Introduction to Large Language Models

GPT (Generative Pre-trained Transformer) by OpenAI

OpenAI’s GPT series has continued to advance rapidly, with new releases now arriving every few weeks. The current flagship is GPT-5.5, released in April 2026.

GPT-5.5 GPT-5.5 is OpenAI’s most capable model, built for long-horizon agentic tasks — multi-step workflows involving research, tool use, and complex execution that previously required human supervision. It excels at writing and debugging code, analysing data, creating documents and spreadsheets, operating software, and moving across tools until a task is finished. It is available in standard, Thinking, and Pro variants for paid subscribers.

GPT-5.5 Instant Released as the default ChatGPT model in May 2026, GPT-5.5 Instant prioritises low latency for general conversational use while reducing hallucination in sensitive areas like law, medicine, and finance.

Llama by Meta

Llama is Meta’s family of open-weight large language models, available for self-hosting and on-premise deployment — a key advantage for organisations with data sovereignty, cost, or customisation requirements.

The current generation, Llama 4, was released in April 2025 and introduced a Mixture-of-Experts architecture with native multimodal training. Two variants are publicly available: Llama 4 Scout (17B active / 109B total parameters) ships with a 10-million-token context window — the largest of any open-weight model — making it suitable for processing entire codebases, document corpora, or multi-year archives in a single context. Llama 4 Maverick (17B active / 400B total) trades context window for raw capability, competing meaningfully with proprietary models on coding and reasoning benchmarks.

In April 2026, Meta Superintelligence Labs released Muse Spark, the company’s first proprietary closed-weight model, signalling a new strategic direction alongside the open-weight Llama line.

Claude by Anthropic

Claude is a family of AI models developed by Anthropic. The current flagship is Claude Opus 4.7, released in April 2026, accompanied by Claude Opus 4.6 and Claude Sonnet 4.6 (a faster, lower-cost workhorse), and Claude Haiku 4.5 for high-throughput and lower-latency use cases.

Claude has become particularly favoured for enterprise use cases involving long-context document analysis, agentic coding, and tasks where output safety and reasoning transparency matter. Recent versions have introduced extended thinking, native tool use, and the ability to operate web browsers, spreadsheets, and file systems autonomously through products like Claude Code and Claude in Chrome.

Gemini by Google

Gemini is Google DeepMind’s family of natively multimodal foundation models. The current flagship is Gemini 3.1 Pro, released in February 2026.

Built on a Mixture-of-Experts architecture, Gemini 3.1 Pro is designed for advanced reasoning, long-horizon agentic workflows, and complex multimodal tasks. It can comprehend text, audio, images, video, PDFs, and entire code repositories within a 1-million-token context window, and introduces a three-tier thinking system (Low, Medium, High) that lets developers trade off latency against reasoning depth. The model is deeply integrated across Google’s productivity ecosystem — available through the Gemini app, Vertex AI, NotebookLM, Gemini Enterprise, and as a coding assistant in Gemini CLI and Android Studio — making it a natural fit for organisations already standardised on Google Workspace and Google Cloud.

Understanding Large Multimodal Models

LMMs are designed to handle and integrate various input types within a single model — moving beyond architectures that processed each modality through a separate component. The leading frontier models from OpenAI, Anthropic, Google, and Meta are now natively multimodal, meaning vision, audio, and text are trained together from the start rather than bolted on after the fact.

Key Features of LMMs

• Cross-Modal Integration — LMMs combine data from multiple modalities natively, allowing for richer interpretations such as analysing a screenshot alongside descriptive text or interpreting a chart in context with the surrounding documentation.
• Enhanced Learning Capabilities — Processing diverse data types lets models learn complex patterns and relationships that are not apparent from a single modality alone.
• Versatility in Applications — Suitable for use cases ranging from voice assistants to medical imaging analysis, document understanding, video summarisation, and computer-use agents.

Notable Large Multimodal Models (2026)

• GPT-5.5 (OpenAI) — natively multimodal across text, image, and audio, with strong agentic and computer-use capabilities.
• Claude Opus 4.7 (Anthropic) — multimodal frontier model with extended thinking, advanced coding, and strong document and visual reasoning.
• Gemini 3.1 Pro (Google) — multimodal frontier model with deep integration across Google’s productivity and search ecosystem.
• Llama 4 Scout / Maverick (Meta) — open-weight natively multimodal models with full on-premise deployment as an option.

Dynamic Knowledge Integration: Accesses external documents or databases in real-time to supplement responses.

Contextual Relevance: Improves the pertinence of answers by focusing on information directly related to the query.

Enhanced Accuracy: Reduces hallucinations and factual errors common in standalone language models.

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