We chose OpenAI because it consistently strikes the best balance between capability, reliability, and developer experience. The models are strong across reasoning, multimodality, and real-world tasks, but what really stands out is how quickly those advances become usable products.
Beyond model quality, the ecosystem matters: stable APIs, clear documentation, and a fast-moving community make it easier to go from prototype to production. Compared to alternatives, OpenAI feels less like a single model and more like a long-term platform we can confidently build on.
GPT-6 Sol and Luna extend OpenAI's newest model family below Astra, trained the same way but tuned for speed and cost.
What makes it different: Sol beats Claude Opus 5 on Automation Bench at roughly 9% of the cost per task, and matches Claude Fable 5.1 on DeepSWE at about 80% lower cost. Both models also carry over Astra's alignment gains, including fewer misleading claims about their own coding work.
Key features:
50% lower API pricing than GPT-5.6 equivalents
Prompt caching improvements, 90% discount on cached input reads
Near-Astra factuality, coding, and computer-use performance
Who it's for: developers and teams running high-volume or long-running agent workflows who need frontier-level performance without frontier-level cost.
Try it via ChatGPT Work, Codex, or through the API
P.S. I hunt the latest and greatest launches in tech, SaaS and AI, follow to be notified → @rohanrecommends
Couch
Does the prompt caching for Sol and Luna work the same way as it does for Astra?