TL;DR: Multimodal AI models go beyond text by processing inputs like voice, images, and video to deliver richer, more human-like interactions. They enable advanced use across customer support, finance, e-commerce, and creative industries by combining multiple data types for more accurate, context-aware outputs. As multimodal AI evolves, trends like on-device processing, native multimodal architectures, and real-time performance will shape the next generation of enterprise agents.

Text-only artificial intelligence (AI) agents started a powerful AI revolution in the consumer and enterprise market. Now multimodal AI model advancements have made it possible for teams to design agents that understand and respond across different input types—from analyzing image inputs to interpreting voice commands and more—opening next-level multimodal capabilities for automation at scale.

But as multimodal models gain traction, so does the challenge: With dozens of models on the market, how do leaders and developers choose the right one?

Below, you’ll learn how to evaluate multimodal models for your intended use, as well as popular models and emerging trends.

What are multimodal models and why do they matter?

Multimodal models are redefining what automation can do. By enabling agents to process and reason across multimodal data, they move beyond simple task execution toward deeper understanding and richer output.

What is a multimodal model?

A multimodal model is an AI system that processes multiple types of inputs simultaneously. This allows the model to interpret information more like humans do, integrating multiple signals to form a complete understanding of context.

By contrast, single-modality models handle only one type of input (like text-only chatbots or image-only classifiers), which limits their ability to reason across data types. Multimodal AI bridges those gaps, leading to richer, more accurate outputs because it draws from multiple sources of meaning rather than one isolated channel.

Why multimodal matters for AI agents

Multimodal AI matters because it makes agents more capable, context-aware, and human-like in how they understand and respond.

Multimodal systems let AI agents interpret multiple signals together for greater accuracy and a better user experience, whether they’re supporting customers or employees. This combined understanding helps agents move beyond basic Q&A to provide more intelligent, relevant responses.

For example:

• Voice + text: A support agent can process a caller’s verbal questions and automatically generate a written summary and next steps.
• Image + text: An insurance chatbot can assess a photo of vehicle damage and provide a clear, written claim estimate.
• Text + video: A knowledge agent can summarize a recorded meeting and highlight key decisions, trends, or speaker sentiment.

What makes a multimodal model the right fit for your project?

The multimodal model you choose determines your AI agent’s effectiveness. The best model aligns with your data types, accuracy requirements, and deployment goals to understand the information it’s given and deliver reliable results. To find that fit, you’ll need to look at both the model’s technical strengths and how well they match your specific use cases.

Core evaluation criteria

Evaluate models against the work your agent actually needs to do, then verify the model can sustain that performance in production. A gap on any of the following model evaluation criteria will cap the return on investment (ROI) on your agent:

• Input types supported: Can it reliably ingest all required inputs without brittle workarounds?
• Accuracy across different modalities: How well does it interpret each input type and align them to real-world conditions?
• Response quality: Are outputs clear, useful, and contextually appropriate? Does the model maintain coherence over longer tasks?
• Performance considerations: Does the model meet your latency, speed, and compute requirements during real workloads?
• Operational fit: Does it align with your deployment constraints, such as privacy, compliance, monitoring, and fallback behavior?

Use case considerations

Focus on how well each potential model performs in your specific environment and data context. A model that excels in general benchmarks might underperform once it’s exposed to your real workflows and input types.

Consider your use cases and the priorities for each. For example:

• Customer support: Prioritize real-time voice handling and reliable transcription accuracy to maintain seamless user experiences.
• E-commerce: Look for models with strong image/text alignment to improve visual search and product recommendations.
• Finance: Choose options that can integrate text and visual data, like charts, scanned documents, and tables, to generate compliant, insightful summaries.

Deployment strategy also shapes model performance. On-device models reduce latency and improve privacy but may face hardware limits. API-based setups scale quickly and integrate easily, though heavy traffic can affect response times. Match your approach to the demands of your workload.

Popular multimodal models to explore

Once you know what to look for, the next step is comparing leading multimodal models and how they’re being used across industries today.

GPT-4o

OpenAI’s GPT-4o combines text, image, and audio understanding within a single architecture, no separate modules required. It’s known for smooth, real-time performance and natural conversational flow, making it a popular choice for customer support, meeting analysis, and multimodal agents that need to react quickly and accurately.

