"Codex 6.1 sol" is a modifier query: it takes the season's Sol model and asks about it inside the coding context. The directory description is explicit about the pairing — "OpenAI's GPT-6.1 Sol model, delivering near-Astra intelligence at low cost in the Codex programming environment" observed. Our tracker logged the phrase at roughly 0.23× the GPTs baseline and rising, with eight dedicated pages in the field observed. This page explains what the modifier changes, what it doesn't, and how to evaluate the pairing without touching the hype. Labels: vendor claim / observed / analysis.
Strip the modifier and you have GPT-6.1 Sol — a model card question. Add "Codex" and the question becomes situational analysis:
Coding agents are token furnaces: every file read and tool result re-enters the context, so per-token cost compounds across a session analysis. A model positioned as "near-Astra intelligence at low cost" is aiming exactly at that math — sacrifice a little ceiling, save a lot of budget, across thousands of calls analysis. Whether the trade holds for your sessions is testable:
| Field | What we can say |
|---|---|
| Phrase meaning | GPT-6.1 Sol as delivered in the Codex coding environment, per directory description observed |
| Positioning | Near-Astra intelligence, low cost — directory language vendor claim |
| Distinct from | Codex 6.1, described as its own coding model from the same launch window observed |
| Sibling context | Sol announced alongside Luna; Astra the earlier-generation comparator observed |
| Independent benchmarks | None we can vouch for yet — no verified coding-agent evaluations exist for this pairing analysis |
Two names, one pairing: the directory description describes the Sol model within the Codex coding environment observed. No source we captured treats it as a separate model analysis.
No — it is directory positioning language vendor claim. The community thread claiming "out at Astra level" is anecdote observed. Wait for reproducible benchmarks, or generate your own on real tasks analysis.
For short, high-volume tasks, historically yes. For long agentic runs with expensive failures, test whether the ceiling gap bites before migrating analysis.
OpenAI's official documentation and pricing pages. Once you have rates, our calculator models per-session cost analysis.
This page is descriptive and independent, last reviewed October 8, 2026. Not affiliated with OpenAI. "OpenAI", "Codex" and "GPT" are trademarks of their respective owners; third-party claims are reported with attribution and not independently verified.