Which is the Best LLM?
Author: Simon Kingsnorth
09 July 2026
In this article, I want to give you something more useful than a generic ranking of the leading LLMs, I want to provide you with a clear picture of where each of the four leading models genuinely excels, where each one struggles, and how to build a deliberate AI strategy rather than defaulting to whatever tool is most famous.
ChatGPT, Gemini, Claude and Microsoft Copilot dominate the general-purpose category that most marketing, strategy and communications teams are actually using. Understanding them properly matters more than most organisations currently appreciate.
How does the LLM market actually break down right now?
Before we compare the tools, it is worth understanding who is actually using what, because market share tells you something important about ecosystem maturity, support quality and long-term reliability.
ChatGPT remains the clear leader by consumer traffic. It holds roughly 54% of worldwide AI chatbot web visits as of mid-2026, though that figure has dropped sharply from the 76-77% share it commanded in early 2025. Gemini has been the biggest beneficiary of that decline, growing from under 6% to nearly 28% of global web traffic over the same period. Claude has seen the fastest recent growth rate of all, tripling its share in a single quarter to reach around 9% worldwide, with US share approaching 13%.
Microsoft Copilot tells a more nuanced story. Its standalone consumer web presence sits at around 1-2% of traffic, which might look underwhelming, but that figure captures only a fraction of its actual footprint. Copilot is embedded directly inside Microsoft 365, Windows, and enterprise Azure infrastructure, which means the majority of its usage never shows up in standalone web traffic data at all. By some estimates, Microsoft’s generative AI tools now reach 85% of Fortune 500 companies through its platforms.
The more revealing picture for enterprise specifically is this: Claude holds an estimated 32% share of enterprise LLM deployments, compared to roughly 25% for OpenAI, with a particularly dominant position in code-heavy and compliance-sensitive sectors. That is a very different story from the consumer numbers, and it matters enormously for anyone making AI procurement decisions in a corporate environment.
The market is no longer winner-takes-all. We are moving into a multi-model world, where the most effective organisations are not asking “which AI should we use?” but “which AI should we use for this specific task?”
What is ChatGPT best at and where does it fall short?
ChatGPT is the one that almost everyone has tried. It launched the category, it has the largest user base, and it has done more than any other tool to normalise the habit of using AI in daily work. That familiarity is genuinely valuable. The interface is polished, the ecosystem around it is enormous, and it handles a remarkably wide range of tasks with reasonable competence.
Is ChatGPT the most versatile AI tool available?
For breadth of use, yes, it is hard to beat. ChatGPT manages research synthesis, draft copy, brainstorming, image generation, basic data analysis, and coding assistance all within a single, relatively seamless experience. For a marketer who needs to jump between tasks quickly, that versatility is real.
The GPT model also has the deepest third-party integrations. Thousands of tools plug into it via the API, which means if you are building a workflow or an internal tool, the ecosystem of support is genuinely mature.
Where does ChatGPT struggle?
The criticism I hear most consistently from experienced users is that ChatGPT tends towards agreeableness. It often tells you what you want to hear rather than what is actually most useful. That matters more than it sounds. If you are using it to pressure-test a strategy or interrogate assumptions, an AI that defaults to validation is actively unhelpful.
There are also documented hallucination concerns. Enterprise chatbot deployments report hallucination rates of around 18% in live interactions, meaning roughly one in five responses contains something that is presented confidently but is factually incorrect. For high stakes work in financial services or regulated technology sectors, that is a meaningful risk without robust verification processes in place.
ChatGPT also has a knowledge cutoff issue that affects research quality. While it can now access the web, it does not always do so reliably, and users have learned to treat its confident assertions with appropriate scepticism.
When should marketers use ChatGPT?
ChatGPT is particularly well suited to:
- Rapid ideation and creative brainstorming sessions
- High-level research and background synthesis
- Simple copy drafts that will be reviewed and edited by a human
- Image generation via DALL-E integration
- Generating options quickly when you need volume rather than depth
Think of it as the most accessible starting point for most tasks, but not necessarily the finishing line.
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What is Gemini best at and where does it fall short?
Google’s entry into the LLM space has been transformed over the past 18 months. Gemini’s growth, from under 6% to nearly 28% of global AI traffic in a single year, is not simply a reflection of product quality. It reflects something more strategic: distribution.
Gemini is embedded across Google Search, Workspace, Chrome, Android and hardware. Users encounter it in the tools they are already working in, which dramatically lowers the barrier to adoption. For marketers already operating within the Google ecosystem, this is genuinely significant.
