The efficiency gains target costs in long-running agentic workflows
When we go from GPT-5.0 -> GPT-5.1 -> ... -> GPT-5.5, the number incrementing goes with improvements in capabilities and token efficiency (which translates to speed gains). With GPT-5.5 our best model yet.
A simple strategy that we would like to continue.
OpenAI is famous for leaks but man to just go out there and tell people we will train more models
Huge alpha in following this guy
When we go from GPT-5.0 -> GPT-5.1 -> ... -> GPT-5.5, the number incrementing goes with improvements in capabilities and token efficiency (which translates to speed gains). With GPT-5.5 our best model yet.
A simple strategy that we would like to continue.
It’s reasonable to expect that the next iteration will be better. It would be surprising if GPT-5.6 wasnt an improvement over GPT-5.5.
But the more interesting part is token efficiency. As models move into more complex, longer-running, agentic workflows, every wasted token becomes latency, cost, and friction. Obv.
GPT-5.5 seems to be a real step here: not just more capable, but more efficient in how it reasons and executes. Kudos. High hopes for 5.6 being even more efficient.
When we go from GPT-5.0 -> GPT-5.1 -> ... -> GPT-5.5, the number incrementing goes with improvements in capabilities and token efficiency (which translates to speed gains). With GPT-5.5 our best model yet.
A simple strategy that we would like to continue.
The efficiency gains target costs in long-running agentic workflows
When we go from GPT-5.0 -> GPT-5.1 -> ... -> GPT-5.5, the number incrementing goes with improvements in capabilities and token efficiency (which translates to speed gains). With GPT-5.5 our best model yet.
A simple strategy that we would like to continue.
Many users praise GPT-5.5 as a powerhouse that works well and express excitement for future iterations like GPT-5.6 plus OpenAI's roadmap transparency, while others dismiss efficiency gains because higher prices make usage more expensive.
