Large language models (LLMs) are no longer interchangeable.
ChatGPT, Claude, Gemini and Perplexity all feel similar at a glance—natural language in, natural language out—but under the surface they differ in reasoning strength, web access, tools, safety and cost.
For AI search, GEO/AEO and AI SEO work, these differences matter a lot.
In this guide we:
- explain each model family in simple + technical terms
- introduce Tacmind’s LLM Evaluation Model (7 variables)
- compare ChatGPT, Claude, Gemini and Perplexity side by side
- show how the same question behaves across 4 LLMs
- give practical recommendations for marketers and strategists
How to evaluate LLMs: Tacmind’s 7-variable model
Before we compare models, we need a shared evaluation framework.
The LLM Evaluation Model (7 variables)
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Tacmind’s LLM Evaluation Model uses seven variables that matter most for AI search, content and GEO/AEO work:
- Reasoning quality
- How well the model follows instructions, decomposes problems and stays consistent across long workflows.
- Knowledge & web access
- How fresh its built-in training data is, and whether it can search the web in real time (and how transparently it cites sources).
- Multimodality
- Support for images, documents, audio, code and tool use (e.g., browsing, running custom tools).
- Speed & interactivity
- Latency, responsiveness at scale, and whether it supports long context or “infinite chat” style memory.
- Safety & governance
- Guardrails, refusal behaviour on risky topics, enterprise controls and auditability.
- Ecosystem & integrations
- APIs, plugins, app integrations, and how well the model fits into your stack (search, docs, BI, internal tools).
- Cost & scalability
- Pricing, rate limits, availability of lighter variants (mini / flash / haiku) for high-volume tasks.
We’ll reference these seven variables for each model.
ChatGPT (OpenAI GPT-4.1 / GPT-4o family)
Technical definition
ChatGPT is OpenAI’s conversational interface on top of the GPT-4.x and GPT-5 series, with API-accessible models like GPT-4.1 and GPT-4.1 mini that improve on GPT-4o in reasoning, coding and long-context handling.
Simple definition
ChatGPT is the “default LLM” many users think of: a general-purpose assistant that’s strong at reasoning, code, writing and multi-step tasks.
Key strengths (7 variables)
- Reasoning quality – Among the strongest for structured tasks, code, data analysis and complex instructions.
- Knowledge & web – Native browsing and tool calling in ChatGPT; API developers can wire their own tools and search stack.
- Multimodality – Supports text, images, documents and, in some modes, audio/vision.
- Speed – Good balance: GPT-4.1 mini / “small” models for speed, full GPT-4.1 for depth.
- Safety & governance – Mature policies and enterprise controls; conservative on high-risk topics.
- Ecosystem – Richest ecosystem of integrations, frameworks and community content.
- Cost & scalability – Tiered models let you trade depth for price.
LLM citation behaviour
In browsing mode, ChatGPT:
- fetches content via web tools
- often cites a small set of URLs at the end of an answer
For GEO/AEO work this means:
- clear opportunity to become a cited source if your content is structured, trustworthy and aligned with AEO best practices.
Claude (Anthropic Claude 3/4 family)
Technical definition
Claude is Anthropic’s family of LLMs (Haiku, Sonnet, Opus, and newer 4.x models) focused on helpful, honest and harmless behaviour. Claude 3 and 4 models offer strong reasoning, long context and non-English fluency; Opus sits at the high-capability end, while Haiku is optimized for speed and cost.
Simple definition
Claude is the “thoughtful analyst”: excellent at long, careful reasoning, summarising big documents and staying aligned with strict safety rules.
Key strengths (7 variables)
- Reasoning quality – Opus 3/4 and newer 4.5 variants are extremely strong at complex analysis and multi-step logic, especially for enterprise tasks and code.
- Knowledge & web – Built-in browsing in Claude for some tiers; strong at reading long PDFs and sites in-context.
- Multimodality – Mainly text-and-image; less focused on audio or real-time media than some competitors.
- Speed – Haiku is fast and cheap; Sonnet balances speed and depth.
