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Independent tool overview
Meta Llama Models at a glance
Meta Llama is a family of downloadable, open-weight AI models for developers and researchers. The current Llama 4 releases—Scout and Maverick—use a mixture-of-experts architecture for text, code, and image understanding, but they remain large infrastructure projects governed by Meta's custom Llama 4 Community License rather than a standard open-source license.
Visit the official Meta Llama Models site ↗
- Product type
- Downloadable open-weight foundation-model family
- Current family
- Llama 4 Scout and Llama 4 Maverick
- Input
- Multilingual text and images
- Output
- Multilingual text and code
- Llama 4 Scout
- 17B active / 109B total parameters; up to 10M context
- Llama 4 Maverick
- 17B active / 400B total parameters; up to 1M context
- License
- Custom Llama 4 Community License
- Knowledge cutoff
- August 2024 for the published Llama 4 releases
Overview
What Meta Llama Models is
Meta Llama is a model family rather than a single end-user app. Developers can obtain weights from Meta or distribution partners, run them in their own infrastructure, fine-tune supported releases, or use a cloud provider that serves compatible Llama endpoints.
The newest model family currently highlighted in Meta's official developer materials is Llama 4. Scout activates 17 billion parameters across 16 experts, with 109 billion total parameters and a published maximum context length of 10 million tokens. Maverick also activates 17 billion parameters but uses 128 experts, 400 billion total parameters, and a 1 million-token context length.
Both Llama 4 releases are natively multimodal for text and image input and produce text or code. Meta's model card lists 12 officially supported languages and an August 2024 knowledge cutoff. These are static models, so applications that need current facts must add retrieval, tools, or fresh domain data.
Llama weights are often described as open, but the licensing distinction matters. Llama 4 uses a custom community license with attribution, naming, acceptable-use, redistribution, scale, and regional conditions. Teams should review the exact license for the specific Llama generation they deploy instead of assuming every Llama model has the same terms.
Use cases
Who Meta Llama Models is best for
The strongest fit depends on the job you need the product to complete, not the size of its feature list.
Multimodal applications
Build assistants that combine text prompts with image understanding, captioning, or visual question answering.
Long-context experiments
Evaluate large document collections or codebases with Scout's published long-context capability, while testing quality at the lengths you actually need.
Customization and fine-tuning
Adapt an eligible Llama release to a domain or workflow after reviewing its license, data, and safety requirements.
Cloud-provider flexibility
Use a provider that serves Llama models when self-hosting the full checkpoint is not operationally practical.
Research and evaluation
Inspect model weights, published model cards, benchmarks, data disclosures, and safety guidance.
Capabilities
Core Meta Llama Models features
Llama 4 Scout
The smaller Llama 4 mixture-of-experts release has 17B active and 109B total parameters, native text-and-image input, and a published 10M-token maximum context.
Llama 4 Maverick
The larger released Llama 4 model has 17B active and 400B total parameters, multimodal input, a 1M-token context, and stronger capacity than Scout.
Native multimodality
Early-fusion training lets the released Llama 4 models reason over text and images within the same model.
Mixture-of-experts architecture
Only a subset of total parameters is activated for a token, balancing model capacity with inference efficiency.
Download and partner access
Meta lists direct access plus distribution through Hugging Face, Kaggle, cloud platforms, and edge partners.
Older model choices
Llama 3.1, 3.2, 3.2-Vision, and 3.3 remain relevant when a smaller footprint, text-only model, or existing ecosystem support is more important than Llama 4 capabilities.
Safety and use policies
Meta publishes model cards, an acceptable-use policy, and supporting repositories for safety evaluation and deployment guidance.
Process
How the Meta Llama Models workflow works
Step 1
Choose the capability target
Define whether the application needs image understanding, long context, multiple languages, code generation, local deployment, or a smaller older checkpoint.
Step 2
Read the exact model card and license
Review the version-specific license, acceptable-use policy, supported languages, knowledge cutoff, intended uses, and geographic conditions.
Step 3
Estimate real infrastructure
Test memory, quantization, throughput, latency, storage, networking, and concurrency instead of selecting a model from active-parameter count alone.
Step 4
Select a deployment route
Request weights for self-hosting or compare cloud and inference partners based on price, region, privacy, controls, and model version.
Step 5
Build the application layer
Add retrieval, tool use, system prompts, access controls, logging, and fallbacks around the static foundation model.
Step 6
Evaluate on representative data
Measure accuracy, hallucination, visual reasoning, long-context retrieval, safety, latency, and cost with your own tasks.
Step 7
Complete compliance and launch review
Implement required attribution, naming and notices, confirm data rights, review high-risk use cases, and monitor production outputs.
Cost
Meta Llama Models pricing and free plan
Meta makes Llama model weights available without an upfront download fee under version-specific community licenses. That is not the same as free deployment: self-hosting requires substantial compute and engineering, while cloud and inference providers set their own token, endpoint, fine-tuning, or capacity prices.
Model weights
No download fee
Access is governed by the license and approval or distribution process for the selected model.
- Llama 4 uses a royalty-free limited license with conditions
- Model generations can have different license terms
- Downloading weights does not include hosting or support
Self-hosted deployment
Infrastructure costs vary
The operator pays for GPUs, storage, networking, observability, security, and engineering.
