# OpenAI’s Ted Sanders explains model naming and size’s role in LLM performance

Digest AI · Generative AI & Models · published 2026-10-28T23:23:21Z

Canonical: https://digestai.news/story/openais-ted-sanders-explains-model-naming-and-sizes-role-in-llm-perfor

## Summary

Ted Sanders, a figure at OpenAI, clarified how the company’s model naming reflects performance and size. He noted that larger models tend to be more reliable, costlier per token, and better at recalling niche facts. OpenAI’s model hierarchy—Luna, Terra, Sol, Astra—orders them by release and improvement magnitude, with version numbers like 6.1 indicating incremental updates rather than major leaps.

Sanders emphasized that model performance varies across tasks, with no single metric ranking all models consistently. Version numbers also reflect shared training data or personality traits, avoiding a sequential numbering system (e.g., skipping to 6.1 instead of 5.7). The post underscores OpenAI’s approach to model iteration and transparency in its naming conventions.

## Key points

- Larger LLMs cost more per token but improve reliability and recall of niche facts, per OpenAI’s Ted Sanders
- OpenAI’s model hierarchy (Luna < Terra < Sol < Astra) orders releases by performance, not just size
- Version numbers like 6.1 signal incremental updates, not major breakthroughs, Sanders explained

## Why it matters

OpenAI’s insights into model sizing and naming clarify how its LLMs evolve, helping developers and users gauge trade-offs between cost, performance, and capability.

## Sources

1. [Quoting Ted Sanders](https://simonwillison.net/2026/Oct/28/ted-sanders) (Simon Willison, 2026-10-28)

## Cite

Digest AI, "OpenAI’s Ted Sanders explains model naming and size’s role in LLM performance", 28 October 2026, https://digestai.news/story/openais-ted-sanders-explains-model-naming-and-sizes-role-in-llm-perfor

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