Muse Ember
Muse Ember is Meta's smallest and most efficient model, released on November 9, 2026 on the Meta Model API and in Muse Code (model ID muse-ember-1.0), for high-volume agent work such as tool calls, coding subtasks, routing and extraction. It takes text, image, video, audio and PDF input and returns text.
Released
November 9, 2026
Type
multimodal
License
proprietary
Context
1,048,576 tokens
Meta lists a 1,048,576-token context window and 524,288 maximum output tokens.
Pricing
Input
$0.10 / 1M tokens
Output
$0.50 / 1M tokens
List price on the developer's own pages, checked . Up to 200K input tokens; cached input $0.004. Requests over 200K input tokens are billed in full at $0.30 input, $0.012 cached input and $1.50 output. Standard mode only.
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 Ember (opens in a new tab)
- Meta AI Developers blog: Start building with Muse Ember (opens in a new tab)
- Meta Model API docs: Muse Ember (opens in a new tab)
- Meta Model API docs: Pricing and rate limits (opens in a new tab)
- Meta AI Developers: Muse Ember (opens in a new tab)
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Links
In the news: Muse Ember and Meta
Digital Trends
Meta launches Muse Ember, a cheaper AI model built for repetitive tasks
SiliconANGLE AI
Meta introduces Muse Ember to power cost-efficient AI agents
The Decoder
Meta launches Muse Ember, a small AI model built for the repetitive work agents actually do
MarkTechPost
Google Open-Sources TorchTPU, a Native PyTorch Backend for Cloud TPUs
Google has open-sourced TorchTPU, a native PyTorch backend for Cloud TPUs, co-developed with the PyTorch team at Meta. Announced at PyTorch Conference 2026 on October 20, 2026, TorchTPU enables existing PyTorch code to target a TPU through a standard torch.device("tpu"). TPUs are now recognized as a native PyTorch device alongside GPUs and CPUs. The Google team recommends TorchTPU for all new workloads, stating it replaces PyTorch/XLA. TorchTPU is a software stack comprising four open repositories: TorchTPU, vLLM-TorchTPU, SGLang-TorchTPU, and TorchTitan. Available as v0.2.0 on PyPI, it requires Python 3.11+ and is licensed under Apache 2.0. TorchTPU runs on Google Cloud TPUs and has been tested on generations from v5e, scaling up to 8,192 Ironwood (TPU7x) chips. The serving capabilities support FP8, MXFP4, and NVFP4 checkpoints. Performance metrics for TorchTPU include eager mode for debugging and torch.compile for production throughput. The system achieved a self-reported 52% MFU training for Qwen 3 8B with TorchTitan on Ironwood. In a fine-tuning scenario, out-of-the-box eager mode took 11.49 seconds per step, compared to 2.78 seconds when fully tuned. TorchTPU functions as an open-source PyTorch backend that allows PyTorch models to run natively on TPUs by plugging into PyTorch’s PrivateUse1 interface. Unlike its predecessor, PyTorch/XLA, which recorded lazy-tensor graphs and required developers to mark compilable regions, TorchTPU handles tensors as ordinary PyTorch tensors without subclasses or wrappers. It supports one process per device, preserves tensor views and in-place mutations, and handles divergent MPMD execution, whereas PyTorch/XLA only supported pure SPMD code. TorchTPU offers three eager modes: Strict (default), which dispatches each operator asynchronously with standard PyTorch semantics; Debug, which runs synchronously with tensor bounds checking; and DeferAndFuse, which fuses runs of eager operators to reduce HBM traffic. The TorchTPU paper indicates that DeferAndFuse can be 50% or more faster than Strict mode.
Search Engine Journal
What Personal AI Agents Like Meta Muse And OpenAI Dots Mean For SEO via @sejournal, @slobodanmanic
Personal AI agents like Meta's Muse and OpenAI's dots are accessing websites through connectors or cloud browsers. This integration is changing the effectiveness of robots.txt, on-page SEO, and analytics in understanding website traffic and content. The development signifies a shift in how search engines and AI tools interact with web content.