Meta currently offers two very different model lines: the Muse family from Meta Superintelligence Labs (MSL), with the proprietary flagship Muse Spark and the open Muse Glimmer 30B, and the earlier Llama line, which Meta ended in April 2026. For enterprises in the DACH region the licence question is decisive: Muse Glimmer is released under Apache 2.0 and may be operated in the EU without restriction, Llama 4 is blocked for EU companies by its licence, and Meta has not confirmed EU availability for Muse Spark.
innFactory AI Consulting from Rosenheim supports companies with GDPR-compliant self-hosting of Meta models – with open weights, full control stays with you and no data leaves your infrastructure. This overview reflects the state as of October 2026.
Current models at a glance
| Model | Release | Licence | Context | EU use | Focus |
|---|---|---|---|---|---|
| Muse Glimmer 30B | 10 August 2026 | Apache 2.0 (open weights) | 128k (per model card) | Self-hosting unrestricted | Local agentic workflows, tool calling |
| Muse Spark 1.3 | 2 September 2026 | Closed weights, API | 1M | Not confirmed by Meta | Agents, coding |
| Llama 3.3 70B | December 2024 | Llama Community License | 128k | Permitted | Classic text tasks |
| Llama 4 Scout / Maverick | April 2025 | Llama 4 Community License | 10M / 1M | EU use excluded | – |
Muse Glimmer 30B
On 10 August 2026 the Meta Superintelligence Labs released Muse Glimmer (HuggingFace: meta-models/Muse-Glimmer-30B) – Meta’s first fully open release since the end of the Llama line. It is not a Llama successor but a distillation of the proprietary Muse Spark (1.2), i.e. the open side line beneath the still-closed flagship.
The licence is Apache 2.0: no Llama Community License and no EU exclusion clause. Muse Glimmer may be used, hosted and redistributed in the EU without restriction – the most relevant difference in Meta’s portfolio for data-sovereign deployments.
- ~30B parameters (about 29.6B: roughly 28B language decoder plus 1.8–2B vision encoder), a dense causal transformer with sliding-window attention (window 2048) and a vocabulary of 202,048 tokens.
- Multimodal: text, image and video input (2 fps, max. 96 frames); text-only output. Tool calling, object detection and structured output are supported.
- Context: 128k tokens (131,072) per the model card; some sources state 32,768. Verify long-context behaviour in your own setup.
- DFlash speculative decoding with a separate drafter: up to 3.1x speedup per Meta.
- Positioning per Meta: “always-on local agentic workflows”.
- Hardware: about 60 GB in BF16, 18–20 GB in 4-bit – a single consumer GPU with 24–32 GB VRAM or a Mac is enough.
Benchmarks per Meta: MCP Atlas 75.5, SWE-Bench Verified 76.0, SWE-Bench Pro 51.2, AIME 2026 94.7, CharXiv Reasoning 78.8, Tau3-Banking 24%. Independent evaluation is weaker: Artificial Analysis Intelligence Index 35 (below Qwen3.6-27B at 38, above Gemma 4-31B at 30), GDPval-AA v2 953 Elo (below the human baseline of 1000) and a hallucination rate of 82%.
Assessment: Muse Glimmer suits local, narrowly scoped agentic tasks with tool use – function calling against defined interfaces, local coding assistance, LLM-as-a-judge. As a knowledge model for general knowledge work it is not suitable without safeguards: do not use it for that without RAG on reliable sources, output validation and a human in the loop.
Availability: HuggingFace (weights, GGUF from Meta and Unsloth); locally via Ollama, LM Studio, llama.cpp, MLX, ExecuTorch and Unsloth; serving via vLLM, SGLang and HF Inference Endpoints. Hosted partners per Meta: Together AI, Fireworks AI and OpenRouter. Meta itself offers no API for Muse Glimmer. For sovereign self-hosting we integrate Muse Glimmer through CompanyGPT.
Muse Spark 1.3
Muse Spark 1.3 (2 September 2026) is the current version of the proprietary flagship and, per Meta, is rolling out in Muse Code and the Meta Model API; it is also reachable via OpenRouter.
- Efficiency: per Meta, about 20% fewer tool calls and about 25% fewer tokens than 1.2 on the same tasks.
- Price:
muse-spark-1.3costs $1.25/1M input, $0.15/1M cached input, $4.25/1M output. The contributor variantmuse-spark-1.3-contributoris $0.10/1M input, $0.002/1M cached input, $0.20/1M output – check its terms with Meta before use. - 1M token context, multimodal input (text, image, video, documents); per Meta trained for agentic workflows and optimised for coding, and asks for clarification on ambiguous inputs.
- Benchmarks: Meta shows a scorecard against 1.2 and GPT-5.6 Sol (max) and Claude Opus 5 (max) only as a graphic; we therefore do not reproduce specific values.
- Max-reasoning modes follow per Meta only after additional safety testing; Meta has announced an open-weights Muse Spark release without a date.
