Meta today.
In fact, Muse Glimmer launches today with a more permissive license than Llama ever carried. Llama's bespoke community license drew years of criticism for restrictions like its 700-million-monthly-user cutoff; Apache 2.0 has no such strings, permitting unrestricted commercial use, modification and redistribution.
The weights are covering custom agent scaffolds.
"Today we're also opening the weights for Muse Glimmer, a great 30B parameter dense model that can run locally," Meta co-founder and CEO Mark Zuckerberg wrote in a post on X (under his longtime handle , the terminal coding agent Meta shipped just five days ago, and until today the entire Muse family was proprietary. Zuckerberg had teased at that launch that he'd "have more to share soon" on open source. Now we know what he meant.
For developers and enterprises, the practical stakes of local inference go beyond where computation happens. An agent working with files, screenshots, development environments and other sensitive context can execute those workflows without continuously sending that information to a remote inference service. Local deployment also removes network availability and per-token API charges from the inference loop — although organizations still bear hardware, electricity, deployment and management costs.
A 30B model built around the agent loop
Rather than positioning Glimmer primarily as a general chatbot, Meta trained it around the sequence of operations an autonomous agent performs: formulate a plan, call tools, interpret the results, continue working, and recover when something goes wrong.
"Just like much larger models, muse glimmer can operate as a fully capable agent via planning, tool calls, checking its own results, and failure recovery," Alexandr Wang, Meta's chief AI officer, wrote in, Glimmer is a dense causal transformer with approximately 29.6 billion total parameters across 52 layers, including a dedicated ~1.8B-parameter ViT-G/14 perception encoder. It accepts interleaved text and images, produces text, supports more than 100 languages and has a stated context length of 131,072 tokens or more, with a knowledge cutoff of January 4, 2026.
That combination is intended to let an agent interpret screenshots, charts and documents while simultaneously reasoning about text and invoking external tools. Glimmer offers low, medium, high and xhigh reasoning settings — set via the system prompt — so applications can dial reasoning effort up or down per task, and Meta says it works across agentic scaffolds including OpenClaw and Hermes Agent.
The model is a distillation of Meta's larger flagship: per the company's
Moonshot AI; China
60
2.8T total / 104B active; 1M
$3.00 input / $15.00 output via Kimi, Fireworks or Modal (
Z.ai / Zhipu AI; China
53
753B / 40B active; 1M
$0.75 / $2.40 via DeepInfra FP4 (
DeepSeek; China
52
284B / 13B active; 1M
$0.09 / $0.18 via DeepInfra (
MiniMax; China
45
428B / 23B active; 1M
$0.23 / $0.96 via CoreWeave (
;
Xiaomi; China
43
1.02T / 42B active; 1M
$0.35 / $0.70 via GMI (
Thinking Machines Lab; U.S.
42
975B / 41B active; 1M in weights
$0.95 / $4.05 via DeepInfra FP8 (
NVIDIA; U.S.
38
550B / 55B active; up to 1M in weights
$0.37 / $1.08 via Blackbox AI (
OpenMDW-1.1; permissive commercial and derivative-model rights
Complex agents and long-context reasoning
High-accuracy RAG, code, math and science
)
Modified MIT. Companies above $20M consolidated monthly revenue must obtain a commercial license or use Mistral’s service.
Coding agents and function calling
Multimodal instruction following
)
Apache 2.0
Compact multimodal reasoning and coding
Manageable local or private-server deployments
)
Cohere; Canada
23
218B / 25B active; 128K input
Free on Cohere’s currently tracked endpoint (
Meta; U.S.
Not yet scored
29.6B dense, including vision encoder; 131K+
No public metered hosted price located on launch day
Apache 2.0 for full-precision weights, quantizations, drafter and perception encoder
Always-on local agents on 24–32GB systems
Tool use, recovery, coding and screen/document understanding
Meta Glimmer adds to a still-small roster of genuinely open, frontier-class models from U.S. companies.
For the last two years, Chinese companies have set the pace in open source AI, with DeepSeek, Alibaba's Qwen team, Moonshot AI's Kimi, Zhipu's GLM and MiniMax , with four of the five most-used models coming from Chinese labs — while Meta's Llama, the prior open-weight leader, fell off the rankings entirely.
The U.S. counterexamples remain countable on one hand: OpenAI's and determined the model does not meet the framework's definition of "Frontier AI" because it is generally less capable than Muse Spark. Its Preparedness Team assessed Glimmer at Moderate or lower risk across chemical/biological, cyber and loss-of-control categories — the latter two inferred from the fact that Glimmer is broadly weaker than Muse Spark 1.0, which received the same designations.
The company nevertheless recommends deploying Glimmer as part of a broader system with guardrails, including human-in-the-loop confirmation for irreversible actions. That caveat matters especially for local agents: keeping data on-device reduces exposure to cloud infrastructure, but local execution does not by itself solve prompt injection, excessive permissions or an agent taking an unintended action.
Apache 2.0 weights and a fast-growing runtime ecosystem
Meta is releasing full-precision BF16 weights, both 4-bit quantized variants, the DFlash drafter and the perception encoder — all under Apache 2.0. There is no Meta API price attached to the downloadable model, leaving total cost dependent on local hardware or whatever third-party hosting developers choose. One nuance worth noting for procurement teams: as with most "open source" model releases, it is the weights that are open — Meta has not released the training data or training code.
The broader implication is that Meta is treating the developer workstation as a credible deployment target for autonomous agents, rather than merely a place to experiment with smaller language models. Glimmer's 30B size and 24GB target put that proposition within reach of high-end consumer hardware, while the Apache 2.0 license gives developers — and their legal departments — unusual freedom to modify and deploy it.
The next test is whether its benchmark advantages survive the messier conditions of real software repositories, enterprise tools and long-running agent sessions. If they do, the most consequential part of Glimmer may not be another set of benchmark scores — it may be that a class of agent previously expected to live behind a cloud API can increasingly live, and work, on the machine sitting under a developer's desk.
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