How do the leading frontier models — GPT, Claude, Gemini, Llama — actually differ in capability and appropriate use case?

The provided sources offer limited details on direct comparisons among GPT, Claude, Gemini, and Llama, with no information on Llama and sparse coverage of Claude. They focus primarily on recent advancements in GPT and Gemini models. OpenAI's GPT-5.4 is highlighted as a frontier model excelling in professional knowledge work, including state-of-the-art coding, native computer control (e.g., navigating desktop environments via screenshots and actions, achieving 75% on OSWorld-Verified benchmarks surpassing human performance at 72.4%), tool search, a 1-million token context window, improved factual reliability, and editing spreadsheets/documents/presentations [2][5][7][10][12]. It targets competitors like Anthropic's Claude with premium pricing and superior coding capabilities [5]. Google's Gemini models, such as Gemini 3.1 Pro and Gemini Deep Think, emphasize adjustable reasoning on demand and applications in accelerating scientific research, including case studies for expert-level mathematical discovery and collaboration on complex tasks [1][3]. Claude is mentioned only in the context of being challenged by GPT-5.4's coding strengths and as a benchmark for local LLMs tuned for coding/agentic tasks [4][5]. Overall, sources indicate GPT-5.4 for broad professional and agentic use cases, Gemini for scientific and reasoning-intensive research, but lack sufficient data for comprehensive differences or Llama's role.