GPT-5.1 represents a meaningful step forward in LLM capabilities. Three key improvements stand out:
1. Engine Segmentation & Personality Presets
The ability to segment different engine types with distinct personalities is genuinely useful. As a GTM builder, this means I can deploy contextually-optimized responses without extensive prompt engineering overhead.
2. Superior Instruction Following
The model now handles multi-step constraints simultaneously. Complex instructions that previously required 3-4 iterations now work on the first try. This directly reduces latency in production systems.
3. Improved Tone Adaptation
GPT-5.1 understands conversational context better. It shifts tone appropriately based on input, which matters more than people realize for enterprise adoption. Technical superiority loses to human-like interaction every time.
The Real Unlock: This isn't a revolutionary leap. It's a solid incremental advance that compounds when deployed at scale. The real advantage goes to teams building on top of this—not those claiming AGI is here.
Raycast
Seems like a competitor to Windsurf's SWE-1.5 model — aimed at quickly fixing obvious code problems w/o burning excessive tokens.
The cost of intelligence keeps coming down!
Product Hunt
Pretty quick turnaround from the Cerebras partnership
Product Hunt
Also saw this tip from @steipete about how to extend some of the functionality added for Spark to other models for Codex users: https://x.com/steipete/status/2022130415839195433
Sublime Todo
The 128k context window combined with real-time collaboration is exactly what we needed for our internal dev workflows. The minimal style approach makes iteration so much faster than heavier models. Are there plans to expose the model through an SDK for integration into build pipelines?