- Meta says Scout can fit on one H100 with on-the-fly int4 quantization
- Meta says Maverick FP8 can fit on one H100 DGX host
- Production throughput and concurrency can require additional capacity
Cloud or inference providers
Provider pricing
Managed Llama endpoints are billed under each partner's current terms.
- Confirm the exact model revision and quantization
- Compare token pricing, reserved capacity, data retention, and regions
- Fine-tuning and dedicated endpoints may be priced separately
Pricing checked . Check current pricing at the source ↗
Assessment
Meta Llama Models strengths and limitations
Where it stands out
- Downloadable weights with a broad deployment and tooling ecosystem
- Native text-and-image understanding in Llama 4 Scout and Maverick
- Very long published context limits, especially for Scout
- Multiple model generations and sizes for different infrastructure constraints
- Can be self-hosted for greater control over data flow and operations
- Detailed official model cards, license terms, and acceptable-use policies
What to consider
- Llama 4 is open-weight under a custom license, not a standard OSI-approved open-source license
- The released Llama 4 checkpoints are too large for ordinary consumer hardware without aggressive compression or remote hosting
- A maximum context limit does not guarantee reliable recall or reasoning across every token
- The August 2024 knowledge cutoff requires retrieval or tools for current information
- Model outputs can be inaccurate, biased, unsafe, or unsupported and need application-level safeguards
- The license requires attribution and imposes redistribution, naming, acceptable-use, scale, and other conditions
- The multimodal license has a developer restriction for individuals or companies based in the European Union, with an end-user exception described in the policy
- Self-hosting transfers monitoring, security, upgrades, capacity planning, and incident response to the operator
Compare
Meta Llama Models alternatives
The right alternative depends on the specific output, workflow, controls and budget your project requires.
Consumer
Mistral 4 Small
A newer open-weight multimodal mixture-of-experts alternative for teams comparing licenses, efficiency, and deployment footprints.
Explore Mistral 4 Small →Consumer
Gemma 4
Google's current open-weight family offers additional model sizes and an alternative ecosystem and license.
Explore Gemma 4 →Consumer
Qwen3.5 Small
A compact open-weight model family worth testing when local resource limits matter more than maximum model capacity.
Explore Qwen3.5 Small →Coding
Ollama
A local model runner that can simplify experiments with compatible Llama and other community model packages.
Explore Ollama →Questions
Meta Llama Models FAQs
What are Meta Llama models?
Meta Llama is a family of downloadable foundation models that developers can run, customize, or access through hosting partners for text, code, and—in Llama 4—image-understanding applications.
What are the current Llama 4 models?
Meta currently publishes Llama 4 Scout and Llama 4 Maverick. Both activate 17B parameters, while Scout has 109B total parameters and Maverick has 400B total parameters.
What is the difference between Llama 4 Scout and Maverick?
Scout is the smaller release and has a published 10M-token context limit. Maverick has many more total experts and parameters, a 1M-token context limit, and a larger deployment footprint.
Is Llama 4 open source?
Llama 4 weights are openly available, but they use Meta's custom Llama 4 Community License rather than a standard OSI-approved open-source license. The exact conditions should be reviewed before use or redistribution.
Can Meta Llama models understand images?
Llama 4 Scout and Maverick accept multilingual text and images and produce text or code. Older Llama releases vary, including text-only and dedicated vision versions.
How long is the Llama 4 context window?
Meta's model card lists up to 10 million tokens for Scout and 1 million tokens for Maverick. Teams should benchmark usable accuracy and cost at their actual input lengths.
Can I run Llama 4 on one GPU?
Meta says Scout can fit on a single H100 GPU with on-the-fly int4 quantization. Maverick's FP8 weights fit on a single H100 DGX host, which contains multiple GPUs rather than one ordinary graphics card.
Are Meta Llama models free?
There is no upfront fee to download the weights under the applicable license, but compute, cloud inference, storage, engineering, safety testing, and operations can be expensive.
What attribution does the Llama 4 license require?
The license includes redistribution notices and a prominent Built with Llama requirement for covered products or services. It also contains naming rules for distributed AI models improved with Llama materials or outputs.
Does the Llama 4 license allow commercial use?
It grants commercial and research rights subject to its conditions. Among other terms, organizations above the specified 700 million monthly-active-user threshold must request a separate license from Meta.
Does Llama 4 know current events?
Not inherently. The published Llama 4 releases have an August 2024 knowledge cutoff, so current applications need retrieval, external tools, or updated domain sources.
Should I use Llama 4 or an older Llama model?
Choose Llama 4 for current native multimodal and long-context capabilities. An older or smaller Llama model may be easier and cheaper to deploy when text-only performance or existing integrations are sufficient.
Bottom line
Our Meta Llama Models verdict
Meta Llama remains a strong option for teams that value downloadable weights, deployment choice, and a large supporting ecosystem. Llama 4 adds meaningful multimodal and long-context capabilities, but Scout and Maverick are not lightweight models, and their custom license deserves the same attention as performance and GPU cost. The right choice should follow a task-specific evaluation that includes accuracy, usable context, infrastructure, safety, regional availability, and legal terms.
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