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Z.ai Says New AI Model Costs Less to Use Than DeepSeek

Published

2 months ago

July 28, 2025

Chinese artificial intelligence startup Z.ai, formerly known as Zhipu, reportedly said its new AI model costs less to use than DeepSeek.

Z.ai’s new GLM-4.5 AI model costs 11 cents per million input tokens and 28 cents per million output tokens, CNBC reported Monday (July 28), citing its interview with Z.ai CEO Zhang Peng.

For DeepSeek R1, those costs are 14 cents and $2.19, respectively, according to the report.

Z.ai’s GLM-4.5 is open source and can operate on eight Nvidia H20 chips, per the report.

The company said in a Monday blog post that the GLM-4.5 model series features two models — GLM-4.5 and GLM-4.5-Air — that satisfy the requirements of agentic applications by unifying reasoning, coding and agentic capabilities.

They are hybrid reasoning models that offer a “thinking mode” for complex reasoning and tool use, as well as a “non-thinking mode” for instant responses, according to the post.

“As a good [large language model (LLM)], it is necessary to deal with general problem solving, generalization, [commonsense] reasoning and self-improvement,” Z.ai said in the post. “In the past five years, OpenAI’s GPT-3 learns [commonsense] knowledge, and o1 uses reinforcement learning to think before [responding], significantly improving reasoning skills in coding, data analysis and complex math. However, the resultant models are still not really general. Some of them are good at coding, some good at math, and some good at reasoning, but none of them could achieve the best performance across all the different tasks. GLM-4.5 makes efforts toward the goal of unifying all the different capabilities.”

According to the CNBC report, Z.ai is on the United States’ entity list, meaning American companies are restricted from doing business with it.

The AI startup has raised $1.5 billion from investors and is planning an initial public offering (IPO) in China, per the report.

It was reported in March that Z.ai, then known as Zhipu AI, raised more than 1 billion yuan (about $140 million) in a funding round led by Hangzhou Municipal Construction Investment Group Co. and Shangcheng Capital, both of which have ties to the Hangzhou local government.

Existing backers Alibaba Group, Tencent and others also participated in the round.

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Commanders vs. Packers props, SportsLine Machine Learning Model AI picks, bets: Jordan Love Over 223.5 yards

Published

1 hour ago

September 11, 2025

The Editors

The NFL Week 2 schedule gets underway with a Thursday Night Football matchup between NFC playoff teams from a year ago. The Washington Commanders battle the Green Bay Packers beginning at 8:15 p.m. ET from Lambeau Field. Second-year quarterback Jayden Daniels led the Commanders to a 21-6 opening-day win over the New York Giants, completing 19 of 30 passes for 233 yards and one touchdown. Jordan Love, meanwhile, helped propel the Packers to a dominating 27-13 win over the Detroit Lions in Week 1. He completed 16 of 22 passes for 188 yards and two touchdowns.

NFL prop bettors will likely target the two young quarterbacks with NFL prop picks, in addition to proven playmakers like Deebo Samuel, Romeo Doubs and Zach Ertz. Green Bay’s Jayden Reed has been dealing with a foot injury, but still managed to haul in a touchdown pass in the opener, while Austin Ekeler (shoulder) does not carry an injury designation for TNF. The Packers enter as a 3-point favorite with Green Bay at -172 on the money line, while the over/under is 49 points. Before betting any Commanders vs. Packers props for Thursday Night Football, you need to see the Commanders vs. Packers prop predictions powered by SportsLine’s Machine Learning Model AI.

Built using cutting-edge artificial intelligence and machine learning techniques by SportsLine’s Data Science team, AI Predictions and AI Ratings are generated for each player prop.

For Packers vs. Commanders NFL betting on Monday Night Football, the Machine Learning Model has evaluated the NFL player prop odds and provided Commanders vs. Packers prop picks. You can only see the Machine Learning Model player prop predictions for Washington vs. Green Bay here.

Top NFL player prop bets for Commanders vs. Packers

After analyzing the Commanders vs. Packers props and examining the dozens of NFL player prop markets, the SportsLine’s Machine Learning Model says Packers quarterback Love goes Over 223.5 passing yards (-112 at FanDuel). Love passed for 224 or more yards in eight games a year ago, despite an injury-filled season. In 15 regular-season games in 2024, he completed 63.1% of his passes for 3,389 yards and 25 touchdowns with 11 interceptions. Additionally, Washington allowed an average of 240.3 passing yards per game on the road last season.

In a 30-13 win over the Seattle Seahawks on Dec. 15, he completed 20 of 27 passes for 229 yards and two touchdowns. Love completed 21 of 28 passes for 274 yards and two scores in a 30-17 victory over the Miami Dolphins on Nov. 28. The model projects Love to pass for 259.5 yards, giving this prop bet a 4.5 rating out of 5. See more NFL props here, and new users can also target the FanDuel promo code, which offers new users $300 in bonus bets if their first $5 bet wins:

How to make NFL player prop bets for Washington vs. Green Bay

In addition, the SportsLine Machine Learning Model says another star sails past his total and has nine additional NFL props that are rated four stars or better. You need to see the Machine Learning Model analysis before making any Commanders vs. Packers prop bets for Thursday Night Football.

