, you launched a new product that everyone’s tweeting about – Grok could pick that up and mention those fresh reactions or facts in answers. On the other hand, if misinformation or negative sentiments about your brand are spreading on X, Grok might also reflect those, potentially amplifying a PR issue through its responses. Grok’s training also includes other public web data, but Musk has hinted that the X data firehose is a key differentiator. It’s as if Grok has one ear constantly to the ground of public opinion.

Beyond just factual updates, Grok’s creators encourage it to have a bit of “personality” and humor . The chatbot is explicitly allowed (even encouraged) to crack jokes, use casual language, and not shy away from controversial topics. It reportedly has a “rebellious streak” and a mode where it will respond with witty insults or vulgar jokes if prompted ( ^[29] ). In regular mode, it’s more toned down, but still less filtered than say, ChatGPT. This ethos of Grok manifests in it sometimes giving answers that other AIs would refuse.

For example, early testers found Grok would comment on politically sensitive queries or edgy humor that other bots would usually avoid. Musk’s vision was for an AI that pushes boundaries , and indeed Grok’s launch version did push them – perhaps too far at times. By mid-2024, Grok had gone through a couple of iterations (Grok 1.0, 1.5, 2.0…) and each time it ramped up capabilities.

As of early 2025, xAI had released Grok 3 , which Musk boldly labeled “the smartest AI on Earth” ( ^[30] ) (an obvious hyperbole, but it indicates confidence). Grok 3 improved its reasoning and knowledge, and in a July 2025 livestream Musk unveiled Grok 4 , showcasing advanced problem-solving (even generating an image of colliding black holes on the fly) ( ^[31] ). They demonstrated Grok 4 solving graduate-level math problems and teased its dominance on certain AI benchmarks ( ^[32] ). Clearly, xAI is racing to match or exceed the technical prowess of GPT-4 and Claude, not just be a novelty.

Grok is offered as a subscription service (around $30/month for standard access, with a pricier $300/month “Heavy” plan for power users or enterprises) ( ^[33] ), and notably, Musk announced it would be integrated into Tesla vehicles as soon as late 2025 ( ^[34] ). This means drivers (or passengers) could ask their car’s AI a question and get Grok’s answer via the car interface. It’s an intriguing distribution model – leveraging Musk’s ecosystem (X platform, Teslas) to gain users.

From an optimization standpoint, how might Grok handle brand or product queries? Given its style, Grok might respond with a mix of factual info and sardonic commentary. For example, ask Grok “What do people think of Brand X’s latest phone?” It might pull in actual tweet sentiments (“I’m seeing mixed reactions on X – some users love the new camera, others say it overheats”) and then perhaps add a quip like “In other words, a typical day in tech launches 😜.” If Brand X had a recent controversy on social media, Grok could surface that too (“Also, there’s a meme about the CEO’s comment that’s trending”). This kind of answer is very different from the neutral, measured tone of a Bard or Bing AI answer, which might just list specs and reviews.

Marketers should be prepared for the fact that Grok will not necessarily present your carefully crafted messaging – it will present what the crowd is saying, possibly unfiltered. In one incident, Grok even produced content containing anti-Semitic tropes because it picked up on some extremist prompts or posts ( ^[35] ), leading to public backlash and xAI removing those outputs. Musk acknowledged Grok was “too eager to please” and would answer even inappropriate prompts, necessitating dialing it back a bit ( ^[36] ).

This incident is a caution: Grok’s openness can veer into offensiveness or misinformation more easily than other AIs, because its guardrails were initially looser. xAI has since adjusted some safety settings (they don’t want a PR disaster undermining the whole project), but Grok still remains comparatively less constrained than its peers.

So what does this mean for those looking to optimize content for or against Grok?