Best for: Teams prioritizing latency, conversation quality, and end-to-end multimodal interaction in production environments.

Gemini

Gemini fuses text, code, image, and audio understanding. It stands out for reasoning depth and integration with Google’s broader ecosystem, which is helpful for enterprises using other Google products (like Workspace, Search, or Vertex AI).

Best for: Research-heavy teams or those needing advanced reasoning across multiple data types in scalable, cloud-native environments.

Claude

Claude is a primarily text-driven large language model (LLM), but its differentiator is its ability to process extremely long inputs while maintaining coherent reasoning. Through API integrations, it can extend to multimodal workflows where text is the primary anchor.

Best for: Teams that handle long-form or sensitive text where accuracy and transparency matter more than creative generation.

CLIP

CLIP excels at connecting language with visual understanding. It’s widely used as the backbone for retrieval, tagging, and similarity search systems that depend on linking text queries to images.

Best for: Retail, design, and manufacturing teams building visual search, automated tagging, or quality-control pipelines.

DALL-E

DALL-E translates natural-language prompts into detailed images and illustrations. It’s distinguished by its creative flexibility and brand-alignment tools, making it popular in content, marketing, and design workflows.

Best for: Creative and marketing teams needing rapid visual concepting or consistent branded imagery.

LLaVA

LLaVA is an open-source model that combines language understanding with visual analysis, offering transparency and flexibility that closed models can’t. It’s cost-effective for organizations building private, domain-specific visual agents.

Best for: Research and enterprise teams building domain-specific agents that need custom visual reasoning or full control over data and model behavior.

Specialized solutions for specialized use cases

Not every project works with a generic AI model. Enterprises with proprietary workflows, internal datasets, and unique systems often require more. Specialized AI models let you fine-tune the AI to those exact needs. For example, controlling how it interprets your documents, prioritizes important information, and follows your custom workflows.

They’re especially valuable when transparency and customization are priorities. You can see how and why the model reaches certain decisions (which is critical in regulated contexts like hiring or finance) while tailoring it to your business priorities and data formats. Specialized models also integrate seamlessly with existing tools and infrastructure, letting you extend AI capabilities without rebuilding your stack.

If your team wants to build voice-enabled agents with this level of control, enterprise-grade integrations, and customization, explore Rasa Voice today.

Future trends and what to watch for

Multimodal AI is evolving fast, and keeping up with these developments is what will help your business keep its competitive edge. Teams that track and adopt emerging capabilities early can automate faster, deliver richer experiences, and differentiate themselves before others catch up.

Let’s take a look at a few key trends shaping the next wave of multimodal AI:

• Native multimodal architectures: New models are being trained from the ground up to process multiple inputs together, improving context and reasoning accuracy.
• Smaller, on-device models: Local processing reduces latency, enhances privacy, and allows AI to run directly on user devices.
• Advanced instruction tuning tools: Fine-tuning is becoming simpler and more accessible, enabling teams to adapt generic models to specific workflows.
• Real-time responsiveness: Demand is growing for agents that can deliver instant, context-aware results in dynamic environments.
• Edge-capable deployment: AI that runs reliably on local or offline systems is increasingly critical for industries like logistics, manufacturing, and field operations.

To stay ahead, make learning an intentional part of your AI strategy. Read foundational books like The AI-Centered Enterprise, check out Rasa’s educational content on YouTube, and attend webinars like Rasa Labs: Shaping the Future of Conversational AI Together.

For a deeper look at what’s coming next in conversational AI, explore The Future of Conversational AI in 2025.

Choose the multimodal AI model that works best for your team

Selecting the right multimodal model starts with understanding your agent’s goals, the data it will process, and how it fits into your existing stack. There’s no one-size-fits-all approach, and what works today will change as new developments emerge.

Look for a platform that aligns with your workflows, supports customization, and scales with your enterprise. The right foundation lets your AI agent deliver faster, smarter, and more reliable results across every channel your users rely on.

Rasa approaches multimodal AI with a focus on flexibility and control, giving teams the infrastructure to build and operate generative AI agents at scale. Unlike black-box platforms, Rasa integrates seamlessly with custom NLU models, speech pipelines, and enterprise systems so teams can design experiences around real-world needs.

Connect with Rasa to start building the next generation of AI agents—your way.