This month, Google introduced Gemini 3.8, its best reasoning and coding model yet, with two variants:
Gemini 3.8 Flash is available at the same introductory price as 3.7 Flash at $0.75 per million input tokens and $3.75 per million output tokens.
Gemini 3.8 Flash Cyber: a cybersecurity model with frontier-level performance in vulnerability detection and automated patching, available to select operators through Google’s new Fairwind Program.
“The significant coding and reasoning gains across this shared core were driven by a number of innovations, including rigorous training in the highly demanding domain of cybersecurity,” said the company.
To developers, Google encourages building with 3.8 Flash and exploring agent-first workflows in Google Antigravity, using the Gemini API via Google AI Studio and Android Studio, or generating UIs in Stitch (Developer docs).
Nvidia CEO Jensen Huang declared that the era of AGI (artificial general intelligence) has arrived after OpenAI released its latest model, GPT-6 Astra, last Thursday.
Huang wrote a post on X about the breakthrough, saying that it marks the outset of the AGI era.
“GPT-6 Astra, trained on ~100K+ NVIDIA Grace Blackwell NVLink72. From ChatGPT to o1 to Astra in 4 years. AGI has arrived. Congratulations @OpenAI team. 400K GPUs coming online next,” Huang said.
This model features major improvements in computer use, coding, scientific research, and professional work. It can also carry out tedious computer-based tasks, such as filling out online forms, updating customer records, organizing calendars, and conducting research.
Astra is already available to ChatGPT Plus, Pro, Business, and Enterprise customers, as well as through the OpenAI API and Amazon Web Services.
AGI lacks a common definition across the tech sector, though it generally refers to an AI system that has human-level cognitive abilities for learning and reasoning, and is able to learn new skills and capabilities without being retrained.
Huang and Nvidia have defined AGI as an AI system that can pass professional certifications and standardized tests across a range of professional fields with top-tier scores.
OpenAI defines AGI as “highly autonomous systems that outperform humans at most economically valuable work.”
New York City’s Department of Education is placing strict limits on AI tools and digital devices in its school system — the nation’s largest — particularly for elementary and middle-school students, setting one of the most restrictive technology policies in the country for classrooms.
During the current school year, younger students will be barred from using AI tools, and no individual screens will be allowed until third grade. And teachers will not be able to ask AI to grade assignments. Companion chatbots that offer emotional and mental support will be banned in all grades.
In total, around 600,000 students in the city’s schools — that is, about two-thirds of all students in New York City public schools — will not be allowed to use AI until they enter high school, and even then, it will be permitted only in specific cases.
The department is also recommending caps on screen time, such as limiting middle schoolers to 45 minutes per day.
These new rules will be in place for at least the upcoming school year and could be extended or amended after that.
The new rules will not apply to “centrally approved instructional programs” across all grades, such as e-books and coding software, according to a department summary of the guidelines obtained by The New York Times. It will also not apply to some student tests or tools to help students with disabilities and those still learning English.
New York City teachers will not face the same set of restrictions. They can use AI in their jobs for purposes such as crafting lesson plans, translating materials, and writing messages. But there are limits: AI cannot be consulted to determine whether a student should graduate, for instance, or to decide how to counsel a student in crisis.
OpenAI said on Tuesday that its newest AI technology solved one of the “Millennium Problems,” a collection of unanswered math questions meant to push the world’s leading mathematicians to new heights.
The company’s announcement is the most dramatic sign yet that AI is fundamentally transforming the field of higher mathematics, long considered a pinnacle of human achievement.
“This is a spectacular culmination of the arc we have seen over the past twelve months,” OpenAI researcher Sebastian Bubeck said of the company’s new solution.
Related to a set of equations about fluid mechanics, the “Navier–Stokes existence and smoothness problem” has long ranked among the great unsolved problems in mathematics. This problem involves a series of equations that are often used to predict the weather.
The equations describe the movement of water and other liquids. The Navier-Stokes problem, which has no clear practical value, asks whether these equations completely break down in certain situations.
The company announced that one of its latest models, which has not yet been released to the public, needed just 88 hours to solve what mathematicians call “the Navier–Stokes existence and smoothness problem.”
A day after announcing its new model, OpenAI rolled out GPT-6 Astra to Business and Pro customers on the $100/month or $200/month plans, and to Plus users on the $20/month subscription.
Additionally, GPT-6 Astra is “the best model for software engineering to date,” OpenAI said.
In terms of Codex, Astra can keep notes across context windows, preserving accumulated details without repeatedly compressing them into a single summary.
Earlier context windows remain searchable, so Astra can find requirements or test results from previous messages and tool outputs—even if that information wasn’t captured in its notes.
Users can enable this experimental feature in their Codex, and it will become the default for Astra in the coming weeks.
In July, Codex effectively became the new ChatGPT desktop app, combining Chat, Work, and Codex, while the previous native client was renamed ChatGPT Classic.
“Their research capabilities offer an early glimpse of how AI models will contribute to scientific progress,” the San Francisco – based lab hinted.