Is Gemini the best LLM for SEO and Google integrations?
Yes, in most cases it is. Gemini’s integration with Google Search Console, Google Analytics, Google Ads and the broader Workspace suite give it a native advantage that none of the other models can match. If your team lives in Google Docs and Sheets, Gemini can pull data, generate summaries and surface insights from within those environments without requiring you to copy, paste, or switch context.
For SEO-adjacent tasks, keyword research, content gap analysis, structured data suggestions, the connection to Google’s own indexing logic is a meaningful advantage. Gemini also has strong multimodal capabilities, handling text, images, video and audio within a single interface, which makes it genuinely useful for teams working across mixed media.
Where does Gemini struggle?
Gemini’s weaknesses tend to show up in creative and long-form work. The outputs can feel more functional than inspired, strong on accuracy and data handling, less strong when you need originality or nuance. It has sometimes been described as more science than art, which is a fair characterisation for many use cases. Having said that, with a subscription you can access the Veo video creation which, with effective prompts can create strong AI videos.
The user experience has historically been more basic than ChatGPT or Claude, though this is improving with each iteration. And while the Google ecosystem integration is a major strength for those who are already there, it is less useful for organisations operating primarily in Microsoft or other environments.
When should marketers use Gemini?
Gemini is particularly effective for:
- SEO-related research and workflow automation
- Analysing large datasets within Google Sheets or Workspace
- Multimodal content projects combining text, image and video
- Teams operating primarily within the Google ecosystem
- Content performance analysis connected to Google’s own data sources
What is Claude best at and where does it fall short?
Claude is the model that often surprises people the first time they use it properly. Consumer market share numbers do not tell the full story, the growth rate is accelerating faster than any competitor, and in enterprise deployments, Claude has quietly become the preferred model for the most demanding use cases.
Is Claude the most powerful AI for complex reasoning and strategy?
The evidence suggests yes. Claude holds a 32% share of enterprise AI deployments, higher than OpenAI’s 25%, and leads significantly in code-intensive and compliance-sensitive sectors. In code development specifically, Claude commands a 54% share among enterprise customers, more than double OpenAI’s figure.
What sets Claude apart is the combination of reasoning depth, long-form writing quality and context window capacity. Claude Enterprise handles up to 500,000 tokens of context, equivalent to hundreds of thousands of words, which means it can analyse entire document sets, lengthy transcripts or complex regulatory filings in a single session. That is transformative for financial services firms dealing with dense documentation.
Claude also tends to push back when it disagrees, which makes it far more useful than ChatGPT for critical thinking tasks. Rather than validating your assumptions, it is more likely to surface the weaknesses in them. For strategic work, that is exactly what you want.
Where does Claude fall short?
Speed is the most consistent criticism. Claude is slower than ChatGPT and Gemini, which matters for high-volume, rapid-fire use cases. The outputs also tend to be wordier by default, more thorough, but requiring more editing to distil into lean final copy.
Claude’s consumer brand recognition remains lower than ChatGPT’s, which means teams without deliberate AI adoption programmes are less likely to discover or default to it. That is less a product limitation than a distribution one.
When should marketers use Claude?
Claude is particularly well suited to:
- Strategy development and complex problem-solving
- Long-form content writing requiring nuance and depth
- Deep analysis of lengthy documents, reports or research
- Regulated industries where reasoning transparency matters
- Connecting platforms and data for real time analysis of performance marketing
- High-quality content where the editing investment is worth the output quality
What is Microsoft Copilot best at and where does it fall short?
Copilot occupies a distinct position in this comparison. Where ChatGPT, Gemini and Claude are general-purpose assistants you go to, Copilot is an assistant that comes to you, embedded inside the tools you are already using.
Is Microsoft Copilot the best AI for enterprise productivity?
For organisations running on Microsoft 365, the answer is a strong yes. Copilot integrates natively with Word, Excel, PowerPoint, Outlook, Teams and the broader Microsoft ecosystem. It can generate reports from data in Excel, summarise email threads, draft slide decks from briefs, and surface relevant documents from SharePoint, all without leaving the interface where the work is actually happening.
The privacy controls are also notably stronger than the alternatives for most enterprise scenarios. For financial services and technology firms handling sensitive or regulated data, that matters enormously. Copilot is built around Microsoft’s enterprise compliance infrastructure, which gives security teams a level of confidence they often cannot get with consumer-focused AI products.