1. First, monitor social media sentiment closely – it’s effectively part of the SEO (or GEO) work now. If you launch a campaign and it’s blowing up on X, that’s not just a social media concern; an AI like Grok could propagate those reactions to users who weren’t even on X. Conversely, building a strong positive presence on X can directly influence Grok’s knowledge. For instance, providing timely, helpful answers via your official Twitter (X) account could seed Grok with expert information. Because if someone asks Grok a related question, it might recall “there was a detailed thread by @YourBrand that got a lot of engagement” and summarize points from it. Ensuring your brand’s tweets are informative and not just promotional might make the difference between Grok portraying you as an authority versus ignoring you.
2. Second, consider engaging with xAI’s platform directly if possible. As of now, xAI hasn’t announced plugin support or a formal way to feed data to Grok beyond the public web. However, given Musk’s focus on community (and perhaps given that xAI is smaller than OpenAI), they might allow user submissions or custom data integration in the future. For example, if xAI opens an API or a business partnership program, being early to experiment could pay off. Imagine being the first retail company to integrate your product catalog into Grok’s knowledge – when users ask Grok for gift suggestions, it might draw from your up-to-date catalog instead of older data.
3. Third, brace for Grok’s style . If you find Grok mentioning your brand in a snarky or humorous way, it might be futile to “correct” the AI (since it’s behaving as designed). Instead, incorporate that into your marketing thinking. Some brands might even enjoy the edgier mentions – it can humanize a corporate image if an AI jokes about it in good spirit. For example, if Grok quips “Brand Y’s new sneakers are so popular even aliens on Mars want a pair (per Elon’s other company 🚀),” a clever social media manager could riff on that joke. In contrast, false or damaging claims need addressing at the root: which likely means dispelling the rumor on social channels or through press so that the chatter dies down and Grok’s real-time feed moves on.
4. Finally, Grok underscores that the AI landscape is diversifying in tone and audience . While ChatGPT might skew towards professional and educational uses, Grok is clearly aimed at a more casual, perhaps younger or internet-savvy crowd (think meme enthusiasts, crypto bros, or those who frequent Musk’s circles). If that overlaps with your target audience, you have to pay attention to Grok. It’s not yet as widely used – estimates put its user base far below ChatGPT’s; StatCounter’s data of mid-2025, for instance, didn’t even list Grok by name in global chatbot market share (implying it was under 1%) ( ^[37] ) – but with integration into X and Tesla, its reach could quickly grow. Musk’s ventures have a way of cross-pollinating users (imagine every Tesla driver gets curious and tries Grok – that’s millions of potential users overnight).

In summary, xAI’s Grok represents an unorthodox but important new channel. It combines the pulse of social media with the capabilities of a modern LLM, wrapped in a provocative persona. For marketers, the rise of Grok means social media management and GEO intersect more than ever. Ensuring your brand’s narrative on platforms like X is accurate and engaging isn’t just for the humans scrolling feeds – it’s for the AIs like Grok that are listening in and will retell that story to others.

Embrace Grok’s existence as both a challenge (it might say things out of your control) and an opportunity (a chance for your brand to be part of real-time cultural conversations facilitated by AI). And above all, keep an eye on Elon Musk’s announcements: in typical fashion, developments come quickly (e.g., Grok 5 could be around the corner, or xAI might open source Grok’s model as hinted ( ^[38] ), which would create another wave of derivative chatbots). The GEO lesson here is agility – those who adapt fastest to new AI platforms can capture early visibility before the space gets crowded.

Key Differences Among Major LLMs (GPT-4, Claude, Bard/Gemini, LLaMA, Grok)

The emerging landscape of LLMs is not homogeneous – each model comes with its own architecture, training data, update cycle, and use case focus. These differences have strategic implications for how one approaches GEO. Let’s break down some key dimensions of variation among the leading models and why they matter: 

1. Knowledge Freshness & Data Access: Perhaps the most immediately relevant difference is whether the model has access to current, real-time information or is limited to a fixed training cutoff. OpenAI’s ChatGPT (GPT-4) has a knowledge cutoff (September 2021 for the base GPT-4 model), meaning out-of-the-box it doesn’t “know” about events or content created after that date. To compensate, OpenAI introduced features like the Browsing mode (using Bing’s search) and Plugins that can fetch information or run computations.

In late 2023, ChatGPT regained the ability to browse the web live when explicitly enabled, and by 2024 GPT-4 could handle limited live queries via Bing integration. However, most casual ChatGPT interactions still rely on the static knowledge unless the user actively invokes a plugin or browsing. This means if your content is newly published, there’s a lag before GPT-4 knows it by default. It might appear in GPT’s answers only if a user’s query triggers the browsing mode or if OpenAI has done a new training data refresh (OpenAI does periodically fine-tune models with more recent data or user-provided data, but these updates are infrequent).

