It may also have a larger usable context window, not totally sure about that.
Can you provide an example of what you mean by this? I provide very verbose prompts where I know what needs to be done and just let AI “do” the work. I’m curious how this is different?
And partly it can actually execute more at the same time without starting to make mistakes
I routinely put about 100K + of context into Sonnet 3.7 in the form of source code, and in the Extended mode, given the right prompt, it will output perhaps 20 large source files before having to make a "continue" request (for example if it's asked to convert a web app from templates to React).
I'm curious whether O1 Pro actually exceeds Sonnet 3.7 in Extended mode for coding or not. Looking forward to seeing some benchmarks.
> We evaluate 12 popular LLMs that claim to support contexts of at least 128K tokens. While they perform well in short contexts (<1K), performance degrades significantly as context length increases. At 32K, for instance, 10 models drop below 50% of their strong short-length baselines. Even GPT-4o, one of the top-performing exceptions, experiences a reduction from an almost-perfect baseline of 99.3% to 69.7%.
Have a look at the RULER benchmark for a bit more detail.