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APIs and tools for building AI products
Grounded in available product and source data
The quality jump is real — outputs feel more intent-aware and less like prompt guessing • Speed + reliability makes it usable for daily, production-level workflows, not just demos • The ecosystem effect is huge: devs, creators, and teams can all build on the same foundation • It’s one of the few AI products that keeps improving without increasing cognitive load What’s most impressive is how OpenAI continues to turn cutting-edge research into something immediately practical. Curious what you’re most excited to unlock next with this release • More transparency and control around model behavior and updates, especially for teams using it in production • Clearer guidance on best practices across different use cases (dev, design, marketing, ops) • Better long-term memory / project-level context to reduce re-explaining complex systems • More predictable pricing and usage limits as capabilities continue to expand Still an incredible product — these improvements would make it even easier to rely on at scale. 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.
These 7 comments were collected from the discovery source. ProductVisit does not host replies in Release A.
Early feedback has been really exciting!! GPT-5.6 is setting a new bar for performance while also reducing token usage and latency, particularly in coding, complex agentic workflows, and tool-heavy tasks.
7 source upvotestwo months ago, @sama posted on X: we still get looksmaxxed on frontend a little but we IQmog hard now nailed it 👏👏
1 source upvotesIs the solar system naming convention around to stay? Or just for the 5.6 release?
1 source upvotesTried it for a quick prototype last week and was surprised how clean the API docs are now. Getting a basic text generation setup running took maybe ten minutes.
Curious how the pricing scales for smaller teams just starting out with API access, especially compared to self-hosting open-weight models.
The “more smarts per token” positioning is interesting. In practice, where have you seen the biggest efficiency gains—long reasoning tasks, coding, or everyday chat?
Programmatic Tool Calling plus explicit prompt caching is the real headline here for anyone running agentic workflows at volume. That's a meaningful cost and latency lever, not just a spec bump. Curious how Sol/Terra/Luna routing works in practice: automatic based on task complexity, or manual per-call?