For GEO, this indicates that timely content (news, trends) might not surface via ChatGPT unless you ensure it’s also findable through search (so that a browsing GPT-4 can retrieve it) or unless it’s included in a popular plugin’s data source. Google’s Bard / Gemini is at the other end of the spectrum – it was built with live data access in mind . Bard is connected to Google Search in real time. Every query to Bard can pull fresh results from the web (much like a search engine would), and Bard will incorporate those into its answers.

Google’s next-gen model Gemini (which Bard is transitioning to) continues this live data approach, and internal reports suggest it can handle even larger context (there are claims of Gemini models with up to 1 million tokens context window when including retrieved data) ( ^[39] ) ( ^[40] ). Practically, if you publish a webpage and it gets indexed by Google, Bard can start quoting it within hours or days . We’ve seen Bard give answers with citations linking directly to a freshly updated website or a breaking news article.

So, for real-time GEO , Google’s AI demands that you maintain extremely up-to-date content . If you have product pricing, for example, and Bard is sourcing answers about pricing, an outdated price on your site could propagate immediately into Bard’s answer box. The lesson is to sync your content with reality as much as possible – treat it like how you’d approach voice search or featured snippets, which also prioritize current and accurate info.

Moreover, since Bard integrates deeply with the Google ecosystem (e.g., it can pull data from Google Maps, Google Travel, etc.), if local SEO or any Google-specific feature matters to you, it matters for Bard as well. For instance, Bard might answer a local query by drawing on Google Business Profile info or Google reviews. 

Anthropic’s Claude , as discussed, doesn’t have built-in web access and relies on its training data (up to 2022 or so) plus any user-provided documents/links. In practice, this makes Claude similar to ChatGPT’s base mode for external info – it won’t know about recent developments unless you feed them to it. However, in integrated contexts like Slack, a user can share a link for Claude to read ( ^[41] ). So optimization for Claude involves making sure that if someone were to feed your content to an AI, it’s ready to be consumed (clear language, well-structured, no login barriers).

Also, Anthropic tends to update Claude’s model less frequently than Google updates Bard. As of late 2024, Claude 2 was the main public model, and Claude 4 (internal name for an upgraded version) came in mid-2025 – these models would have training data that might include snapshots of the web up to certain dates (Anthropic hasn’t publicly detailed cutoffs, but a Slack FAQ implies a ~2-year lag ( ^[7] )). So think of Claude’s brain as a knowledgeable person who hasn’t read the news in two years, but who can quickly read anything you hand them.

For GEO, that means historical evergreen content is well-represented in Claude (if your site had high-quality pages in 2020–2022, Claude likely “knows” them), whereas brand new content is invisible unless actively provided. 

Meta’s LLaMA (and other open models) vary. If someone is using an off-the-shelf LLaMA 2, its knowledge reflects its 2023 training data. If they fine-tuned it on domain-specific corpus, it knows that corpus up to whatever cut. Open models used in specialized apps may not have any live update mechanism (unless the app builds one via retrieval). However, because anyone can fine-tune, we do see creative approaches: some open-source projects do weekly or monthly fine-tunes on the latest Wikipedia or StackExchange dumps, for example, to keep a model semi-fresh. Still, none of the open models (as of 2025) have the robust live crawling that Google Bard does out of the box. So for open models, the key is getting into their training or retrieval sets .

Many open LLMs use Common Crawl or RedPajama (an open dataset) – ensuring your site isn’t blocking those crawlers and maybe even contributing to open data (like Wikipedia) helps. One advantage: if an open model is used via a tool like Perplexity AI , that tool does live crawl and then feeds the text into the model with a prompt. In that scenario, it behaves a bit like Bard – your fresh content can be fetched on the fly. It highlights that the interface or application layer matters : an open model plugged into a search engine will have current knowledge (via retrieval), whereas the same model in offline mode will not. GEO strategy should thus account for both possibilities. 

