GPT-6 Sol vs Luna: Security, Cost, Routing

GPT-6 Sol and GPT-6 Luna are positioned as lower-cost OpenAI models derived from GPT-6 Astra’s capabilities, with Sol aimed at complex coding and multi-step agent tasks and Luna designed for fast, high-volume, well-defined workloads. Luna costs 20 times less per input token than Sol, although independent estimates cited in the comparison suggest its higher token use reduces per-task savings to roughly 12 times and that it trails Sol on intelligence-oriented benchmarks while operating faster. GPT-6.1 Sol retains Sol’s standard pricing, lowers cached-input costs, and is presented by OpenAI as offering near-Astra agentic performance, though it is also classified as critical for cybersecurity and may require stricter access controls. The comparison advises organizations to validate models on their own workloads, use rule-based escalation from Luna to Sol or Astra, account for retries and output volume rather than token prices alone, and apply security measures such as gateway enforcement, tool permissions, redaction, code scanning, logging, human approval, and separate red-team testing for each model version.

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