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What is the reason behind OpenAI being able to release new models very fast? Since Feb when we got Gemini 3.1, Opus 4.6, and GPT-5.3-Codex we have seen GPT-5.4 and GPT-5.5 but only Opus 4.7 and no new Gemini model. Both of these are pretty decent improvements.
Competition.
If there's a bingo card for model releases, "our [superlative] and [superlative] model yet" is surely the free space.
For a 56.7 score on the Artificial Intelligence Index, GPT 5.5 used 22m output tokens. For a score of 57, Opus 4.7 used 111m output tokens. The efficiency gap is enormous. Maybe it's the difference between GB200 NVL72 and an Amazon Tranium chip?
If there's a bingo card for model releases, "our [superlative] and [superlative] model yet" is surely the free space.
Just as a heads up, even though GPT-5.5 is releasing today, the rollout in ChatGPT and Codex will be gradual over many hours so that we can make sure service remains stable for everyone (same as our previous launches). You may not see it right away, and if you don't, try again later in the day. We usually start with Pro/Enterprise accounts and then work our way down to Plus. We know it's slightly annoying to have to…
I'm here for the pelicans and I'm not leaving until I see one!
Are there faster mini/nano versions as well?
The more interesting part of the announcement than "it's better at benchmarks": > To better utilize GPUs, Codex analyzed weeks’ worth of production traffic patterns and wrote custom heuristic algorithms to optimally partition and balance work. The effort had an outsized impact, increasing token generation speeds by over 20%. The ability for agentic LLMs to improve computational efficiency/speed is a highly impactful…
Honestly the problem with these is how empirical it is, how someone can reproduce this? I love when Labs go beyond traditional benchies like MMLU and friends but these kind of statements don't help much either - unless it's a proper controlled study!
A more empirical test would be good for everyone (i.e. on equal hardware, give each agent the goal to implement an algorithm and make it as fast as possible, then quantify relative speed improvements that pass all test cases).
I have to imagine they'll go to Gemini 3.5 if only for marketing reasons.