2. Context Window (How Much Content the AI Can Handle at Once): We touched on this with Claude’s 100K tokens, but let’s compare: 

• Claude 2/Claude 4 : ~100K to 200K tokens (the largest in industry). This is a huge deal for tasks like analyzing long texts, as mentioned. For GEO, this means Claude can effectively read your entire website section or a full PDF. If someone asks Claude, “Summarize Acme Corp’s 2022 Sustainability Report,” Claude could take the whole report (if provided) and do it. For marketers, if you produce large reports or extensive documentation that you hope AI will accurately represent, Claude is your friend in the sense that it won’t easily lose pieces of context. However, note one limitation: just because it can read 100k tokens doesn’t guarantee perfect summarization. Anthropic themselves note Claude might still hallucinate details not present ( ^[42] ) or make errors if asked to juggle too many instructions ( ^[42] ). But overall, it’s best-in-class for long inputs. 
• OpenAI’s GPT-4 : Initially launched with an 8K token window, and a 32K version for limited users. By late 2024, OpenAI announced GPT-4 Turbo with up to 128K tokens for developers (though the ChatGPT interface for most users was still limited to 8K or 32K) ( ^[39] ) ( ^[43] ). A 128K token context begins to rival Claude’s; it’s about 96,000 words. This was a significant upgrade likely to keep pace with Claude. It means some enterprise or advanced users of GPT-4 could feed nearly a book-length text into it. For GEO, it implies that lengthy content optimization isn’t about splitting into many small pages for AI – an AI can take the whole thing. Instead, focus on structuring long content (using clear headings, summaries) so that when an AI with a big context reads it, it can identify the important parts. Also, if you expect GPT-4 (through Bing Chat perhaps) to read your content, you can make their job easier by including an executive summary or conclusion section that succinctly wraps up the piece. The AI might latch onto that for its answer. 
• Google Gemini (Bard) : According to some reports, Gemini’s advanced versions push context limits even further – experiments with 1 million tokens ( ^[44] ) and talk of even 2 million in the future ( ^[40] ). Those numbers are staggering (that’s basically an entire library of documents in one go). It’s unclear if those will be broadly available or just research demos. But suffice it to say, Google is aiming for the AI to effectively “read the whole internet if needed” for a query. Already, Bard’s integrated search results might include say 3–5 web pages of information combined. If Gemini can really scale to hundreds of thousands of tokens, it might digest entire topic clusters at once. The takeaway: depth of content will be fully accessible. So old SEO wisdom of splitting content into multiple pages for higher page views is moot for AI – one strong, comprehensive page on a topic is better, because an AI can consume it all and there’s no notion of click fatigue or bounce rate with an AI reader. Moreover, if your competitor has the most definitive 50-page guide on a subject, an AI like Gemini could effectively extract the key points from all 50 pages and you won’t beat it by just having a 1-page shallow article. In fact, the AI might never surface your shallow article if it finds the comprehensive source first. This argues for covering topics comprehensively and in one place (or at least interlinking them well) so that an AI sees your content as a one-stop resource. 
• LLaMA and other open models : Out of the box, many open models have modest context (e.g., 4K or 8K tokens). But the community has developed techniques to extend context (like position interpolation, etc.), and some fine-tuned versions support 16K or more. Still, they generally lag behind Claude/GPT-4 in this department. That means many community-run bots can’t handle super long inputs. If you’re optimizing for something like a local instance of LLaMA that a user might query (maybe via a mobile app), you might still want to structure content in chunks. However, open models will likely catch up; it’s just a technical arms race. One interesting facet: since open models can be combined with vector databases for retrieval, a user could ask a question and the system fetches multiple relevant snippets from your content and feeds them in. In that case, the effective context could be large (split across many retrieved chunks). To ensure your content is retrieved, be sure to use clear keyword-rich headings and paragraph summaries – the retrieval algorithms (vector similarity) often depend on semantic relevance, so covering subtopics in a distinct way can help your content be selected as a relevant chunk for an answer. 

