Big knowledge cutoff jump from Sep 2024 to Aug 2025. How'd they pull that off for a small point release, which presumably hasn't done a fresh pre-training over the web?

Did they figure out how to do more incremental knowledge updates somehow? If yes that'd be a huge change to these releases going forward. I'd appreciate the freshness that comes with that (without having to rely on web search as a RAG tool, which isn't as deeply intelligent, as is game-able by SEO).

With Gemini 3, my only disappointment was 0 change in knowledge cutoff relative to 2.5's (Jan 2025).

Wish they would include or leak more info about what this is, exactly. 5.1 was just released, yet they are claiming big improvements (on benchmarks, obviously). Did they purposely not release the best they had to keep some cards to play in case of Gemini 3 success or is this a tweak to use more time/tokens to get better output, or what?

I don’t know if they used the new ChatGPT to translate this page but I was served the French version and it is NOT good. There are placeholders for quotes like <quote> and the prose is incredibly repetitive. You’d figure that OpenAI of all people would be able to translate something to one of the worlds most spoken language.

Why doesn't OpenAI include comparisons to other models anymore?

There’s really no point in looking at benchmarks anymore as real world usage of these models varies between task and prompting strategies. Use your internal benchmarks to evaluate and ignore everything else. It is curious to me how they don’t provide a side x side comparison of other models benchmarks for this release

I've been looking really hard at combining Roslyn (.NET compiler platform SDK) with one of these high end tool calling models. The ability to have the LLM create custom analyzers and then verify them with a human in the loop can provide stable, compile-time guarantees of business rules that accumulate without paying for context tokens.

I feel like there is a small chance I could actually make this work in some areas of the business now. 400k is a really big context window. The last time I made any serious attempt I only had 32k tokens to work with. I still don't think these things can build the whole product for you, but if you have a structured configuration abstraction in an existing product, I think there is definitely uplift possible.

This is a whole bunch of patting themselves on the back.

Let me know when Gemini 3 Pro and Opus 4.5 are compared against it.

I am really curious about speed/latency. For my use case there is a big difference in UX if the model is faster. Wish this was included in some benchmarks.

I will run 80 3D model generations benchmark tomorrow and update this comment with the results about cost/speed/quality.

Trying it now in Vscode Insiders with Github Copilot (codex crashes with HTTP 400 server errors), and it eventually started using sed and grep in shells instead of using the better tools it has access to. I guess this is not an issue to perform well in benchmarks.

This feels like "could've been an email" type of thing, a very incremental update that just adds one more version. I bet there is literally no one in the world who wanted *one more version of GPT* in the list of available models from OpenAI.

"All models" section on https://platform.openai.com/docs/models is quite ridiculous.