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One model can't cover the whole loop of building solo — planning, coding, debugging, and writing docs each favor a different model. Here's a setup that doesn't mean paying for five subscriptions.

DeepSeek V4 costs a fraction of GPT-5.6 per token. We ran the same everyday tasks through both to find out where the price gap actually shows up in the output, and where it doesn't.

GPT-5.6 now ships as three variants with different speed/cost/reasoning trade-offs. We ran the same five tasks through all three to find where the split actually matters and where it's just marketing.

Kimi K2.7 advertises a huge context window at a fraction of Claude's price. We tested both on a real 180-page contract bundle to see whether "more context" actually means "better answers" once you're past 100 pages.

Anthropic just shipped Claude Fable 5.1 and Claude Mythos 5.1. Fable 5.1 roughly doubles Terminal-Bench-Science scores while cutting prices up to 25-45% — here's whether it's worth switching.

Google's third budget model in six weeks pushes DeepSWE 65.3% to 73.7%, near Claude Opus 5, but costs 40% more per task. What actually changed.

GPT-6 Astra trained on Stargate's 100k+ GPU cluster, launched enterprise-only, now on the API and AIWITH.CHAT. What's confirmed, who should switch.

No - ChatGPT 5.6 isn't free on demand; the free tier quietly routes you to a lighter model. Four routes to GPT-5.6 compared, with real costs.

Perplexity Pro is $20/month for search plus a few frontier models. AIWITH.CHAT is $9.9 for five. How they compare, feature by feature.

Full-time creators don't need every AI subscription — they need the two or three that actually move output. Here's a budget-conscious stack that covers writing, editing, and visuals without stacking five separate bills.

Both models advertise huge context windows. We fed each one the same 80-page document and asked the same questions to see which one actually reads it rather than skims it.

A single AI model has no way to tell you when it's confidently wrong. Here's a lightweight workflow for using a second model as a check, and when it's actually worth the extra step.