Muse Spark 1.3
Muse Spark 1.3 is the latest version of Meta's Muse Spark model, released on September 2, 2026 and tuned for agentic workflows and coding. It takes text, image, video, audio and PDF input and returns text, with reasoning effort up to 'max'. Meta says audio input is not yet fully supported.
Released
September 2, 2026
Type
multimodal
License
proprietary
Context
1,048,576 tokens
Pricing
Input
$1.25 / 1M tokens
Output
$4.25 / 1M tokens
List price on the developer's own pages, checked . Standard mode; cached input $0.15. Same rates for muse-spark-1.3, 1.2 and 1.1. Contributor mode (muse-spark-1.3-contributor, your content may train Meta models) $0.10 input / $0.20 output / $0.002 cached. No long-context premium.
Capabilities
Sources
Every figure on this page was checked against these pages on . A figure we could not source is left out.
- Meta AI Research: Introducing Muse Spark 1.3 (opens in a new tab)
- Meta Model API docs: Models (opens in a new tab)
- Meta Model API docs: Pricing and rate limits (opens in a new tab)
- Meta AI Developers: Muse Spark 1.3 (opens in a new tab)
- Meta AI Developers blog: Build with Muse Spark (Jul 08, 2026) (opens in a new tab)
Spotted something out of date? Tell us.
Links
In the news: Muse Spark 1.3 and Meta
The Hacker News
Bring Your Own Agent: Your Employees' Assistants Are Already Logged In
The rollout of personal AI agents is encountering significant challenges, including privacy concerns and identification issues. Amazon has blocked Meta's Muse agent for failing to identify itself as a bot during browsing, and the Instinct agent has also raised privacy alarms. Despite these ongoing issues and accumulating negative experiences, the technology sector remains captivated by the capabilities and potential of these personal AI services. The article highlights that the personal AI agent rollout is proceeding with expected difficulties.
The Hacker News
Fake ChatGPT, Gemini, and Claude Ad Portals Capture Credentials and MFA Codes
Cybersecurity researchers have detailed a sophisticated, human-operated phishing platform designed to impersonate advertising portals for prominent artificial intelligence (AI) chatbots. This platform targets users seeking to manage or optimize advertising campaigns for services such as Google Gemini, Anthropic Claude, OpenAI ChatGPT, Perplexity, Meta Muse, and Manus. The fake portals are crafted to appear as legitimate tools offering features like campaign optimization, spend audits, and business account connections, with the sole objective of capturing sensitive user information. The operation was uncovered by researchers who observed the platform's intricate setup and its methods for deceiving potential victims. The phishing platform employs a multi-stage attack vector. Initially, victims are lured to fake advertising portals through various means, likely including malicious advertisements or direct phishing emails. Once on the counterfeit site, users are prompted to log in with their existing credentials for the respective AI chatbot services. The platform is engineered to capture not only usernames and passwords but also time-based one-time passwords (TOTP) generated by multi-factor authentication (MFA) applications. This capability is particularly concerning as it bypasses a critical security layer designed to prevent unauthorized access even if credentials are compromised. The attackers leverage the trust users place in these AI tools and their associated advertising ecosystems to execute their fraudulent activities. The researchers highlighted that the platform's operators actively manage and update the phishing sites, indicating a persistent and organized threat actor. The impersonated services are among the most widely used AI chatbots, suggesting a broad target base. By mimicking the legitimate interfaces and functionalities of these advertising platforms, the attackers aim to maximize the chances of success. The stolen credentials and MFA codes can then be used to gain full access to user accounts, potentially leading to financial fraud, data theft, or the misuse of AI services for malicious purposes. The existence of such platforms underscores the growing need for enhanced vigilance and security awareness among users of AI technologies and their associated business tools. This discovery by cybersecurity researchers serves as a stark reminder of the evolving threat landscape in the AI sector. As AI chatbots become increasingly integrated into business operations and marketing strategies, the platforms that support them become attractive targets for cybercriminals. The human-operated nature of this phishing platform suggests a level of sophistication beyond automated attacks, allowing for real-time adaptation and evasion of detection systems. The researchers have not yet publicly disclosed the specific names of the threat actors or the full extent of the compromised accounts, but the detailed technical analysis provides a clear picture of the operational methods. The implications for businesses relying on these AI services are significant, necessitating a review of their security protocols and employee training to mitigate the risks associated with credential harvesting and MFA bypass techniques.
