Llama
FreeMeta's open-source large language model family. Download and run powerful LLMs for free with commercial-friendly licensing for research and production use.
What does this tool do?
Llama is Meta's open-source large language model family designed for commercial deployment. The current flagship offerings include Llama 4 (with Maverick and Scout variants featuring native multimodality and 10M-token context windows) and Llama 3 series (3.1, 3.2, 3.3) available in multiple sizes from 1B to 405B parameters. The models are optimized for various deployment scenarios—from edge devices to enterprise infrastructure—with pricing estimates around $0.19-$0.49 per million tokens. Unlike closed-source alternatives, Llama provides full model weights under a commercial-friendly license, allowing organizations to fine-tune, distill, and run models entirely on-premise without vendor lock-in.
AI analysis from Feb 23, 2026
Key Features
- Native multimodality in Llama 4 with early fusion pre-training for integrated image and text understanding
- 10M-token context window enabling long-document analysis and multi-document reasoning tasks
- Multiple model size options (1B, 3B, 8B, 11B, 70B, 90B, 405B) optimized for different deployment constraints
- Multilingual capabilities with support for translation and cross-language understanding across MMLU Multi benchmarks
- Fine-tuning and model distillation support with full model weight access for custom optimization
- JSON output structured generation with documented cost efficiency improvements
- Comprehensive benchmarking across reasoning (MMLU Pro, GPQA), coding (LiveCodeBench), and multimodal tasks (MMMU, ChartQA, DocVQA)
Use Cases
- 1E-commerce product page generation and content localization (as demonstrated with Shopify's 76% throughput improvement)
- 2On-device inference for mobile and edge applications using lightweight 1B/3B Llama 3.2 variants
- 3Multi-document analysis and long-form content processing leveraging 10M context windows
- 4Multilingual translation and intent detection for customer support automation
- 5Synthetic data generation for training datasets using Llama 3.3's cost-efficient 70B variant
- 6Image and text reasoning tasks using Llama 4's native multimodal capabilities
- 7Custom fine-tuning for domain-specific applications in finance, healthcare, or legal domains
Pros & Cons
Advantages
- Commercial-friendly open-source licensing eliminates vendor lock-in and allows unrestricted production deployment without ongoing API fees
- Exceptionally competitive pricing ($0.19-$0.49/1M tokens) and cost efficiency (Shopify achieved 33% compute savings with JSON output optimization)
- True multimodality support in Llama 4 with early fusion architecture for integrated vision-language understanding, not bolted-on afterthought
- Extreme flexibility in deployment—from single H100 GPU edge deployments to distributed inference at scale, with full model weights accessible for optimization
- Comprehensive model sizing (1B to 405B parameters) allowing exact trade-off selection between capability and resource requirements
Limitations
- Requires significant infrastructure expertise and DevOps resources to self-host and maintain models compared to API-based alternatives
- No official managed API tier—organizations must choose between building custom inference pipelines or relying on third-party hosted solutions
- Limited information on safety guardrails; website mentions 'comprehensive protections' but provides no concrete technical details or red-teaming methodology
- Benchmark comparisons primarily against other open-source models; limited direct head-to-head performance data against GPT-4 or Claude on standardized tasks
- Model weights still require substantial VRAM (405B model needs distributed infrastructure); not practical for resource-constrained environments without quantization
Pricing Details
Llama models are free to download and run with no licensing fees. When used through hosting providers, inference costs range from $0.19-$0.49 per million tokens (estimates provided for Llama 4 with 3:1 blended ratio). Costs vary by deployment architecture: distributed inference at $0.19/Mtok, single-host deployment at $0.30-$0.49/Mtok. No pricing tier information for commercial support or managed services published on the website.
Who is this for?
ML engineers and DevOps teams at mid-to-large organizations needing cost-effective, self-hosted LLMs; e-commerce platforms requiring content generation and localization; companies with strict data privacy requirements precluding third-party API usage; research teams requiring full model access for fine-tuning and experimentation; startups seeking to minimize LLM operational costs through self-hosting.