Copilot grew 42% year-over-year in web traffic quietly, without significant marketing noise largely because adoption is driven by IT procurement and existing Microsoft agreements rather than individual user choice.
Where does Copilot struggle?
Outside of the Microsoft ecosystem, Copilot’s value proposition weakens considerably. Its performance on open-ended creative tasks, long-form writing and complex reasoning lags behind Claude and even ChatGPT. Users describe it as sometimes feeling constrained, well-suited to structured business tasks but less useful for exploratory, creative or research-heavy work.
The filtering on non-work-related topics can also frustrate users who want a more flexible AI conversation partner. Copilot is, by design, a professional productivity tool first.
When should marketers use Copilot?
Copilot is best deployed for:
- Internal reporting and structured business documents
- Sensitive, confidential or regulated tasks requiring stronger privacy controls
- Excel-based data work, modelling and analysis
- Day-to-day productivity within the Microsoft 365 environment
- Email drafting, meeting summaries and Teams-based collaboration
SK
Simon is CEO is of SK, a global strategic marketing agency that works with companies of all sizes to build smart marketing strategies.
SK works with global corporations and start-ups, especially in the financial services and technology sectors to deliver growth and improvement across a wide range of disciplines..
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Which LLM should marketers use for content and copywriting?
This is the question most marketing teams are wrestling with, so it deserves a direct answer rather than a hedge.
For first-draft volume at speed, ChatGPT remains the practical choice for most teams. It is fast, accessible and handles a wide enough range of briefs to be genuinely useful across a content calendar.
For long-form content requiring original thinking, structural rigour and the kind of depth that holds up under scrutiny, Claude is the stronger option. The additional editing time it typically requires is usually offset by the quality gap in the underlying draft.
For content specifically designed to support SEO or Google-adjacent campaigns, Gemini offers integration advantages that are worth considering, particularly if your workflow is already Google-based.
The most sophisticated marketing teams are not choosing one. They are running a multi-model workflow: ChatGPT or Gemini for ideation and rapid drafts, Claude for strategy briefs and high-stakes content, Copilot for internal communications and reporting.
Which LLM is best for data analysis and reporting?
For structured data analysis within a defined ecosystem, Gemini’s Google Workspace integration gives it a practical edge for Google-native teams. Copilot leads clearly for Microsoft-native organisations working in Excel and Power BI.
Claude’s extended context window makes it the strongest option for analysing large, unstructured documents, regulatory filings, lengthy reports, due diligence packages where synthesising hundreds of pages at once is the core requirement.
ChatGPT’s data analysis capabilities have improved substantially and are entirely serviceable for moderate complexity tasks, though it lacks the native integration advantages of the others.
What do hallucination rates tell us about trusting AI output?
This is the part of the LLM conversation that does not get nearly enough attention, particularly in financial services and technology sectors where the cost of error is high.
Hallucination where an AI presents incorrect information with confidence, is not a rare edge case. Enterprise benchmarks report hallucination rates of between 15% and 52% across commercial LLMs for complex tasks. Production chatbot deployments average around 18% in live interactions. And McKinsey’s research suggests that only 1% of companies consider themselves genuinely AI-mature, meaning 99% are deploying AI without the quality frameworks to systematically manage these failure modes.
The practical implication for any marketing or communications team is straightforward: every AI-generated output that will be published, presented or used to inform a decision needs human review. This is not a counsel of excessive caution, it is the minimum responsible standard.
The good news is that hallucination rates fall sharply when AI is given well-grounded, structured prompts connected to verified sources. The worst hallucination outcomes happen when AI is asked to generate factual content from general knowledge without constraints. The best outcomes happen when AI is asked to synthesise content from provided sources, with a human checking the output.
Building that discipline into your workflow matters more than which model you choose.
How does your choice of LLM affect your AEO strategy?
Answer Engine Optimisation has moved from emerging concept to strategic priority over the past 12 months, and your LLM ecosystem has direct implications for how you approach it.
AI search is growing at a pace that is impossible to ignore. ChatGPT alone now processes over 2.5 billion prompts daily. AI-referred traffic to websites grew 527% year-over-year through mid-2025. And conversion rates from AI-referred sessions are substantially higher than from traditional organic search, one insurance provider recorded 3.76% conversion from LLM traffic compared to 1.19% from organic search.
But the four models we have discussed here cite from different sources and weight credibility differently, which means your AEO strategy needs to account for each one.
ChatGPT tends to favour content that appears consistently across multiple credible sources. Repetition of authoritative signals across your blog, LinkedIn presence and third-party publications matters.