- Safety & governance – Very strong focus on constitutional AI and enterprise safety controls.
- Ecosystem – Growing quickly: integrations into productivity suites, browsers and developer tools.
- Cost & scalability – Clear tiering (Haiku → Sonnet → Opus) lets you scale usage across workloads.
LLM citation behaviour
Claude tends to:
- cite sources when browsing is enabled
- rely heavily on the documents you upload for enterprise use
For GEO/AEO:
- Claude is ideal when you want to test how well your internal corpus and structured documentation support AI assistants.
Gemini (Google Gemini 1.5 / 2.x)
Technical definition
Gemini is Google’s family of multimodal LLMs—1.5 Pro, 1.5 Flash, Nano and newer 2.x models—designed to work across text, images, code and audio, and tightly integrated into Google Workspace and Search.
Simple definition
Gemini is Google’s all-rounder: deeply integrated into Google Search, Docs, Gmail and the broader Google ecosystem, with strong multimodal capabilities.
Key strengths (7 variables)
- Reasoning quality – 1.5 Pro and 2.x handle complex reasoning and coding; Deep Research modes specialise in multi-step web investigations.
- Knowledge & web – Direct pipeline into Google Search; Deep Research uses live queries to build multi-source answers.
- Multimodality – Strong across text, images, audio and video understanding.
- Speed – 1.5 Flash and 2.0 Flash are tuned for latency and cost.
- Safety & governance – Benefit from Google’s safety stack and enterprise controls in Workspace.
- Ecosystem – Best choice if your stack is already heavily invested in Google tools.
- Cost & scalability – Mix of free tier (consumer Gemini) and paid enterprise/API options; Flash variants cover high-volume use.
LLM citation behaviour
In Deep Research and some Gemini Search experiences, the model:
- clearly shows source cards from Google’s index
- blends LLM summaries with classic SERP signals
For GEO/AEO this is an important environment to test, because Google’s AI Overviews and Gemini share underlying retrieval behaviour.
Perplexity (AI answer engine built on LLMs)
Technical definition
Perplexity is an AI-powered answer engine that routes queries to a mix of LLMs and uses real-time web search to deliver cited, concise answers rather than a list of links.
Unlike the other three, Perplexity is not a single model; it orchestrates multiple models through a routing system.
Simple definition
Perplexity is “AI search in production”: its core product is an answer-first search interface that always shows sources.
Key strengths (7 variables)
- Reasoning quality – Depends on the routed model, but answers are usually coherent and citation-backed.
- Knowledge & web – Strongest on fresh web information; every query triggers real-time search.
- Multimodality – Primarily text and web, with some document/image handling.
- Speed – Optimised for search-style interactions; fast enough for day-to-day Q&A.
- Safety & governance – Benefits from model-level safety; still maturing on enterprise controls compared with ChatGPT/Claude/Gemini.
- Ecosystem – Focused product rather than a broad platform; has browser extensions and mobile apps.
- Cost & scalability – Free tier plus Pro plans; less of an API-first play than the others.
LLM citation behaviour
Perplexity’s core differentiator:
- Every answer comes with sources, usually 4–10 links, prominently displayed.
For GEO/AEO:
- It is one of the best environments to test your answer-readiness: if Perplexity never cites you, your AEO and GEO work is probably weak.
Comparative table: strengths by variable
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Case: same question in four LLMs
To make this concrete, imagine we ask all four systems a GEO/AEO-style question:
“How can a B2B SaaS company improve its visibility in AI search results?”
(We won’t quote full answers—models change fast—but we can characterise typical behaviour.)
ChatGPT
- Likely to produce a structured, multi-step plan: improve technical SEO, create AEO-style content, measure AI citations, use tools like Tacmind for GEO.
- Tends to summarise best practices, sometimes referencing AI search features (AI Overviews, answer engines).
Claude
- May emphasise governance and safety: responsible data use, clear documentation, careful handling of claims.
- Often provides thoughtful nuance about trade-offs and how to coordinate SEO, content and product teams.