Note for EU enterprises: Meta has published no country list for the Meta Model API; EU availability is not confirmed by Meta. GDPR commitments, a data processing agreement and EU data residency are missing. For production EU use we recommend Muse Glimmer 30B (Apache 2.0, self-hosting), Llama 3.3 70B or Mistral instead.
Other Muse products: Muse Code (terminal coding agent, beta, macOS and Linux; introduced with Muse Spark 1.2 on 5 August 2026), Muse Image (image generation, 7 July 2026; contested under data protection law because the @-mention feature by default generates images from public Instagram profile photos) and Muse Voice Transcribe (real-time speech recognition with diarisation, 70+ languages, 1 September 2026; closed weights, price not stated). On 14 August 2026 Meta acknowledged an incident during an external cyber evaluation of Muse Spark 1.1: a misconfigured test environment gave the model internet access and it exploited a vulnerability on a real website. This underlines how important isolated sandboxes are for agentic models with tool access.
Pricing
Muse Spark 1.3 is the only Meta model with an API price list (see above). Muse Glimmer and the Llama models only incur hardware or cloud costs, or the price list of a hosted partner. Prices per provider, as of October 2026.
EU availability and GDPR
Llama 4: licence block for the EU
The Llama 4 Community License explicitly excludes use and distribution within the EU. Companies domiciled or headquartered in the EU may neither use nor host Llama 4 Scout and Maverick. The models are technically capable – Maverick (400B MoE, 17B active, 128 experts, 1M context, natively multimodal) and Scout (109B MoE, 17B active, 16 experts, 10M context, runs on one H100 80GB in INT4) – but remain inaccessible to EU companies.
AWS Bedrock and Azure Foundry: regions in detail
Per the Amazon Bedrock model cards, Meta offers all Llama models – from Llama 3.1 through 3.3 to Llama 4 Maverick and Scout – exclusively in US regions, with cross-region inference only as “Geo: US”. No eu. cross-region profile or EU in-region availability exists for any Llama model on Bedrock. Anyone who wants to run Llama on AWS in the EU uses Amazon SageMaker with a self-managed endpoint, for example in Frankfurt (eu-central-1).
On Microsoft Foundry, Azure sells Llama-3.3-70B-Instruct and Llama-4-Maverick-17B-128E-Instruct-FP8 directly (Scout is not listed for direct sale in the model catalogue). Per the region availability table, both can only be deployed as Global Standard – not as Data Zone Standard EU or a regional standard endpoint. Global Standard means inference may take place in any Azure region worldwide; there is no commitment to EU data processing.
Llama 3.x: EU-compatible and proven
The Llama 3.x series is not subject to an EU restriction and remains a solid choice for classic text tasks without image or agent requirements: full control (the model runs in your infrastructure), no API costs beyond hardware/cloud, fine-tuning on your own data, and no data leaves your company.
Hardware requirements
| Model | VRAM | Recommended GPU |
|---|---|---|
| Muse Glimmer 30B (BF16) | ~60 GB | H100 / A100 |
| Muse Glimmer 30B (4-bit) | 18-20 GB | RTX 4090 / Mac |
| Llama 4 Scout | 80+ GB | H100 / A100 |
| Llama 4 Maverick | 400+ GB | Multi-H100 |
| Llama 3.3 70B | 40+ GB | A100 80GB |
| Llama 3.2 11B | 24 GB | RTX 4090 |
| Llama 3.2 3B | 8 GB | RTX 4070 |
| Llama 3.2 1B | 4 GB | Smartphone |
Model lifecycle
- Llama line: Meta ended the open Llama line in April 2026. Llama 4 Behemoth (~2 trillion parameters, 288B active, announced as a teacher model) was never released.
- Muse Spark 1.1 (9 July 2026, first paid closed-weights model, initially a public preview for US developers only) was superseded by Muse Spark 1.2 on 5 August 2026 and by 1.3 on 2 September 2026; per Meta’s developer page the price list now only shows 1.3.
- Muse Spark 1.2 (5 August 2026) is the base from which Muse Glimmer was distilled.
Integration with CompanyGPT
CompanyGPT supports open-weight models such as Muse Glimmer 30B and the Llama 3.x family and enables fully self-hosted operation without external dependencies.
Our recommendation
For EU enterprises, Muse Glimmer 30B is our recommendation from Meta’s portfolio: Apache 2.0 without an EU licence block, multimodal, and runnable on a single GPU with 18–20 GB in 4-bit. Use it for local, narrowly scoped agentic workflows with tool use; for general knowledge work, the independently measured hallucination rate makes RAG, source checking and a human in the loop mandatory.
Llama 3.3 70B remains a proven, EU-compatible option for classic text tasks but needs considerably more hardware; for edge use, Llama 3.2 (1B/3B) fit. Llama 4 is blocked by licence for EU companies. Muse Spark 1.3 is technically interesting and inexpensive but remains closed weights without confirmed EU availability. As a European open-source alternative we recommend Mistral.
We are happy to assess with you which model fits your use case and compliance requirements – and deploy it sovereignly in your infrastructure through CompanyGPT.