Multimodal model

A multimodal model is an AI model that can work with more than one type of input or output, such as text, audio, images, video, or structured data in the same agent workflow.

How multimodal models work

A multimodal model can process or generate more than one kind of information. Instead of only reading text, it may understand audio, images, video, documents, or structured data depending on the model.

In avatar systems, multimodal models matter because the interaction itself is multimodal. The user may speak, the avatar may respond with voice and video, and the agent may need visual or contextual inputs to answer well.

A concrete example: a user shares a screenshot during onboarding, asks what went wrong, and the avatar uses visual context plus product documentation to explain the next step.

Multimodal does not automatically mean real-time. For live avatars, the model still has to fit inside a low-latency loop so the conversation does not feel delayed.

What Anam ships

Anam's Cara-4 model delivers expressive real-time avatars with around 150 ms server-side avatar-generation latency once a session is running, across 70+ languages. Builders use JavaScript and Python SDKs or integrations for LiveKit, Pipecat, ElevenLabs Agents, Agora, and VideoSDK. Bring any AI stack including OpenAI, Claude, Gemini, Mistral, Groq, Deepgram, Cartesia, or custom providers. The platform supports WebRTC delivery, SOC 2 Type II, HIPAA, zero data retention, and regional data residency. Sessions stream low-latency audio and video to browsers and native apps.

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Related terms

LLM (large language model)

An LLM, or large language model, is the AI model that interprets user input, reasons over context, and generates the response an avatar or voice agent will speak back to the user.

Digital human

A digital human is a realistic virtual person designed to communicate with users through a face, voice, expressions, and conversational behaviour, often powered by AI models and live media streaming.

Streaming inference

Streaming inference is the practice of producing model output incrementally as it is generated, so an avatar can start responding sooner instead of waiting for a full answer to finish.

Real-time avatar

A real-time avatar is a photorealistic digital character that listens, thinks, and speaks back inside a live audio and video stream, typically with sub-second response latency, so it can hold a two-way conversation rather than play a pre-rendered clip.

Frequently asked questions

What is a multimodal model?

A multimodal model can understand or generate multiple types of information, such as text, audio, images, video, or structured data, rather than working with text alone.

Why do multimodal models matter for avatars?

Avatar experiences combine speech, language, face animation, and video. Multimodal models can help the agent reason across those signals instead of treating the conversation as text only.

Is every avatar system multimodal?

Every avatar experience has multiple media outputs, but the underlying agent is only multimodal if the model itself can process or generate more than one modality.

What should teams check with multimodal avatar models?

Check latency, supported inputs, privacy controls, accuracy on visual or audio context, and whether multimodal reasoning improves the actual user workflow.

Last updated: 17th July 2026 · Reviewed quarterly.

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