Cross-Model Continuity
Start architecture with Opus, implement with Sonnet, review with Opus — same context throughout. 35% cost reduction with zero context loss.
Cross-Model Continuity — Start with Opus, Finish with Sonnet
Switch between AI models mid-project without losing context. Opus thinks deeply ($15/M tokens), Sonnet codes fast ($3/M tokens). Cortex Memory persists the reasoning chain between them.
The Problem
Switching models means starting from zero. The Sonnet session doesn't know what Opus decided. Developers manually copy-paste context — losing nuance, decisions, and reasoning trails.
What Changes With Cortex
Phase 1 — Opus analyzes the architecture, creates a thinking session with 4 thoughts and a conclusion. Memory nodes capture key decisions.
Phase 2 — Sonnet starts the session and Cortex loads all context automatically. Sonnet reads the thinking session, understands WHY (not just what), and implements without re-deriving decisions.
Phase 3 — Opus reviews with full history — its own analysis AND Sonnet's implementation notes. Catches a discrepancy between the design and implementation.
The Numbers
| Metric | Without Cortex | With Cortex |
|---|---|---|
| Total cost | ~$14 (all Opus) | ~$9 (Opus + Sonnet + Opus) |
| Context lost | 100% (manual copy) | 0% (automatic) |
| Time on context recovery | ~10 min/switch | 0 min |
| Decision traceability | None | Full thinking session trail |
35% cost reduction. 18% time reduction. Zero context loss.
How It Works
The thinking session is the continuity thread. It's model-agnostic — any model can read and extend it. Cortex's SessionStart hook loads recent context when a new session begins, so the developer never has to explain "what happened last time."
User Dashboard in 30 Minutes
28 files created and edited, 1,831 lines of production code — blueprint planned and implemented in 30 minutes on Opus 4.6 with 98.5% cache hit rate.
Voice Agent Guardrails
A party planning voice agent that stays on-topic, handles emergencies, and never gives medical advice — all in under 5ms per evaluation.