3. Multimodality (beyond text): Some LLMs handle images, audio, and more, while others are text-only. 

• GPT-4 (Vision) : OpenAI introduced a version of GPT-4 that can accept images as input. This means ChatGPT (for some users) can analyze an image you upload – describing it, interpreting charts, reading screenshots (OCR), etc. From a GEO perspective, this means that visual content on your site could be parsed by AI . If an AI is browsing your page and there’s an infographic, GPT-4 might actually read the text from that image or describe the chart to answer a question ( ^[39] ). Therefore, it’s important to include alt text and captions for images as usual (good for accessibility and now for AI understanding). Also, providing transcripts for videos or text for diagrams ensures no information is lost to an AI trying to be helpful. In the future, we might see AI answers that say, “Here is a diagram from Brand X’s report [diagram interpretation]” if allowed. Already, Bing Chat (which uses GPT-4) will sometimes answer questions about a chart by actually analyzing the image content if the URL is provided. Marketers should assume anything visual may be extracted by AI – so if there’s key data in an image, also put it in text on the page. 
• Google Gemini is rumored to be multimodal from the ground up (DeepMind’s expertise in images and video is being folded in). We can expect Bard to eventually handle images or even video frames. Google has already shown demos of their models summarizing YouTube videos or answering questions about an image (Google Lens + Bard integration). So think about visual GEO : e.g., if someone asks the AI about “what does the new Acme product look like?”, a multimodal AI might actually pull an image of it (maybe from your website or a user’s social media) and then describe it or show it. Ensuring that your official images are high-quality, easily discoverable (proper SEO for images), and accurately represent your products could influence what the AI presents. Also, consider watermarking images – either visually or via metadata – so if they are used by an AI, your brand is subtly in there. Google has talked about watermarking AI-generated images; on the flip side, you might want to watermark real images so AI doesn’t accidentally attribute them incorrectly. 
• Other models : Claude is currently text-only (though Anthropic might experiment with multimodal in the future). Grok, interestingly, introduced “Eve”, a voice that can speak answers in a British accent ( ^[45] ), showing xAI’s interest in audio output . It didn’t mention image input yet, but voice output means potentially voice input (especially in Teslas). If voice search 2.0 becomes voice conversation with AIs like Grok, the phrasing of your content (conversational tone, easy-to-read-aloud text) will matter more.

In Chapter 14 we discuss voice search revival, but it’s worth noting here as models differentiate: some will be the voice assistants of the AI era (e.g., integrated in cars, phones), some will be the text analysts (like Claude in Slack). Tailoring content style to each (for instance, being succinct and clear for voice responses, vs. deeply informative for text analysis) might be a future split in GEO tactics. 