Gemini
- Likely to connect answers to Google ecosystem: structured data for search, using Gemini for content ideation, leveraging AI Overviews.
- Deep Research mode could show a multi-source analysis including current articles on AI search optimisation.
Perplexity
- Will return a short, citation-rich summary plus sources from across the web—blog posts, research and product pages.
- Great environment for checking whether your brand or site appears in those citations at all.
From a Tacmind perspective:
- Use ChatGPT and Claude for deep strategy and content creation.
- Use Gemini to understand how Google’s stack might surface your content.
- Use Perplexity as a live test bench for your AEO & GEO work.
How to choose the right LLM for AI search & GEO/AEO
Using our 7-variable model, think in terms of jobs to be done:
- Strategy & frameworks (GEO/AEO, AI SEO)
- Content creation and optimisation
- Use ChatGPT, Claude or Gemini Pro depending on your ecosystem.
- Always pair with human editing and Tacmind’s AEO/GEO structures.
- AI search experimentation & benchmarking
- Use Perplexity and Gemini (Deep Research / AI Overviews) to see how AI search surfaces your content.
- Run prompt sets across tools to measure visibility (linking back to our LLM brand audit article).
- Enterprise assistants & internal knowledge
- Claude is strong for long documents and safety;
- ChatGPT or Gemini may be better if you’re heavily invested in their ecosystems.
- High-volume, low-risk automations
- Use lighter tiers: GPT-4.1 mini, Claude Haiku, Gemini Flash—chosen mainly on cost, latency and integration fit.
The important thing is not to “pick a winner” but to design a hybrid model strategy aligned with your AI search and GEO/AEO roadmap.
FAQs
Which LLM is “best” overall?
There’s no universal winner. For complex reasoning and code, OpenAI and Anthropic usually lead benchmarks; for deep Google integration and multimodal features, Gemini is strong; for answer-first search with citations, Perplexity is unique. Your “best” model depends on use case, stack and budget.
Should I standardise on one LLM or use several?
For most organisations, a primary model (e.g., ChatGPT or Claude) plus specialist tools (Gemini, Perplexity) works best. Tacmind can sit above that mix, keeping your AEO/GEO strategy consistent regardless of which LLM you query.
How often do these comparisons go out of date?
Very quickly. New versions (GPT-4.1, Claude 4.5, Gemini 2.x, Perplexity routing changes) ship frequently. Treat this article as a framework and re-check specific capabilities every few months.
Are there big differences in safety between models?
All major vendors enforce strong safety policies, but Anthropic and Google emphasise formal safety frameworks and constitutional approaches; OpenAI has extensive tooling and monitoring; Perplexity inherits safety from underlying models. For regulated industries, always review vendor docs and legal terms directly.
How does this tie into AEO and GEO?
- AEO: you care about how each model selects and cites your content when answering questions.
- GEO: you care about how these models describe your brand and frameworks across many prompts.
- Model differences change how you optimise—but Tacmind’s underlying AEO & GEO principles stay stable.
Where should I start if I’m new to LLMs?
Start with:
- A primary assistant (often ChatGPT or Claude).
- Perplexity to see AI search in action.
- A simple Tacmind project to map your core topics and build AEO-ready pillar content.
Then expand into APIs and automation later.
Conclusion & next steps
Comparing LLMs isn’t about hype—it’s about choosing the right engines for your AI search and content strategy.
Tacmind’s LLM Evaluation Model (7 variables) gives you a structured way to think about:
- reasoning
- web access and multimodality
- speed, safety, ecosystem and cost
Across ChatGPT, Claude, Gemini and Perplexity, you’ll likely use more than one model as AI search and GEO/AEO become core channels.
From here, a practical next step is to:
- Map your top 3–5 AI “jobs” (strategy, content, experimentation, internal assistant).
- Choose the LLM (or combination) that best fits each job using the 7-variable model.
- Use Tacmind to design AEO/GEO-ready content architectures, then test them across these LLMs—turning model differences into an advantage instead of a source of confusion.
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