MiniMax M3.1 Flash and OpenAI's legacy-code push

Also: Gemini Notebook adds reviewable slides and video, and TorchNeuron makes Trainium much easier to use MiniMax rolled out M3.1 Flash, a cheaper, faster model aimed squarely at agentic work. The company says it is nearly three times faster online than its predecessor, cuts usage cost by 70%, and is now available in the MiniMax API, M Plan, and MiniMax Code. What matters is the mix: MiniMax is pitching one model for coding, computer use, enterprise workflows, and cybersecurity, with posted token pricing low enough to make heavier agent loops more practical. OpenAI published new results on coding in low-resource languages such as COBOL, Fortran, OCaml, and Visual Basic. This is not a new model launch, but it is a useful capability update for real enterprise software. OpenAI says GPT-6 Astra reached an 87.4% average pass rate on its new 20-language MultiLangBench, while GPT-6.1 Sol hit 86.6% accuracy on a COBOL migration evaluation at low reasoning effort, rising to 97.9% at higher effort. OpenAI also released the benchmark setup, which makes this more than a marketing claim. Google is upgrading Gemini Notebook with a more controllable creation flow for Slide Decks and Video Overviews. Instead of jumping straight from prompt to finished output, users can now review the outline or producer notes, change structure, and then render the result. That matters because these products become much more useful when people can steer them before generation, not just edit after. Google says the feature is rolling out globally over the coming week. PyTorch and AWS introduced TorchNeuron, a native PyTorch backend for Trainium, with public beta starting October 20. The practical change is that developers can use familiar PyTorch and Hugging Face workflows instead of adapting models through a separate Neuron-specific stack. PyTorch says this brings eager mode, torch.compile, and native distributed APIs to Trainium, while Hugging Face libraries including Transformers and Diffusers support the backend. If it works as advertised, Trainium becomes a far more realistic target for mainstream model training and inference. Thanks for listening. See you tomorrow. Sources: minimax.io · openai.com · blog.google · pytorch.org This episode includes AI-generated content.