The marquee feature is obviously the 1M context window, compared to the ~200k other models support with maybe an extra cost for generations beyond >200k tokens. Per the pricing page, there is no additional cost for tokens beyond 200k: https://openai.com/api/pricing/ Also per pricing, GPT-5.4 ($2.50/M input, $15/M output) is much cheaper than Opus 4.6 ($5/M input, $25/M output) and Opus has a penalty for its beta >200…
Yeah, long context vs compaction is always an interesting tradeoff. More information isn't always better for LLMs, as each token adds distraction, cost, and latency. There's no single optimum for all use cases. For Codex, we're making 1M context experimentally available, but we're not making it the default experience for everyone, as from our testing we think that shorter context plus compaction works best for most p…
Re: GPT-5.4
#421It's funny that the context window size is such a thing still. Like the whole LLM 'thing' is compression. Why can't we figure out some equally brilliant way of handling context besides just storing text somewhere and feeding it to the llm? RAG is the best attempt so far. We need something like a dynamic in flight llm/data structure being generated from the context that the agent can query as it goes.