MarkTechPost
The Story of Qwen: Alibaba’s AI Models From 7B to 2.4T
Alibaba Cloud's AI model family, Qwen, has undergone rapid development since its initial unveiling in April 2023, culminating in the release of open-weight versions with up to 2.4 trillion parameters. The journey began on April 7, 2023, with the demonstration of a chatbot named Tongyi Qianwen, whose name translates to 'truth from a thousand questions' and draws inspiration from the philosopher Mencius. This initial model was slated for integration into Alibaba's various business applications, including DingTalk and the Tmall Genie voice assistant, as announced by then-CEO Daniel Zhang at the Alibaba Cloud Summit in Beijing on April 11, 2023. A significant milestone was reached on August 3, 2023, when Alibaba open-sourced Qwen-7B and Qwen-7B-Chat. This release was positioned as a direct competitor to Meta's Llama 2. Qwen-7B was pretrained on an extensive dataset of over 2.2 trillion tokens and featured a 2,048-token context window. Its license permitted free commercial use for entities with fewer than 100 million monthly active users. The multimodal capabilities of the Qwen family emerged shortly thereafter, with the launch of Qwen-VL, the first vision-language branch, in late August 2023. Further public accessibility was granted on September 13, 2023, when Tongyi Qianwen was opened to the general public, indicating regulatory approval within China. The team also published a technical report detailing the Qwen models on arXiv in September 2023. The year concluded with an expansion in model scale, as Alibaba released its 72B and 1.8B parameter models for download around December 1, 2023, offering a range of sizes from laptop-compatible to frontier-scale open weights. The year 2024 marked Qwen's ascent as a preferred choice for developers, with three major generations released within eight months. The Qwen1.5 series, launched on February 5, 2024, was presented as a beta version of Qwen2. This iteration included dense models ranging from 0.5 billion to 72 billion parameters, boasting a stable 32K context window. The evolution continued with the release of Qwen2, which introduced models up to 72B parameters and expanded context windows to 128K tokens. The most significant development in 2024 was the release of the Qwen1.5-72B-Chat model, which achieved a score of 82.1 on the MT-Bench benchmark, surpassing other open-weight models like Llama-3-70B-Instruct and Mixtral-8x22B-Instruct. The Qwen team also announced the development of a 2.4 trillion parameter model, though its weights are not yet publicly available. This model demonstrated advanced reasoning capabilities, including the ability to process and analyze video content, a feature that sets it apart from many contemporary AI models. The Qwen series continues to be a significant contribution to the open-source AI landscape, providing developers with powerful and versatile tools for a wide range of applications.
MarkTechPost
Meta, OpenAI and Uber Just Taught AI Agents to Talk First. What About When to Stay Quiet?