Which Commanders vs. Packers prop bets should you target for Thursday Night Football? Visit SportsLine now to see the top Commanders vs. Packers props, all from the SportsLine Machine Learning Model.

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Adobe Says Its AI Sales Are Coming in Strong. But Will It Lift the Stock?

Published

2 hours ago

September 11, 2025

The Editors

Adobe (ADBE) just reported record quarterly revenue driven by artificial intelligence gains. Will it revive confidence in the stock?

The creative software giant late Thursday posted adjusted earnings per share of $5.31 on revenue that jumped 11% year-over-year to a record $5.99 billion in the fiscal third quarter, above analysts’ estimates compiled by Visible Alpha, as AI revenues topped company targets.

CEO Shantanu Narayen said that with the third-quarter’s revenue driven by AI, Adobe has already surpassed its “AI-first” revenue goals for the year, leading the company to boost its outlook. The company said it now anticipates full-year adjusted earnings of $20.80 to $20.85 per share and revenue of $23.65 billion to $23.7 billion, up from adjusted earnings of $20.50 to $20.70 on revenue of $23.50 billion to $23.6 billion previously.

Shares of Adobe were recently rising in late trading. But they’ve had a tough year so far, with the stock down more than 20% for 2025 through Thursday’s close amid worries about the company’s AI progress and growing competition.

Wall Street is optimistic. The shares finished Thursday a bit below $351, and the mean price target as tracked by Visible Alpha, above $461, represents a more than 30% premium. Most of the analysts tracking the stock have “buy” ratings.

But even that target represents a degree of caution in the context of recent highs. The shares were above $600 in February 2024.

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Microsoft launches Personal Shopping Agent preview

Published

2 hours ago

September 11, 2025

The Editors

Microsoft just launched a preview of its Personal Shopping Agent. The AI tool is designed to assist retailers. According to the tech giant, its new tool can provide a personalized product-discovery experience for consumers.

The Personal Shopping Agent makes it possible for shoppers to have an interactive chat that’s powered by AI and contextual to a specific brand. Ralph Lauren used an early version of the agent to power a shopping experience within an app. Azure OpenAI powers the Ask Ralph feature.

In addition to being embedded within apps, the Personal Shopping Agent can also power shopping websites and in-store experiences. Employees can also use the tool to help customers.

Microsoft highlights key features and benefits of its Personal Shopping Agent:

Faster deployment and quick experience
Conversational product discovery
Intuitive clarifications and next-level guidance
Future-proof with agent ecosystem
Flexible and simple data ingestion

Shoppers can receive suggestions from the Personal Shopping Agent based on information shared with the tool, such as the age of the person being shopped for or topics that person enjoys. It’s also possible to interact with the tool using natural language to pin down the best items to buy.

Retailers can guide the Personal Shopping Agent to share information in line with company policies, product information, and other guidelines. The tone of the tool can also be set to match what shoppers would expect from a certain type of retailer.

Other tech giants are also working on AI shopping assistants. Amazon has integrated its AI tool, Rufus, into its website and app.

What are AI agents?

The Personal Shopping Agent is based on Copilot Studio, which is a tool for building agents and agent flows. It is a “low-code” tool, making complex tasks more accessible.

Agents are a big part of the next wave of AI. They can handle complex tasks and work alongside language models and connected data to accomplish goals. Agents can be used in a variety of workflows, including sales, customer support, and managing finances.

NVIDIA CEO Jensen Huang breaks down AI into four waves, the third of which is “Agentic AI.”

AI’s first phase is about perception, such as recognizing speech and identifying images. The next wave is generative and focuses on creating text and images. Tools like ChatGPT, DALL-E, and Gemini are examples of generative AI.

Agentic AI is still in its early stages. It includes reasoning models performing tasks independently. Microsoft foresees Windows will be an agentic OS in less than a decade.

Think of agents as the new apps for an AI-powered world.
Jared Spataro, Microsoft Chief Marketing Officer for AI

The fourth wave is physical AI, which places AI into real-world devices such as robots. I believe we’re still far from a walking robot helping you shop, but NVIDIA recently built a “brain” that can power humanoid robots.

“Think of agents as the new apps for an AI-powered world,” said Jared Spataro, Microsoft’s Chief Marketing Officer for AI at Work. “We’re rapidly adding new capabilities to tackle individuals’ biggest pain points at work and drive real business results.”

Microsoft has ready-made agents available through Microsoft 365 and Dynamics 365, but organizations can also create custom agents through Copilot Studio.

Over time, agents should become more capable as they are given more autonomy and access to more information. Naveen Seshadri, Ralph Lauren’s Chief Digital Officer, alluded to this when discussing the future of the Ralph Lauren app.

“This is a styling tool right now,” said Seshadri, “but the ultimate aim here is to make it a more immersive, personalized, and friction-free shopping experience.”

Microsoft’s Personal Shopping Agent is in preview. Organizations can sign up using a form on Microsoft’s website.

To deploy Personal Shopping Agent, you’ll need to have Microsoft Copilot Studio, a Power Platform environment, and set up permissions. A Microsoft document includes all the steps needed to start using the Personal Shopping Agent.