What is Llama Guard 3-11B-Vision?
Llama Guard 3-11B-Vision is Meta’s multimodal content-safety classifier. It is based on the Llama 3.2 Vision model family and has approximately 11 billion parameters. Its purpose is not to answer ordinary user questions, generate images, or function as a general visual assistant. Instead, it evaluates content and returns a textual safety decision.
The model is intended for applications in which a multimodal language model receives an image and text, produces a response, and needs an additional check for potentially harmful material. Llama Guard 3-11B-Vision can classify the mixed prompt and the associated text response using a 13-category hazard taxonomy based on the MLCommons safety framework.
Meta released the model on September 25, 2024. It is available as open weights through Meta’s Llama repositories and the official Meta Llama organization on Hugging Face. This makes local or self-managed deployment possible, subject to the Llama 3.2 Community License and the hardware and inference software required by the operator.
How the model works
In a typical safety pipeline, an application sends Llama Guard 3-11B-Vision a user prompt that may contain text and an image. The model examines that input and can also evaluate a text response associated with the prompt. Its output is text describing whether the content is safe or unsafe. When it identifies unsafe material, it reports the relevant hazard category or categories.
This design separates safety classification from the main conversational model. A developer can therefore use one model to produce an answer and Llama Guard 3-11B-Vision to screen the input and output. The classifier’s result can be used to block a response, ask for a safer reformulation, route the case to human review, or log a policy decision.
The documented vision configuration processes one image at a time. Meta’s model materials describe resizing the image into four 560-by-560-pixel chunks before classification. That implementation detail matters when designing an application: the model should not automatically be assumed to support arbitrary numbers of images or every image-processing workflow.
Supported inputs and outputs
| Capability | Supported or documented behavior |
|---|---|
| Text input | Yes |
| Image input | Yes; the documented setup supports one image at a time |
| Audio input | Not documented |
| Video input | Not documented |
| Text output | Yes; safety labels and hazard-category information |
| Image, audio, or video output | No |
| Tool or function calling | Not documented as a model capability |
The model’s output is textual rather than a direct image or other media output. “Multimodal” describes the input and classification task here, not a capability to generate visual content. Llama Guard 3-11B-Vision should therefore be treated as a moderation component, not as an image-generation model or a general vision chatbot.
Context and output limits
The supplied model data lists a 128,000-token context length. A context window is the amount of text and other supported input information that the model can process in one request, although the practical usable amount can depend on the deployment implementation and image-processing pipeline.
No verified maximum output-token limit is specified in the supplied first-party materials. In practice, the expected result is a comparatively short classification response rather than a long narrative answer, but an application should not treat an undocumented output ceiling as a guaranteed specification.
Safety taxonomy and practical strengths
Llama Guard 3-11B-Vision’s main strength is specialization. It is designed specifically to identify unsafe content in multimodal interactions instead of trying to balance safety classification with broad conversation, coding, or creative-generation duties. This focused role can make it useful as a separate checkpoint in systems that accept images alongside text.
- Mixed prompt screening: It can assess text and image content together, which is important when the meaning of a request depends on both modalities.
- Response screening: It can classify text responses associated with multimodal prompts, helping detect unsafe output from another model.
- Category-level results: An unsafe result can include information about the relevant hazard categories rather than only a binary decision.
- Open-weight deployment: Operators can download and run the model through compatible infrastructure instead of relying on an identified official per-token hosted endpoint.
- Integration flexibility: A separate classifier can be placed before user content reaches a main model, after a response is generated, or at both stages.
These strengths are most relevant to developers building an application-level safety system. They do not mean that the classifier can replace policy design, human review, access controls, rate limits, or testing against adversarial inputs.
Limitations and risks
Meta describes the model as optimized for English. Applications serving other languages should validate performance rather than assuming that the same classification quality applies across languages.
The model is also not intended to replace every type of safety classifier. Meta recommends text-focused alternatives such as Llama Guard 3-8B or Llama Guard 3-1B when the task is text-only moderation. Conversely, Llama Guard 3-11B-Vision should not automatically be treated as a dedicated image-only moderation solution. Its documented role concerns multimodal prompts and associated text responses, and a specialized image safety system may be more appropriate for an image-only workflow.
Safety classification is inherently dependent on policy definitions and context. The model may miss harmful material, incorrectly flag benign material, or struggle when the relevant category requires current factual knowledge. Meta’s materials also warn that the model may be vulnerable to adversarial attacks. Users can intentionally phrase prompts, manipulate images, or construct inputs that try to evade the classifier.
For those reasons, Llama Guard 3-11B-Vision should be evaluated as one part of a broader safety process. Teams should test representative content, measure false positives and false negatives, define escalation paths, and consider human review for high-impact decisions.
Reasoning, coding, speed, and cost positioning
This is a safety classification model, not a general reasoning or coding model. It can analyze the content needed for a safety decision, but the supplied research does not establish broad mathematical reasoning, software-development, or agentic capabilities. It also has no documented tool or function-calling capability.
The accompanying editorial assessment gives the model a reasoning score of 4 out of 10 and a coding score of 2 out of 10. These are subjective database evaluations, not scores published by Meta and not benchmark results. They reflect the model’s limited suitability for general reasoning and coding compared with models designed for those tasks.
The same assessment assigns a speed score of 5 out of 10 and a cost score of 8 out of 10. These are also editorial indicators rather than provider specifications. The cost score reflects the potential appeal of downloadable weights compared with paying for a hosted moderation API, but local deployment still incurs hardware, storage, engineering, monitoring, and maintenance costs. An 11-billion-parameter model can also require more infrastructure than a smaller text-only safety classifier.
Pricing and availability
No official per-token hosted API price for this exact model is identified in the supplied first-party materials. Therefore, there is no verified input or output price to quote. The model is distributed as downloadable open weights, so the direct model price and the total cost of operation are different questions.
Self-hosting may be attractive when an organization needs control over data handling, deployment location, or throughput. The actual cost depends on the selected hardware, inference stack, number of classifications, and operational requirements. A hosted inference provider may offer simpler deployment, but its pricing and availability would be provider-specific and should not be confused with an official Meta price for Llama Guard 3-11B-Vision.
When to choose Llama Guard 3-11B-Vision
Choose Llama Guard 3-11B-Vision when the application needs an open-weight safety classifier that can inspect image-and-text prompts and associated text responses. It is a reasonable fit for multimodal chat applications, visual question-answering systems, image-assisted support tools, and other products where generated text must be screened alongside visual input.
It is particularly suitable when the team wants to deploy the safety layer locally or integrate it into a self-managed inference pipeline. The 13-category hazard taxonomy can also provide more actionable information than a single undifferentiated block or allow different categories to trigger different application responses.
Another option may be more appropriate when the workload is text-only, when the primary task is image-only moderation, or when the system needs a general-purpose assistant that can reason, code, use tools, or generate media. For text-only moderation, Meta’s Llama Guard 3-8B and Llama Guard 3-1B are the named alternatives in the supplied materials. For high-risk or adversarial environments, a layered system combining multiple checks and human oversight may be preferable to relying on this model alone.
Bottom line
Llama Guard 3-11B-Vision occupies a focused role in Meta’s Llama Guard lineup: it is an open-weight, vision-enabled safety classifier for multimodal interactions. Its defining value is the ability to inspect an image together with text and classify related text responses using a structured hazard taxonomy. Its boundaries are equally important: it is optimized for English, supports one image in the documented configuration, does not provide media generation, has no verified hosted token price, and should not be treated as a complete safety system or a general-purpose model.