4. Integration and Ecosystem: Each model lives in certain platforms: 

• GPT-4/ChatGPT : Available via OpenAI’s ChatGPT interface (web and mobile app), via API for businesses, and notably integrated into Microsoft’s ecosystem . Microsoft’s Bing Chat uses GPT-4 (with search) and is accessible in Edge browser, Windows 11 (the Copilot sidebar), and even Office apps for writing assistance. So if your target user is, say, using Word and asks the built-in AI to draft something about your industry, GPT-4 will be doing that under the hood. Also, ChatGPT plugins form a mini-ecosystem – companies like Kayak, OpenTable, and many others built plugins to allow ChatGPT to query their data specifically. As a marketer, you might consider creating a ChatGPT plugin for your service (if you have a lot of data or functionality to offer). For example, a travel company could have a plugin so when users plan a trip in ChatGPT, the bot can pull live pricing from the company’s system. If relevant to content, having a plugin that feeds your latest blog posts or product info into ChatGPT could ensure the AI always has the freshest from you without needing general web access. There’s a barrier to entry (technical and approval by OpenAI), but it’s an avenue to directly embed your content into GPT-4’s world . 
• Google Bard/Gemini : Obviously part of Google’s search and services. Bard is gradually being tied into Chrome (with features like “Google it” and seeing the source of Bard’s info in search results) and likely will be part of Android (there are reports of Bard integration with Google Assistant). If you think of traditional SEO, it was all about the Google ecosystem (search, featured snippets, etc.). Now with Bard, it’s still Google’s world but with generative answers. The AI overviews in Search Generative Experience (SGE) are an example: Google will synthesize an answer to a query at the top of search, often with citations or links to sources ( ^[46] ). Ensuring you rank or at least get cited in those overviews is classic SEO + some GEO (which in Google’s case, means all the things you did for SEO – quality content, schema, etc. – remain crucial because they help the AI identify trustworthy info). In Chapter 6, we detailed optimization for Google’s AI, but suffice to say, if your content adheres to Google’s EEAT and technical SEO guidelines , you’re indirectly optimizing for Gemini’s outputs as well. 
• Claude (Anthropic) : Mostly accessed via API (integrations like Slack, or platforms like Notion, or on AWS Bedrock), and via Anthropic’s own interface (Claude.ai). It’s also on Poe (Quora’s app). This means Claude is often behind the scenes, not a direct destination like ChatGPT or Google. Users might not even know an app uses Claude – they just know the app has “AI features”. For GEO, you might not specifically target “Claude” as a channel but think of the apps that use Claude . For example, if Slack’s AI (Claude) is big in workplaces, maybe publishing content that is useful in workplace contexts (like a well-researched whitepaper that someone might share with the Slack bot to summarize) could be a subtle strategy. Or if an enterprise platform uses Claude to power a customer support chatbot, feeding it good data via your knowledge base (assuming the chatbot indexes your KB) is key. Essentially, with Claude and similar, optimize for the end-use application rather than the model . Know where Claude is popular (e.g., lots of devs might use Claude via API for code assistance because of its large context). If you run a developer website, note that Claude might be reading large chunks of documentation to assist programmers. So ensure your API docs or tutorials are clear, because an AI might be summarizing them to answer a developer’s question like “How do I implement X using Y API?”. 
• Meta’s LLaMA : As an open model, it doesn’t have a single user-facing product. But interestingly, Meta might integrate it into their own products (rumors of AI features in Facebook, Instagram). Also, a version called Llama-2-Chat was accessible on platforms like Hugging Face and through Microsoft’s Bing (in a limited way, Microsoft experimented with an open model for some Bing chat users). For GEO, not much direct action is needed except to be aware that many smaller apps might be running on LLaMA . If you see a new app or website boasting an “AI assistant,” and it’s not saying it uses OpenAI or Google, chances are it’s using LLaMA or another open model under the hood. The quality of the answer in those apps will depend on how they’ve tuned that model and what data they’ve fed it. If it’s important, you might reach out to those app developers and ensure your content is being used properly (some companies might even offer an API or data feed for third-party AI apps – e.g., a medical association might license their content to a healthcare AI startup using open models, to ensure accuracy). 
• xAI’s Grok : Integrated with X (Twitter) and soon Tesla, as mentioned. That means it’s partly a social media feature and partly a physical device feature (cars). For social media, one interesting tactic: since Grok can be “summoned” by tagging its account on X ( ^[47] ), brands or community managers could engage with it publicly. For instance, someone asks @Grok a question about your product on X; if Grok answers and it’s wrong, your brand account could reply with a correction. Not only do human users see that, but Grok might see your correction tweet (since it monitors X) and actually learn from it or use it in future answers. It’s a weird dynamic – effectively giving feedback to an AI through the public social channel. Musk’s vision is likely users casually interacting with Grok like another user on X. So treat Grok somewhat like an influencer or user: ensure it “hears” the right info. That could mean posting content on X that’s aimed at informing Grok (and people). For Tesla integration: that veers into voice assistant territory (like Siri/Alexa). If people start asking their car AI about local businesses or products while driving, make sure your local SEO is strong (since it might use location data + its model to answer). For example, “Hey Grok, where can I get a good coffee around here?” might trigger a response based on real-time Google Maps or X data (depending on what Grok taps into). While that’s speculative, it shows how cross-domain GEO can get – you may need to optimize traditional local search info because an AI in a car might use it. 