Meta, OpenAI, and Uber have recently launched AI agents that adopt a "speak first" approach, fundamentally shifting the design challenge from responding to user queries to proactively initiating interactions. This new paradigm moves the core problem from "what to answer" to "when to interrupt, on which channel, and with what offer." These agents leverage classic machine learning and new decision models to manage these proactive communications effectively. Meta’s Muse, launched on September 8, is a personal agent designed to perform tasks like booking appointments and sending emails, even when the associated app is closed. It remembers user details, offers unprompted suggestions, and seeks approval before acting, operating within its own application and also via WhatsApp. OpenAI’s Dots, released on September 29, functions as an always-on agent that performs proactive research and monitors user applications in a read-only mode. Its purpose is to identify potential issues such as forgotten invoices or bugs in platforms like Slack, and it communicates these findings through ChatGPT, Slack, and Teams, with both text and voice capabilities planned. Uber’s driver assistant, announced with a hands-free voice version on September 24, utilizes live marketplace signals to provide advice to drivers. For instance, Uber’s product team described a scenario where a driver who had been idle for 33 minutes was proactively directed to a more advantageous zone, supported by relevant data. The guiding principles for these agents include always prioritizing the driver's interests, surfacing opportunities proactively, and measuring the effectiveness of their interventions by tracking whether drivers acted on the advice. The shift from traditional chatbots, which operate on a "pull" interface where users initiate interaction, to these proactive agents represents a significant inversion. Proactive agents operate on a "push" model, determining the timing, channel, and content of communication. The critical challenge for these agents is to avoid overwhelming users with too many interruptions, which could lead to them being muted, or conversely, to miss crucial opportunities by interrupting too late, such as when a surge pricing window has closed or an invoice deadline has passed. The choice of communication channel is also paramount, as a well-crafted message can fail if delivered through an inappropriate medium. While large language models (LLMs) are adept at generating messages, they are not the ideal tool for deciding when to send them. The core rule governing these proactive communications is that the "value must beat the interruption cost." Every proactive message sent by an AI agent is essentially a calculated bet. The agent should only send a message when its anticipated value to the user demonstrably exceeds the cost incurred by interrupting that user's current activity. This value is typically assessed across four dimensions: the stakes involved, the probability of the user taking action, the urgency of the opportunity (how quickly it expires), and whose benefit the message serves – the user's or the platform's. This value calculation informs both the decision to send a message and the choice of communication channel. High-value, time-sensitive messages are prioritized for immediate delivery, while those with modest value might be batched into a digest for later delivery, and messages with negligible value are not sent at all. Furthermore, the greater the perceived worth of a message, the more intrusive a channel it may be deemed to have earned.
Search Engine Journal
Shopify Now Enrolls Your Store In Every New AI Shopping Channel via @sejournal, @slobodanmanic
Shopify has implemented a new policy that automatically enrolls merchants' stores into every new AI-powered shopping channel that the e-commerce platform integrates. This change, effective immediately, means that products from participating Shopify stores will be shared with Meta and other unnamed emerging startups that partner with Shopify for these AI shopping integrations. The intention behind this automatic enrollment is to increase the visibility of merchant products across a wider array of emerging sales channels without requiring manual setup by the store owners. Merchants who wish to opt out of this automatic enrollment or manage their participation in specific channels will need to actively adjust their settings. The platform aims to streamline the process of product discovery and sales by leveraging artificial intelligence to connect products with potential buyers on new, AI-driven marketplaces. This initiative is part of Shopify's broader strategy to enhance merchant reach and sales opportunities in an increasingly dynamic e-commerce landscape. The specific names of the unnamed startups joining these AI shopping channels have not yet been disclosed, but the inclusion of Meta indicates a significant push into leveraging large social and tech platforms for e-commerce. Merchants are advised to review their storefront settings to ensure they are comfortable with the automatic listing of their products on all new AI shopping channels. The platform's agentic storefronts are designed to proactively manage product listings and sales across various integrated channels. This move signifies a proactive approach by Shopify to adapt to the evolving nature of online retail, where AI is playing an increasingly crucial role in customer acquisition and product recommendation. By automatically enrolling stores, Shopify is essentially betting on the potential of these new AI channels to drive sales for its merchants, thereby increasing the value proposition of its platform. The company's communication suggests that leaving the setting enabled will allow merchants to benefit from whatever new opportunities Shopify integrates in the future, implying a continuous expansion of these AI-driven sales avenues. The exact mechanics of how products are selected and presented on these AI channels are not detailed, but the overarching goal is to create a more seamless and expansive marketplace for Shopify's vast network of online businesses. This development positions Shopify as a forward-thinking platform that is actively exploring and integrating cutting-edge technologies to support its merchant base.