Quoting Ted Sanders
- ID
- 40628
- Status
- summarized
- Published
- 29 Oct 2026, 7:23 AM
- Fetched
- 29 Oct 2026, 8:28 AM
- Provider
- Simon Willison
- Category
- developer-ai
- Original URL
- https://simonwillison.net/2026/Oct/28/ted-sanders/
- Source URL
- https://simonwillison.net/atom/everything/
Summary
- Score
- 4.5
- Created
- 29 Oct 2026, 8:28 AM
- Tags
- Audience
- developersai_ml_learnersai_agent_userssaas_founders
What happened
Ted Sanders, quoted by Simon Willison, explains that LLM performance is multidimensional, but model size explains much of the variation: bigger models tend to be more reliable, cost more per token, and recall esoteric facts better. Sanders says OpenAI's models are ordered Luna < Terra < Sol < Astra, and that version numbers encode release order, family relationship, and magnitude of improvement — so 6.1 follows 5.6, shares traits with other 6.1 models, and is not the leap a 7 would signal.
Why it matters
Use the tier name (Luna/Terra/Sol/Astra) as the main signal for size, cost, and reliability, and treat a version bump like 6.1 as an incremental update rather than automatic evidence of a major capability jump. When picking a model, compare cost per token and task-specific reliability instead of assuming the newest or highest-numbered model is best at everything.
Discussion angle
If model names mix size tiers and release versions, how should teams decide when a new version actually justifies re-benchmarking and switching — and what internal eval would you run before changing models?