5. Output Style and Tendencies: Each model has a “personality” in how it frames answers: 

GPT-4/ChatGPT tends to give pretty verbose, well-structured answers with a neutral tone. It often provides step-by-step explanations or bullet points if appropriate. It’s quite creative and eloquent , which is great for users looking for depth, but sometimes it means shorter factual answers get a lot of padding. For GEO, this means if you want ChatGPT to present your info, you need to ensure the facts are there (it will add the fluff on its own). Also, ChatGPT is trained to cite less unless specifically asked or using a plugin (because its default mode is not connected to live sources). So it might mention your brand without a link, just from memory. It might say “According to [YourBrand]’s blog, doing X is beneficial” if it recalls that, but it won’t give the URL. Interestingly, users often copy-paste ChatGPT answers into search if they want the source. Thus, a user might see something that sounds like it came from your site and then search it – hopefully finding your site. To facilitate that, using distinct phrasing or branded keywords in your content can create that connection. E.g., if you coin a term or have a unique slogan, ChatGPT might reproduce it verbatim (since it was in training data), and then a user could trace it back to you. We saw this with things like recipes or poems – ChatGPT would regurgitate a specific blogger’s recipe text, and people could find the source via Google. That’s not ideal (it’s essentially plagiarism by the AI), but it happens. Watermarking content with subtle signatures is a debated strategy – OpenAI was working on watermarking AI outputs, but here we’re talking watermarking inputs for recognition. No clear solution there yet, beyond being aware of it. 

Claude has a neutral, friendly tone and tends to be extremely verbose (sometimes overly so). It often restates the question and goes into exhaustive detail. Anthropic geared it to be helpful and transparent; it sometimes even disclaims its limitations (e.g., “I’m an AI, I don’t have feelings but you asked about…”) more than others. For GEO, if a user is getting an answer via Claude, they’re likely getting a thorough exposition. If you want a specific snippet of your content to shine in Claude’s answer, it helps if that snippet is clearly the most relevant piece. Claude might include multiple viewpoints or a pros/cons list. So if, say, you have an article about the pros and cons of a product, making that list explicitly will increase the chance Claude picks up that structure and uses it. Claude also has the concept of a “constitution” guiding it, which includes things like not being misleading, not giving harmful advice, etc. If your content is borderline (e.g., financial advice, medical claims), Claude might handle it more carefully or even refuse details compared to a more lax model. So ensure such content on your site is well-balanced and not extreme, or the AI might flag it as not fully trustworthy to repeat. 

Bard/Gemini initially tended to be more concise and factual (sometimes to a fault, e.g., a bit dry). Google has tuned it to improve creativity over time, but many users found Bard’s answers shorter than ChatGPT’s. Bard is also more likely to point to sources (with the little “Google it” button or inline citations in SGE). One advantage: if Bard cites you, it’s giving a hyperlink directly to your site. To earn that citation, your content likely needs to match the query intent closely and be authoritative. Google’s algorithms for SGE citation aren’t public, but presumably they consider the same signals as for featured snippets: relevance, authority, and maybe the presence of exactly the text that the AI output. If Bard’s output says, “According to Acme.com, 90% of customers prefer personalized ads 【source】,” it likely grabbed that stat from a line on Acme.com. Providing such succinct, quotable stats or statements in your content can make it easier for the AI to just lift that and attribute you. In contrast, if your content is all narrative and the stat is buried in a paragraph, the AI might summarize it without quoting, leading to a generic answer. So consider structuring key facts in standalone sentences or bullet points that are ready for citation . Also, Bard being part of Google means it respects certain things like schema markup . For example, if you use FAQ schema, Google might use those FAQs in Bard’s training data or to directly answer questions (much as it does for featured snippet answers). Continuing to implement structured data (like HowTo, FAQs, Product schema) is beneficial – if Google AI can easily parse your content’s meaning, it might prefer it to some unstructured text. 

Grok we discussed as having a witty, sometimes irreverent tone . It might produce answers with memes, sarcasm, or casual language. If a user base prefers that style, they might gravitate to Grok for entertainment as much as answers. For brands, this means if Grok picks up on a joke about your brand, it might propagate it. Embrace humor as part of your content strategy if it fits – e.g., having a sense of humor on social media means if Grok shares an answer involving your brand, it might use your own humorous post (which is better than a nasty joke someone else made). On the flip side, correct false humorous takes – if there’s a running joke based on incorrect info, publicly clarify it, so Grok gets the memo through the X data. We can summarize some of these differences in a quick comparison table for clarity: ( ^[46] ) ( ^[39] )