Expanding its AI portfolio, Meta Platforms has introduced Muse Spark 1.1, a new multimodal reasoning model developed by Meta Superintelligence Labs. Alongside the launch, the company has also released a public preview of the Meta Model API, allowing developers to access the new AI model.
Muse Spark 1.1 is designed for complex agentic AI tasks involving planning, reasoning, coding, computer interaction, and tool usage. The model supports a 1 million-token context window, enabling it to retain long-term memory, retrieve information from earlier conversations, and compress context while preserving important details.
According to Meta, the model can work with native tools, Model Context Protocol (MCP) servers, and custom skills without requiring additional training. It can also coordinate multiple subagents simultaneously, gathering context, creating execution plans, and assigning tasks in parallel. This architecture is expected to reduce latency and complete projects faster than the previous Muse Spark model.
Meta also highlighted improvements in coding and computer-use capabilities. Muse Spark 1.1 can navigate desktop applications, interact with unfamiliar software interfaces, decide when tasks should be automated using scripts, and handle enterprise software engineering tasks such as debugging large codebases, implementing new features, code migration, and building complete web applications. The company said its developers are already using the model internally for software development and AI research.
Benchmark results shared by Meta showed 88.1 on MCP Atlas, 54.7 on JobBench, 62.1 on Humanity’s Last Exam, 57.2 on Finance Agent v2, 80.0 on Terminal-Bench 2.1, and 53.3 on DeepSWE 1.1.
Meta also said the model underwent safety testing under its Advanced AI Scaling Framework before deployment. It reportedly met acceptable safety thresholds across chemical, biological, cybersecurity, and loss-of-control risk categories while demonstrating stronger resistance to jailbreak attempts, prompt injection attacks, and hallucinations compared to its predecessor.
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