How 1M Token Context Actually Changed My Daily Workflow

Not theory. Here's exactly how I use it. TL;DR GPT-5.4 and Claude Sonnet 4.6 both shipped with 1 million token context windows this week. I've been testing them in real work — research, writing, code review. Here's what actually works, what doesn't, and the prompts I'm using. The Promise vs Reality The hype: "Feed entire codebases! Analyze whole books! Never lose context!" The reality: More nuanced. 1M tokens is roughly 750,000 words — yes, that's an entire book. But throwing everything at the model doesn't automatically make it smarter. What Actually Works 1. Research Synthesis (My Killer Use Case) The workflow: Fetch 15-20 sources on a topic Paste them all in a single context Ask for synthesis, not summary The prompt: I've included {N} sources about {topic}. Don't summarize them individually. Instead: 1. Find the 3-5 key insights across multiple sources 2. Identify contradictions or debates 3. Note what's missing 4. Give me your synthesis in 500 words max. Why this works: The model c