Meta Llama
Reviews, test reports and deep-dive analysis
Open-weight multimodal models with 10M token context
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llama.meta.com
Details
Pros
- Free tier available
- API available
- Streaming support
- Multimodal input
- Web search capability
- Voice mode
Cons
- No GDPR compliance confirmed
- No EU server location
Profile: Meta Llama
| Company | Meta Llama |
| Type | AI Chatbots & LLMs |
| Founded | 2023 |
| Headquarters | Menlo Park, USA |
| Server Location | US |
| GDPR Status | ⚠️ Not confirmed |
| Free Tier | Yes |
| Starting Price | Free |
| Pricing Model | FREEMIUM |
| Website | llama.meta.com |
About Meta Llama
Meta's Llama 4 family, released in April 2025, represents the most advanced open-weight AI models available. Using Meta's first Mixture-of-Experts (MoE) architecture, these models are natively multimodal (text + image + video) and match or exceed proprietary models on key benchmarks.
The Llama 4 family includes Scout (10 million token context window for long-document processing), Maverick (general-purpose multimodal model for conversation, reasoning, image analysis, and code), and Behemoth (teacher model, still in training). Open weights under Meta's license permit commercial and research use for organizations under 700M monthly active users.
Meta.ai provides a free chat interface with web search, image generation (Imagine), voice mode, mobile apps (iOS/Android), and file uploads. For developers, models are freely downloadable via Ollama, vLLM, and Hugging Face, or accessible per-token through third-party hosts (Together.ai, Groq, OpenRouter). There is no usage-based cost for self-hosting — just compute.
Scout's 10 million token context window is unmatched by any model, enabling processing of entire codebases, multi-book analysis, or days of audio processing. Maverick delivers consistent high-quality output across conversation, reasoning, and code generation, supporting 12+ languages. Coding quality is "Very Good" with strong performance across Python, JavaScript, Java, and more.
The open-weight model enables fine-tuning on proprietary data, on-premise deployment behind firewalls, and air-gapped environments for regulated industries (healthcare, finance, defense). This definitively addresses privacy concerns — data never leaves your infrastructure.
Limitations: No polished consumer product comparable to ChatGPT or Claude, no memory or plugin ecosystem, and self-hosting requires ML engineering expertise and significant compute. For most individuals, third-party hosts provide the easiest access. For organizations with the capability to self-host, Llama 4 offers unmatched flexibility, the industry's largest context window, and zero marginal cost.