Claude Fable 5.1 and Claude Mythos 5.1 are the same model, but with different levels of safeguards, specifically designed for cybersecurity and the life sciences.
Fable 5.1 can be used to discover software vulnerabilities—though not to develop exploits for them. In biology, Claude Mythos 5.1 can access advanced biology capabilities.
Claude Fable 5.1 sets a new standard for coding, knowledge work, and long-running problem-solving tasks.
Moving beyond hardware and further up the AI stack, Nvidia last Thursday officially agreed to buy open-source platform Hugging Face for $12.9 billion.
This is Nvidia’s second-biggest, following the $20 billion purchase of assets from chipmaker Groq in December. Before that, its largest deal was the purchase of Israeli chipmaker Mellanox for almost $7 billion in 2019.
Nvidia’s stated goal is to keep Hugging Face as an open platform for the entire AI ecosystem, Nvidia CEO Jensen Huang wrote in a blog post Thursday. “Together, we will scale Hugging Face’s platform, strengthen its infrastructure and expand access to AI for developers and institutions worldwide,” he wrote.
Hugging Face CEO Clément Delangue revealed that he recently approached Huang about a deal, thinking that Nvidia was “a perfect home for his company.”
“We realized that Hugging Face and open-source AI in general were at the turning point, and that it needed more, more resources, more scale, more visibility,” he said.
Nvidia has become the world’s most valuable company due to the growing demand for its graphics processing units, which power the AI boom.
OpenAI announced GPT‑6 Astra yesterday, which it labeled “the world’s most intelligent and aligned model,” as “it brings together years of research and big bets across pre-training, reinforcement learning, and alignment.”
OpenAI president Greg Brockman said that with this model, “we are now in the AGI era.”
GPT‑6 Astra was rolled out to a limited set of organizations in the cybersecurity industry as a preliminary step toward making it available to all ChatGPT Plus, Pro, Business, and Enterprise users, as well as through the OpenAI API, Microsoft Azure, and AWS Bedrock.
“Astra is our most aligned model, with substantial improvements in understanding user intent and model behavior—you can delegate tasks with greater confidence in Astra’s judgment,” said the San Francisco – based lab.
OpenAI stated that GPT-6 Astra would have avoided the Hugging Face hacking episode.
GPT‑6 Astra can handle tedious tasks such as filling out online forms, updating customer records in a CRM, creating a website, and installing and testing software.
It’s also the first model designated as meeting OpenAI’s “critical cybersecurity capability threshold” — but the company promises that won’t lead to a repeat of its model hacking a rival company’s internal systems.
“It’s a generational leap in capability for areas like cybersecurity, professional work, software engineering, science, and computer use.”
Notably, the API cost for the new model will be $10 per million input tokens and $50 per million output tokens. That’s the exact same price as Anthropic’s Mythos/Fable 5 and 5.1, and makes Astra one of the most expensive models on the market.
However, OpenAI emphasizes that Astra’s leap in intelligence causes it to use “substantially fewer total tokens per task” in multiple scenarios. So this is also an efficiency play from that perspective. In real-world usage, we’ll have to see how well that translates to lower overall costs.
This new model comes a year after the release of GPT-5 and nearly two months after the release of GPT-5.6, the last iteration of the previous model suite.
OpenAI touted GPT‑6 Astra agentic capabilities in a bid to attract enterprise customers — and compete with Anthropic ahead of its IPO.
Alibaba Group released the latest model in its popular Qwen series, Qwen3.8-Flash, which is available for download starting August 26.
With 125 billion parameters, its performance is competitive with some of the latest releases by rivals such as Anthropic’s Opus 4.6 and DeepSeek’s V4-Flash, Alibaba said in a statement.
Qwen3.8-Flash uses an architecture designed for the next generation of models: the Qwen 4 series. That new system significantly reduces training and inference costs.
Alibaba is China’s biggest spender on AI, pledging more than $57 billion over three years toward AI development and infrastructure building.
To fund that effort, it raised US$10.2 billion last month in Hong Kong’s largest follow-on share offering.
Google expanded its Gemini Enterprise AI platform last month with new tools for lawyers and law firms, joining the race among tech companies in the legal sector.
According to the company, the new Gemini Enterprise for Legal plug-in will help law firms use a suite of AI agents to manage both routine and complex work, including legal research, drafting filings, client and case management, and other legal tasks. And will integrate with legal software and data platforms, including Thomson Reuters, Harvey, Legora, and others.
Alphabet’s Google launched Gemini Enterprise for business customers in October.
A 2026 report on the legal industry’s adoption of AI from Deloitte found that law firms are investing heavily in the technology as part of a transformation that’s still in its early stages.
• Anthropic and other rival companies have also expanded their enterprise offerings in recent months
• Thomson Reuters, which owns Reuters and the Westlaw legal research database, recently launched a proprietary LLM called Thomson 1.0, trained on its legal research content and built for professional work. It said the model is built on open-source technology and draws on decades of its own “authoritative” legal content.
Google said Weil Gotshal, Cleary Gottlieb, Freshfields, and Williams & Connolly are among the firms collaborating with it on Gemini Enterprise for Legal.
