innFactory AI Consulting based in Rosenheim, Germany supports enterprises across the DACH region with GDPR-compliant self-hosting of Meta Llama. With open weights you have full control – no data leaves your infrastructure.
Muse Spark 1.1: Meta’s First Paid Model (July 9, 2026)
On July 9, 2026, Meta Superintelligence Labs (MSL), led by Chief AI Officer Alexandr Wang, released Muse Spark 1.1 – Meta’s first paid closed-weights model and the definitive break with the Llama family’s open-weight tradition. Mark Zuckerberg positions it as “a strong agentic and coding model at a very low price.”
The key facts:
- Agentic multimodal model for tool use, computer use, coding, and multimodal understanding; can launch parallel subagents.
- 1 million token context window, with the model managing its own context (retaining, retrieving, compressing) – with no long-context surcharge.
- Aggressive pricing: $1.25/1M input tokens, $4.25/1M output tokens, $0.15 for cached input – well below OpenAI’s and Anthropic’s frontier pricing.
- Benchmarks: Strong on tool use (MCP Atlas 88.1 – ahead of Claude Opus 4.8 and GPT-5.5 per Meta’s own charts), but behind the frontier models on pure coding (SWE-Bench Pro 61.5%, Terminal-Bench 2.1 80.0).
- Availability: Via the new, OpenAI-compatible Meta Model API – but only as a public preview with a waitlist and initially for US developers only. For consumers, Muse Spark 1.1 runs as the “Thinking” mode in the Meta AI app and on meta.ai.
- Not disclosed: Parameter count, architecture details, and knowledge cutoff.
Strategically, Muse Spark is set to replace the Llama models across WhatsApp, Instagram, Facebook, Messenger, and Meta’s AI glasses. On July 7, 2026, MSL had already introduced Muse Image, its first image generation model (text-to-image, image editing, multi-photo compositing) – controversial from a privacy perspective, because the @-mention feature generates images based on public Instagram profile photos by default (opt-out instead of opt-in).
Note for EU enterprises: The Meta Model API is not available in the EU (preview restricted to US developers). Meta has communicated no GDPR commitments, no data processing agreement, and no EU data residency; there is no EU rollout timeline. For production EU applications, we continue to recommend Llama 3.3 70B (self-hosting) or Mistral as a European alternative.
Important Notice: EU Licensing Restriction for Llama 4
The Llama 4 Community License explicitly prohibits use and distribution within the EU. Companies domiciled or with their main place of business in the EU may not use or host Llama 4 Scout or Maverick. For EU enterprises, we recommend Llama 3.3 70B or alternatively Mistral as a European open-source alternative.
Llama 4: Technical Excellence – Without EU Access
Llama 4 Maverick (400B MoE)
- 128 experts, 17B active parameters per token
- 1M token context window
- Natively multimodal (text, image, video)
- Outperforms GPT-4 on reasoning and coding benchmarks
Llama 4 Scout (109B MoE)
- 16 experts, 17B active parameters
- 10M token context window – industry-unique
- Runs on a single H100 80GB (INT4)
- Ideal for massive document analysis and codebase parsing
Llama 4 Behemoth (announced)
- ~2 trillion parameters, 288B active
- Announced but never released. Superseded by Muse Spark.
- Positioned as a “teacher model” for other Llama models
Llama 3.x: Recommended for EU Enterprises
The Llama 3.x series is not subject to EU restrictions and remains the recommended choice for European enterprises:
Key Strengths
- Full Control: Model runs in your infrastructure
- No API Costs: Only hardware/cloud costs
- Customizable: Fine-tuning on your own data possible
- GDPR-Friendly: No data leaves your company
Hardware Requirements
| Model | VRAM | Recommended GPU |
|---|---|---|
| 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 |
Integration with CompanyGPT
CompanyGPT supports Llama 3.x models and enables completely self-hosted operation without external dependencies.
Our Recommendation
For EU enterprises, Llama 3.3 70B is the best choice from the Llama family – proven, EU-compatible, and with a strong performance profile. For edge applications, the compact Llama 3.2 (1B/3B) models are ideal. If you’re looking for a powerful European open-source alternative, we recommend Mistral as a Llama 4 replacement for the DACH region.