I've been driving GPT5.5 on low reasoning for the last week+ and it's very good, very efficient. Haven't been tempted to reach for Opus at all. And it's more succinct than Kimi too. Huge leap forward for @OpenAI 👌
@dyett@OpenAI Thanks for everything you have done for OpenAI and, at a personal level, thanks for bringing me in to this amazing org. Your leadership will be missed.
For clarity, we are running a small test for ~100% of Codex users where we:
- make our best models available to all users
- make Codex available to all plans, free and paid
Claude Code users aren't affected :)
Great news to start off the new financial year. @Razorpay is now embedded natively inside @OpenAI’s Codex, an industry-first for any Indian payments company.
A developer prompts "build me a fitness app with a ₹499 one-time payment." Codex builds it. Razorpay handles the payments. No separate integration, no dashboard-hopping.
If AI makes creation effortless, monetisation should be just as simple.
India has 6 million developers & weekly Indian users of Codex 4x'd in two weeks this February. We’re building for this shift where more people can create, launch, and earn without friction.
We have always enabled businesses so that their payments are smoother, easier, simpler & faster and Codex just got added to that already illustrious list.
Do people like this? We don't do this for codex because it exists to help you and it's important that you remain the owner and accountable for your work without AI taking credit. At the same time it does mean that you can't trace how popular codex is among repos.
Chatgpt Vs Claude in Excel
TLDR: Gave ChatGPT and Claude the same credit risk modeling task in Excel. ChatGPT followed proper methodology, did variable clustering, train/test split, Excel native model, AUC 0.627 on test data. Claude skipped clustering, inflated IV with sparse categories, ran the model in JavaScript not Excel, reported 0.706 AUC on training data with no split. Chagpt 5.4 nailed a real world Banking and Finance modelling case study.
I gave both ChatGPT and Claude the same banking credit risk dataset with the same prompt: bin the variables, calculate Information Value, do variable clustering, select features, build a model in Excel, generate ROC. Basically an end to end scorecard development workflow inside Excel.
ChatGPT followed the brief properly. Quantile-based WoE binning, IV ranking across all 33 variables, correlation-based variable clustering at 0.75 threshold to remove redundant features, picked 6 representative variables, built a 2-variable Excel-native decision tree with proper train/test split via ID mod 10, scored leaf-level bad rates, ROC on held-out test data. AUC: 0.627.
Claude looked more impressive on the surface. 28 sheets, individual binning for every variable, logistic regression with 27 features, gradient descent at 500 epochs, full coefficient table with importance bars, confusion matrix, precision/recall/F1. AUC: 0.7066.
Sounds like it won right?
So, The logistic regression was computed in JavaScript via execute_office_js and results were pasted as static values. That's not Excel-native, against the task. The 0.706 AUC? On the full training set, no train/test split. That number is meaningless. Variable clustering? Skipped entirely even though the prompt explicitly asked for it. It "selected" 31 out of 33 features which is barely feature selection. The entire feature ranking was built on a methodological error.
I've done this exact dataset myself with deeper feature engineering and ensemble technique hit 0.70 AUC. The fact that ChatGPT got to 0.627 with less feature enigineering and naive modelling is genuinely impressive. Binning and IV based feature selection is one of the most important techniques in credit risk modeling and it nailed the workflow.
One thing claude did well is the workbook is visually impressive.
One area ChatGPT can improve, the visuals and formatting could be sharper, the workbook is functional but not polished.
Helping AI reach more people requires deep collaboration across the ecosystem.
Today we’re announcing new investment, with support from @SoftBank, @NVIDIA, and @Amazon, to scale the infrastructure needed to bring AI to everyone.
https://t.co/xW0ItgMTLe
It was a very good meeting indeed. India is making immense strides in the world of AI. We invite the world to invest in our talented youth and add vigour to this sector.
@OpenAI
I fail to understand the issue with this menu? It showcases local ingredients (banana blossom ftw), coupled with modern cooking styles. Indian food for foreign visitors does not always have to be the 5000 calorie daal Bukhara!
Charred pineapple yogurt foam and banana blossom skewers?? Open secret in Delhi's diplomatic circles that visiting dignitaries now routinely return from such strange meals at official banquets and order Indian food from room service.
I didn't think we'd see this from OpenAI.
Such a big shift from what they were doing... and such a drastic contrast to the direction anthropic has taken with regards to subscription lockdown.
@rajeshsawhney You could say the same about the US universities as well but US created a better risk reward ratio technical talent. Till mid 2010s we failed to do so.