Gemini pulls from Google’s own index, meaning strong organic search rankings directly feed Gemini visibility. If you are ranking in the top 10 for a query, your chances of appearing in a Gemini answer are substantially higher.
Claude prioritises verifiable credibility and uses Brave Search as its retrieval layer rather than Google’s index. That makes Brave indexing a separate, often overlooked optimisation target. Claude also responds well to content that links every meaningful claim to a primary source, and that is transparent about its expertise and reasoning.
Copilot uses Bing as its primary search layer, meaning Bing indexing and citation signals matter in ways that many marketing teams, who optimise exclusively for Google, tend to overlook.
The broader AEO principle that applies across all four models is structural clarity. Content that answers one specific question per section, uses question-based subheadings, cites primary sources, and is kept fresh with quarterly updates is significantly more likely to be cited than content optimised for traditional SEO patterns alone.
How should you measure the performance of your AI tools?
Most organisations are not measuring their AI tool performance systematically, which means they are making investment decisions on instinct rather than evidence.
A practical measurement framework covers three areas.
The first is output quality. This means tracking revision rates, how often AI-generated drafts are substantially rewritten before use and correlating AI usage with content performance metrics such as engagement, conversion and search visibility over time.
The second is hallucination and accuracy. Building a simple review process that flags factual errors in AI output, and tracking error frequency by tool and task type, gives you genuine data to inform which model to use for which job.
The third is AEO visibility. Tools including Semrush, Profound and Advanced Web Ranking now track brand citation share across ChatGPT, Gemini, Perplexity and others. Monitoring where your brand appears in AI-generated answers, and tracking that against content investment, is increasingly central to understanding your organic visibility.
The organisations that will be best positioned in two years are the ones building these measurement disciplines now, while most competitors are still treating AI as a productivity hack rather than a strategic capability.
What is the right AI strategy for technology and financial services firms?
For most technology and financial services organisations, the answer is not one model but a deliberate framework for deploying each tool where it performs best.
The starting point is an audit of your current AI usage, which tools your teams are using, for what tasks, with what governance, and what the output quality looks like. In most organisations, AI adoption has happened bottom-up and inconsistently. Getting a clear picture of what is actually happening is step one.
From there, the strategic build is roughly as follows. Use Claude or ChatGPT for high-value marketing content, strategy documents and anything that will represent the organisation externally. Use Copilot for internal productivity, reporting and anything touching sensitive or regulated data. Use Gemini for Google-ecosystem workflows, SEO research and multimodal content projects.
Underpin all of it with AEO thinking, structuring content so that each model can find, retrieve and cite your expertise when prospects are searching in AI environments. And build the measurement infrastructure to track whether it is working.
At SK, we help technology and financial services clients develop AI platforms, embed AI into their marketing strategies and improve productivity through AI systems that are properly governed and genuinely effective. If you want to move beyond ad hoc AI use and build something deliberate, we would be glad to talk.
Summary
- The LLM market is fragmenting fast. ChatGPT holds around 54% of consumer web traffic but is losing share rapidly to Gemini and Claude. In enterprise deployments, Claude leads with an estimated 32% share.
- ChatGPT is the most versatile all-rounder — best for rapid ideation, simple copy drafts and broad creative tasks, but prone to agreeableness and hallucination without strong prompting discipline.
- Gemini leads for Google ecosystem integration — the strongest choice for SEO workflows, large dataset analysis and teams operating within Google Workspace.
- Claude is the highest-quality reasoning and writing model — slower and wordier than alternatives, but the preferred enterprise choice for strategy, long-form content and complex document analysis.
- Microsoft Copilot leads for Microsoft 365 productivity — the best choice for internal reporting, sensitive data tasks and structured business work within the Microsoft environment.
- Hallucination rates remain a meaningful risk — enterprise deployments average around 18% error rates in live interactions, making human review non-negotiable for high-stakes outputs.
- Your choice of LLM directly affects your AEO performance — each model cites from different sources and weights credibility differently, requiring a model-aware content strategy.
- The most effective organisations are not choosing one model — they are running deliberate multi-model workflows, using each tool where it performs best.
- Measurement is the missing piece for most teams — tracking output quality, hallucination rates and AI citation share is essential for making evidence-based AI investment decisions.
- For technology and financial services firms, AI strategy should be deliberate, not accidental — the firms building governance frameworks and measurement infrastructure now will compound a significant advantage over the next two years.