![Hand-drawn robot labeled AI beside a glowing lightbulb, in a playful marker style.](https://aivoyagerimages.blob.core.windows.net/blog-images/meet-the-models-ai-llm-illustration.png) ## Introduction Large language models (LLMs) such as **GPT**, **Claude**, **Gemini**, and **Grok** power many of today’s AI tools. Each is developed by a different organization with unique goals, architectures, and values. This post provides a clear, factual overview of the five most significant models, **GPT (OpenAI)**, **Claude (Anthropic)**, **Gemini (Google DeepMind)**, **Grok (xAI)**, **LLaMA (Meta)**, and **Mistral (France)**, based on verified, publicly available information. For official documentation, see OpenAI, Anthropic, Google DeepMind, Meta AI and Mistral AI ### GPT — The Broadly Deployed Model - Full name: Generative Pre-trained Transformer - Created by: OpenAI - Used in: ChatGPT, Microsoft Copilot, Bing AI GPT (Generative Pre-trained Transformer) is a family of large language models designed to generate human-like text. GPT-4 and GPT-5 support multimodal inputs, meaning they can process both text and images. #### Strengths - High-quality reasoning and creative generation. - Well-integrated with tools like DALL·E, Browsing, and Code Interpreter. #### Limitations - Closed-source; training data details remain undisclosed. - Can produce inaccurate or overconfident results (known as “hallucinations”). #### Benchmark: LMSys Chatbot Arena Leaderboard ranks GPT-4 among the highest-performing general-purpose chat models. ### Claude — The Constitutional AI Approach - Full name: Claude (named after Claude Shannon, father of information theory) - Created by: Anthropic - Used in: Claude.ai, Notion AI, and other apps Claude models (Claude 1–3) use a method called constitutional AI, designed to align responses with a written set of ethical principles. #### Strengths - Extended context length (200K+ tokens in Claude 3). - High accuracy on reasoning and document analysis. #### Limitations - More likely to refuse unclear or risky requests. - Closed-source; limited transparency on datasets. #### Benchmark: See Anthropic’s Claude 3 Technical Overview ### Gemini — Google’s Multimodal Model - Full name: Gemini (formerly Bard) - Created by: Google DeepMind - Used in: Google Workspace, Android, and YouTube Studio Gemini integrates text, code, and visual understanding. It builds on the PaLM 2 architecture and enables multimodal reasoning tasks. #### Strengths - Access to up-to-date web data through Google Search integration. - Multimodal inputs (text, image, audio). #### Limitations - Limited public details on data governance. - Output variability due to live search integration. #### Benchmark: See Google DeepMind’s Gemini 2.5 report ### LLaMA — Meta’s Open-Source Framework - Full name: Large Language Model Meta AI (LLaMA) - Created by: Meta AI - Used in: Research, open-source applications LLaMA (Large Language Model Meta AI) is fully open-weight, allowing developers to fine-tune and deploy it independently. It underpins many community-driven LLMs. #### Strengths - Open-source; enables transparency and reproducibility. - Efficient performance on smaller devices. #### Limitations - Output quality varies by fine-tuning. - Requires technical setup and resources for deployment. #### Benchmark: See Hugging Face Open LLM Leaderboard ### Mistral — The European Open-Weight Competitor - Full name: Mistral AI - Created by: Mistral (France) - Used in: Mixtral 8x7B, open-source deployments Mistral develops efficient “mixture of experts” models such as Mixtral 8x7B, combining open-weight transparency with competitive reasoning ability. #### Strengths - Open-weight and multilingual. - High efficiency in compute usage. #### Limitations - Ecosystem is still emerging. - May underperform on extended creative writing tasks. #### Benchmark: See Mistral AI blog for model specifications. ### Grok — xAI’s Search‑Integrated Model - Full name: Grok - Created by: xAI - Used in: grok.com (web and mobile apps), the xAI API, and integrations that leverage X (formerly Twitter) search and live web browsing Grok is a family of models from xAI that emphasizes native tool use and live web/X search integration. Recent Grok releases (Grok 4 and Grok 4 Fast) focus on cost-efficient reasoning, agentic tool use (browsing, code execution), and very large context windows in certain variants. #### Strengths - Native web/X search and tool use for up-to-date answers and multi-hop research. - High token-efficiency and cost-optimized variants (Grok 4 Fast claims improved price-to-performance). - Very large context windows and multimodal features announced for some Grok 4 models (useful for long documents or agentic workflows). #### Limitations - Closed / proprietary model weights (not open-weight). Vendor-published benchmarks and claims should be balanced with independent evaluations. - Access and advanced capabilities are tiered (free vs. SuperGrok/Premium tiers and API pricing), which may affect availability for readers. - Tight integration with X/web search can increase variability vs. static knowledge models and raises considerations about source provenance and moderation. #### Benchmark: See xAI’s Grok 4 and Grok 4 Fast announcements and model pages for vendor-reported benchmarks and model cards. ### How to Choose a Model

Goal	Recommended Model	Why
General-purpose writing or coding	GPT‑5	Balanced capability and tool integration
Research or long documents	Claude 3	Extended context and safety mechanisms
Fact-checking or education	Gemini	Web connectivity and multimodal data
Real-time search / agentic apps	Grok	Native web/X search and tool-use training; strong for search-augmented or agent workflows
Developer customization	LLaMA or Mistral	Open-weight, adaptable, transparent

**Test Recommendation:** Compare outputs on the same prompt using two models. Differences in tone, accuracy, and reasoning reveal how training strategies vary. ### Maintaining Accuracy Because AI models evolve rapidly, factual accuracy requires updates at least every six months. Readers should verify version numbers and changelogs using official documentation. **Best Practice:** Include a timestamp and direct source for all model claims. ### Conclusion Each large language model represents a different philosophy of artificial intelligence, from OpenAI’s closed commercial systems to Meta’s open research approach. None is universally “best.” Selecting a model depends on user goals, data privacy needs, and technical expertise. This post will remain current through scheduled biannual reviews using official benchmarks and documentation. ### Next steps: When AI Gets It Wrong Now that you’ve met the main characters in the AI world, it’s time to explore their flaws. Even the smartest models still make mistakes. Next we’ll look at: **AI Hallucinations and Bias, When Machines Get It Wrong.** You’ll learn why these systems sometimes “imagine” false information and how to spot it before it misleads you. Until then, keep experimenting, comparing, and asking questions, that’s how real AI literacy begins. — [André Barnard](/about), AI Explorer

Meet the Models: GPT, Claude, Gemini, and Friends

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