Meta released Muse Spark 1.3 on September 2, and AI chief Alexandr Wang said it is competitive with Claude Fable 5.1 and beats GPT-5.6 Sol at coding from Meta Superintelligence Labs, the unit built around Wang after Meta’s $14.3 billion Scale AI investment. Announced on Sept. 2, the model is intended to work across multiple tasks in a single conversation, use tools to gather information and adjust its approach when it encounters missing information or other obstacles. Meta has also introduced changes aimed at making the model more efficient during coding work and improving how it handles user interaction during longer tasks. Muse Spark 1.3 is rolling out through Muse Code and the Meta Model API at the same price as version 1.2, with a maximum reasoning mode to follow after further safety testing. The release is part of Meta’s broader work on personal AI agents that can carry out tasks on behalf of users.
What Can Muse Spark 1.3 Do?
Muse Spark 1.3 is designed for tasks that require several steps rather than a single response. In its research announcement, Meta said the model can sustain longer workflows by working with users in a single conversation while using tools to build context from different sources. The company demonstrated the model handling tasks that require it to gather information, work with files and produce a final deliverable. Examples include creating a flow-simulation report using engineering data and a CAD file, working with an audio track and reference files, preparing a presentation and summarising information from a spreadsheet. These demonstrations are intended to show how the model can move through a sequence of actions rather than simply generate text from one prompt.
In this context, agentic work refers to tasks in which an AI system can plan and carry out multiple actions toward a goal, often using tools along the way. Muse Spark 1.3 is designed to collaborate with the user during those workflows. Meta said it can ask clarifying questions when an instruction is ambiguous, request assistance when it encounters a problem and seek confirmation before taking consequential actions. The model also includes changes aimed at coding workflows. Meta said it trained Muse Spark 1.3 on more long-horizon coding tasks and reduced its verbosity compared with Muse Spark 1.2. In Meta’s internal comparisons, 1.3 used about 20% fewer tool calls and 25% fewer tokens than 1.2 on coding tasks, scoring 75.4% on DeepSWE 1.1 within a 1 million token context window. Meta also said the model has improved at recognising its own limitations. Rather than continuing with an unsupported result when it encounters an obstacle, the company said the model is better at identifying when it needs additional information or assistance. Safety is another part of the update. Meta said 1.3 better resists adversarial inputs and prompt injections and handles hard-to-reverse actions more carefully. Notably, Wang told Axios that unlike other labs, Meta did not pause any work for safety, a contrast with OpenAI and Anthropic this week.
Meta’s muse spark 1.3 surpassed fable 5 and GPT 5.6 sol
by u/Snoo26837 in singularity
Meta has not decided whether to release 1.3’s weights, though it still plans to publish 1.2’s. A closed frontier release would mark a shift for a company known for open models.
Where Is Muse Spark 1.3 Available And Who Is It For?
Muse Spark 1.3 is rolling out through Muse Code and Meta’s API. The company said the available reasoning modes are being released with the model, while its maximum reasoning mode will follow after additional safety testing. Axios reported that the new model is priced at the same level as its predecessor.
The model is primarily aimed at developers and technical users. Its coding capabilities can be used for writing, reviewing and debugging software, while the agentic features are intended for workflows that involve multiple steps, tools, files or sources of information. That can include tasks such as researching information and turning it into a structured report, working across a software repository, analysing documents or building applications that require an AI system to perform several actions in sequence. Developers can also access the model through Meta’s API when incorporating its capabilities into their own applications.
Users who mainly want short answers, basic writing assistance or straightforward questions, some of Muse Spark 1.3’s longer-workflow features may be less relevant. The model is more closely suited to tasks where planning, tool use and continued interaction are part of the job. Meta’s release also fits into its broader work on personal AI agents. Wang told Axios the update paves the way for personal agents that can, in Mark Zuckerberg’s framing, work around the clock on a user’s behalf.
i really hate to say it, but…
— Alexandr Wang (@alexandr_wang) September 2, 2026
gemini who? 🏎️💨 https://t.co/Umv4CVqgre
Wang also commented on the model’s benchmark results on X, posting a comparison with Google’s Gemini models and writing, “i really hate to say it, but… gemini who?” The post reflects Wang’s own reaction to the results rather than an independent assessment of the model’s overall performance.
The release comes as several major AI developers are working on models designed for coding, tool use and longer-running tasks. For developers, the practical question will be how Muse Spark 1.3 performs across their own workflows rather than how it ranks on any single benchmark. The benchmark figures are Meta’s own and, as several outlets noted, cannot be independently verified.
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