Long‑context LLMs extend the window in which models can read, reason and synthesize information from entire documents, multi‑document sets, or extended research threads. This topic covers how those extended contexts are being used in research, enterprise search, and knowledge management workflows as of 2026‑02‑19, and how platform choices (Anthropic’s Claude Sonnet 4.6, OpenAI’s long‑context GPT variants, Google Gemini and specialist apps) shape results. Relevance: researchers, analysts and knowledge teams increasingly need end‑to‑end handling of long PDFs, multi‑file dossiers and live web grounding without repeatedly chunking inputs. Long contexts reduce context‑switching friction for literature reviews, reproducible analysis and multi‑document synthesis, but introduce engineering and cost tradeoffs (compute, latency, vector‑store design, retrieval and citation hygiene). Key tools and roles: Anthropic’s Claude family (including Sonnet 4.6) provides conversational and developer‑friendly assistants aimed at research, writing and analysis; OpenAI’s long‑context GPT variants offer comparable long‑context capabilities within the GPT ecosystem; Google Gemini supplies multimodal and API access for document and image‑rich workflows. Specialist applications layer UX and retrieval on top of these models: Perplexity AI delivers web‑grounded, cited answers; ChatPDF and PDF.ai convert uploaded documents into chat‑driven knowledge sources; DeeperMind.ai focuses on secure semantic search across corpora; Mindgrasp.ai targets lecture capture and study‑oriented note generation. Trends and considerations: expect continued growth in context window sizes and hybrid pipelines (RAG + long context), stronger tooling for citation, provenance and chunkless summarization, and tighter integration with enterprise KM and PKM tools. Evaluate models on context fidelity, citation support, latency/cost, and how well the provider’s ecosystem (APIs, data controls, retrieval tooling) matches your document and research workflow needs.