Last week, I spent three hours trying to debug a 40,000-line Python repository. I needed an AI that could actually hold the entire codebase in its working memory without hallucinating function signatures or forgetting the file structure from two prompts ago. If you're shopping for a high-context LLM in mid-2026, you've probably narrowed your list down to the same two models I did: Google's Gemini 3.1 Pro Preview and Zhipu AI's GLM-5.2 (max).
Last month, I was running a project that required spinning up dozens of automated coding agents to refactor a massive monolithic codebase. Within 48 hours, my OpenAI bill had shot past $300. Claude So
I was deep in a literature review last month, juggling 14 different PDFs across two monitors, and I kept hitting the same wall. I'd feed a 40-page paper into my usual model, ask a specific question ab
# Getting Started with Google Gemini: A Practical Guide I remember the first time I opened Google Gemini—I was both excited and overwhelmed. After spending hours testing prompts, debugging outputs, a