Overview of Leading AI Large Language Models and Strategies in 2025
In 2025, the strategy for AI large language models (LLMs) has shifted towards assembling a stack of specialized models tailored for distinct tasks, rather than pursuing a single best model. Users select based on cost, capability, and task focus, treating models as tools rather than personalities. For coding, Claude Opus 4.5 stands out, achieving 80.9% on SWE-bench Verified with strong reasoning and low hallucinations, although it is expensive and has context-window limits for long sessions. DeepSeek V3.2 offers best value at approximately $0.28 per million input tokens and comes with MIT-licensed weights; a Speciale version is also available via API. The emergence of agentic AI, enabling multi-step workflows, browsing, and error recovery, marks 2025's key battleground for autonomous task execution. GPT-5.2 "Thinking" leads in end-to-end execution and tool-calling, with 80% SWE-bench Verified and adaptive routing that balances fast replies with deep reasoning. MiniMax M2 provides a cost-efficient, scalable interactive agent using a sparse Mixture of Experts (MoE) architecture at about $0.01 per 1,000 tokens, making it suitable for knowledge bases and automated summaries. Lastly, Gemini 3 Pro excels with a 91.9% score on GPQA Diamond and 100% on AIME 2025, featuring a Deep Think mode and a large 10-million-token context window for processing extensive documents and papers.
