Comparisons
Honest side-by-side analysis of local and cloud AI — model against model, app against app, architecture against architecture, including where on-device still loses.
Is Local AI Good Enough Yet? An Honest 2026 Assessment
Where on-device models have genuinely caught up, where the gap to frontier models is still large, and how to tell which side of the line your own work falls on.
ChatGPT vs a Local LLM: An Honest Everyday Comparison
Where a frontier cloud model genuinely beats a local one, where the gap has closed, and how to decide which to reach for — without pretending either side wins everything.
Private ChatGPT Alternatives: A 2026 Guide to AI That Can't Store Your Chats
Every category of private AI alternative, honestly assessed — local apps, self-hosted models, privacy-focused proxies, and enterprise modes — with the trade-offs each one actually makes.
Best Local LLM Models for iPhone in 2026
A hands-on comparison of the 11 local LLM models you can run on iPhone — Qwen 3.5, Qwen 3, Llama 3.2, Phi-4 Mini, Ministral 3, and DeepSeek R1. Real sizes, real RAM requirements, real speeds.
On-Device AI vs Cloud AI: Privacy, Speed, and Cost Compared
A direct comparison of on-device AI and cloud AI across privacy, latency, cost, and offline capability — so you can make an informed choice about where your conversations actually go.
Best Offline AI Apps for iPhone in 2026
A practical comparison of the best apps for running AI locally on your iPhone — what each one does well, which models they support, and how to choose the right one for your use case.
Qwen 3 vs Llama 3: Which Runs Better on iPhone?
Qwen 3.5 4B and Llama 3.2 3B are the two most capable on-device language models for iPhone. Here's a direct comparison of their sizes, performance, thinking modes, and which tasks each handles best — with a clear recommendation for most users.
Apple Intelligence vs Open Source On-Device AI: An Honest Comparison
Apple Intelligence and open source on-device AI like Cloaked both run AI locally, but they take fundamentally different approaches to models, privacy, and hardware requirements. Here's a fair look at the trade-offs.