Multimodal AI extends generative AI beyond text: depending on the model and service, it can take in or produce combinations of text, images, audio, and video. That can make it useful for tasks such as asking questions about a picture or finding information in a recording. It is not a universal upgrade to text-only LLMs, though. Capabilities, file handling, quality, and cost vary by model and task, so choose and test against the work you actually need done.
What are multimodal LLMs?
A text-centric LLM receives and returns text. A multimodal system can work with text alongside other kinds of content, such as an image, an audio recording, or a video. Some services also generate media. The term describes a broad class of workflows, not a promise that one model can handle every input and output type.
For example, Google documents content generation through its API across text, image, audio, and video, while also noting that input capabilities vary by model. A specific model, endpoint, or processing route may support only a subset of those capabilities. Check the provider’s current documentation for the exact model you plan to use, rather than relying on a platform-wide label such as “multimodal.” See Google’s content-generation API documentation.
How are multimodal AI models different from text-only LLMs?
The key difference is not simply that a model can “see” or “hear.” Each modality adds its own input formats, preprocessing choices, limits, and ways to fail. A model that can answer questions about a still image may not be suited to detecting a brief event in a video; a service that accepts audio may not return the kind of output a workflow needs.
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| Workflow | Examples of tasks | What to check |
|---|---|---|
| Image | Captioning, classification, visual question answering; object detection or segmentation on specifically enhanced models | Supported file formats, image size and detail handling, orientation, clarity, and whether the required task is supported |
| Audio | Audio understanding or generation as part of a multimodal workflow | Whether the exact model accepts the recording and can produce the required output; confirm format and limits in its documentation |
| Video | Description, segmentation, information extraction, or questions about events and timestamps | Duration, frame sampling, audio handling, and whether short or fast events remain visible to the processing route |
| Mixed media | Combining text prompts with media inputs, or producing media alongside text | Whether the chosen model and endpoint support the combination and output format, not just each modality separately |
These are task categories, not a capability checklist for every product. For instance, Anthropic’s model overview describes its current models as supporting text and image input, text output, vision, and tool use; those details are specific to the models and platform described there. Its models overview and vision documentation are useful examples of why model and platform limits need to be checked individually.
What can multimodal AI do with images and video?
Images: ask about visual content, but account for detail
Google documents image captioning, classification, and visual question answering, with object detection and segmentation available on specifically enhanced models. Its image guide lists PNG, JPEG, WebP, HEIC, and HEIF input. For that API, images whose width and height are both 384 pixels or less are allocated 258 tokens; larger images are handled through tiling. These are Google API mechanics, not universal vision-model rules.
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More detail can help with fine print or small objects, but Google’s media-resolution control can also increase token use and latency. Its guidance recommends checking image rotation and using clear, non-blurry images. Outputs can still be inaccurate, biased, or offensive, so Google’s image guidance recommends post-processing and human evaluation. See Google’s image-understanding documentation.
Video: sampling can hide brief events
Video understanding can support descriptions, segmentation, information extraction, and questions tied to timestamps. In Google’s documented static processing mode, video is sampled at one frame per second and audio is processed at 1 Kbps mono. Google cautions that fast action may lose detail at that frame rate; some listed models offer agentic processing that explores a timeline adaptively. Availability and behavior depend on the model and processing mode.
This distinction matters when a missed moment has consequences—for example, reviewing sports footage, a manufacturing process, or surveillance video. Test clips containing brief events and verify timestamp accuracy instead of assuming that a video summary covers every moment. Google’s video-understanding guide describes its processing approaches and limits.
How do I choose a multimodal AI model?
Start with the workflow, not a “best model” ranking. Compare candidates using the same representative inputs and desired outputs, then weigh their performance against operational constraints.
- Task and modality fit: Confirm the exact model accepts the required input types and returns the output format your workflow needs. Distinguish a general perception task from a specialized function such as segmentation.
- Quality and reliability: Try ordinary examples as well as ambiguous, degraded, edge-case, and adversarial inputs. Define which errors matter and retain human review where a wrong output could cause harm.
- Coverage and limits: Check image dimensions, video duration and frame sampling, audio tracks, file sizes, context limits, and any restrictions imposed by the model, endpoint, or hosting platform.
- End-to-end latency and cost: Measure preparation, upload, processing, retries, and human review on the intended workload. Higher image detail can increase token use and latency, and the same task may behave differently with different media settings.
- Integration and operations: Compare API shape, streaming or real-time needs, tools, storage and file handling, platform availability, and monitoring requirements. Google’s API reference documents standard, streaming, and real-time APIs, and describes its Interactions API as optimized for agentic workflows and complex multimodal, multi-turn conversations.
- Data handling and governance: Review the provider’s current terms and your organization’s privacy, security, safety, provenance, oversight, and incident-response requirements. The cited model and API documentation does not establish a universal data-retention or legal-compliance answer.
For technical comparisons, use the provider’s current documentation—such as Google’s API reference, Anthropic’s model overview, and platform-specific vision limits—because supported models and limits can change.
A practical adoption sequence
- Specify the job. Record the input material, expected output, acceptable error rate, and what happens if the system is wrong.
- Shortlist documented candidates. Confirm current support for the required modality, model, endpoint, file types, and processing route.
- Build a representative test set. Include routine, poor-quality, ambiguous, and adversarial examples. Where useful, compare results with a human or the existing process.
- Measure the full workflow. Track task quality, failures, latency, cost, and review burden. Keep image-resolution and video-sampling settings visible so a result can be interpreted and reproduced.
- Pilot with oversight. Provide a human review path, monitoring, and a way to report and correct failures. Expand only when measured benefits justify the operational and risk costs.
What risks and safeguards matter?
Multimodal inputs can add useful context, but they do not make an output inherently reliable. A clear-looking answer can still be wrong, and a summary can omit a critical visual or audio detail. Validate outputs against the source material when accuracy matters, and use human judgment for consequential decisions.
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- Use scikit-learn to track an example ML project end to end
- Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
- Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
- Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
- Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
NIST’s AI Risk Management Framework is voluntary and intended to help incorporate trustworthiness considerations into the design, development, use, and evaluation of AI systems. Its Generative AI Profile is a companion resource for identifying generative-AI-specific risks and considering risk-management actions. NIST says the framework is being revised, so check its current status and materials on the AI Risk Management Framework page.
NIST’s Generative AI evaluation program examines capabilities and limitations across modalities, including adversarial evaluation. The program page reports that in its first text-summarization pilot, three generators produced summaries that fooled every detector. That finding is specific to that pilot; it does not establish that every detector fails on all content. It does show why a detector alone should not be treated as an authenticity control. See NIST’s Generative AI evaluation program.
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