{"version":1,"type":"story","url":"https://digestai.news/story/openais-ted-sanders-explains-model-naming-and-sizes-role-in-llm-perfor","json":"https://digestai.news/story/openais-ted-sanders-explains-model-naming-and-sizes-role-in-llm-perfor.json","markdown":"https://digestai.news/story/openais-ted-sanders-explains-model-naming-and-sizes-role-in-llm-perfor.md","slug":"openais-ted-sanders-explains-model-naming-and-sizes-role-in-llm-perfor","headline":"OpenAI’s Ted Sanders explains model naming and size’s role in LLM performance","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.\n\nSanders 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.","keyPoints":["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"],"whyItMatters":"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.","category":{"slug":"models","name":"Generative AI & Models","url":"https://digestai.news/category/models"},"entities":{"companies":["OpenAI"],"models":["Luna","Terra","Sol","Astra"],"people":["Ted Sanders"]},"firstPublishedAt":"2026-10-28T23:23:21Z","updatedAt":"2026-10-28T23:23:21Z","sourceCount":1,"hasPrimarySource":false,"sources":[{"outlet":"Simon Willison","title":"Quoting Ted Sanders","url":"https://simonwillison.net/2026/Oct/28/ted-sanders","publishedAt":"2026-10-28T23:23:21Z","type":"newsletter","primary":false,"lead":true}],"sourceNotes":null,"discussions":[],"thread":null,"cite":{"text":"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","publisher":"Digest AI","title":"OpenAI’s Ted Sanders explains model naming and size’s role in LLM performance","datePublished":"2026-10-28T23:23:21Z","url":"https://digestai.news/story/openais-ted-sanders-explains-model-naming-and-sizes-role-in-llm-perfor"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}