Yesterday, a developer opened up Claude Opus 4.7 to strangers on the internet and said: send me your hardest prompts.
Max effort mode. 1M token context. Full API. u/sweetloup is running community-submitted prompts and publishing the full outputs.
The twist: Look at what people actually asked for when the ceiling was lifted.
Not "write me an email." Not "summarize this article."
They went deep:
- Analyze a whole codebase across 50 iterations to find the best programming language
- Run all of War and Peace through the lens of one character’s eating habits
- Build the strongest theological case for two competing eschatological positions at the same time
When people know the model can actually handle it, they stop asking small questions. That’s the real insight here.
How to design prompts that actually use deep reasoning + huge context:
- 🔍 Pick tasks that require holding a lot in memory at once. Full books, codebases, long research threads.
- 🔄 Add a constraining lens. "Analyze X" is weak. "Analyze X only through the frame of Y" forces depth and specificity.
- 🧠 Ask for iteration or comparison. "Strongest case for both sides" or "50 iterations" puts the reasoning budget to work.
- 📊 Request structured output. At this scale you want organized findings, not a wall of text.
Pro tip: The 1M context window is not for pasting more stuff in. It’s for tasks where forgetting one detail changes the whole answer. Legal cross-referencing, codebase-wide refactors, multi-source research synthesis.
The real unlock: you can finally ask questions you used to have to split across 10 conversations.
👉 The thread is live on r/PromptEngineering. Watch what outputs come back and steal the prompt structures that actually worked.
Frequently Asked Questions
Q: What types of prompts work best with 1M token context and Max effort?
Prompts that require deep reasoning over large amounts of information shine here. The comments show examples like iterative analysis (evaluating 20 programming languages across 50 rounds), detailed literary analysis (tracking themes through entire books like War and Peace), and complex multi-stage business planning. If your prompt needs the model to hold lots of context and connect ideas across it, this is your sweet spot.
Q: Can I develop and refine my prompt before you run it?
Yes! One commenter mentioned wanting to work with you on a pixel art prompt before submission. If you have an idea that’s not fully baked yet, share a draft in the comments, the poster seems open to iterating with you to make sure the prompt is structured right for a full run.
Q: How should I structure really complex prompts to make the best use of all that context?
Break your prompt into clear modules (2, 5 sections), then use Connection instructions to chain them together: “Feed output →” (use the previous output as input), “Build on ⊕” (expand without repeating), “Compare to ⇄” (check results against each other), or “Independent ||” (separate tasks). This helps the model work through multi-stage reasoning efficiently without redundancy.
Q: Can this work for creative or specialized domains like pixel art or image generation?
Absolutely. One commenter is exploring pixel art sprite sheets for vehicle animation and developing a prompt specifically for that. The combination of high context and extended reasoning can help tackle tricky specialized domains where you need the model to think deeply about constraints and iterate toward a solution.
I’m running Redditors prompts on Claude Opus 4.7 at Max effort + 1M context
by u/sweetloup in PromptEngineering