Omni models: Using multimodal AI correctly

Omni models are considered one of the most important advancements of modern AI systems. They are not limited to one modality, but understand and process text, images, audio, video, and data simultaneously. In doing so, they are fundamentally changing how AI is used in everyday work - and are placing new demands on good prompting.

• What distinguishes Omni models from classic and thinking models
• Why multimodality is a real game changer
• Which use cases benefit in particular
• What is fundamentally changing in prompting
• What typical mistakes companies make

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Omni models explained: What does “omni” mean in AI?

The term omni comes from Latin and means “everything” or “comprehensive.” In AI models, he describes systems that Combine multiple modalities natively in one model.

Omni models can, for example:

• Read, write, and analyze text
• Interpreting and describing images
• Understanding charts, screenshots, or documents
• Hear, transcribe, and generate audio
• Combine content across modalities

Important: Omni models are not simply several individual models that are linked together. They are trained in such a way that they Relationships between modalities understand - between an image and an accompanying text, for example, or between spoken language and a graphic.

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Omni models vs. classic AI models

The difference is not so much in intelligence as in Range of perception.

Classic AI models are mostly specialized:

• Text models for writing and analysis
• Image models for image generation or recognition
• Audio models for speech or transcription

Omni models combine these capabilities in one system. This allows them to solve tasks that were previously only possible with tool chains — or not at all.

example:
A classic model can summarize a text.
An Omni model can analyze a photo of a presentation slide, understand the spoken comment, and create a structured summary from it.

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Why Omni models are so relevant in companies

Everyday work in companies is almost always multimodal: PDFs, emails, screenshots, spreadsheets, meetings, whiteboards, presentations, videos.

Omni models fit right in with this reality.

Typical business applications include:

• Analyze PDFs, screenshots, and presentations
• Customer service support (text + screenshot + log file)
• Evaluation of meetings (audio + slides + notes)
• Training & enablement (video + text + tasks)
• Marketing & content (image, text, video in one workflow)

Instead of coordinating multiple tools, teams can use an omni-model consistent workflows depict.

A common mistake in thinking: treating Omni models as a normal text model and only using text prompts.

This gives away the greatest added value.

Omni models are built to Draw context from multiple sources at the same time. If you only enter text, you actually use it below value.

The real advantage comes when content is combined:

• “Here is a screenshot + a brief context + a question”
• “Here is a PDF + a specific task”
• “Here is a video + the goal I want to achieve”

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Prompting in Omni models: The biggest difference

Prompting for Omni models is less about “nice wording” and more about clean context control.

The central question is:
👉 What should the model take into account from which modality — and for what?

A good omni prompt makes explicit:

• Which inputs are relevant
• How they relate to each other
• What is the goal

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The ideal prompt structure for Omni models

The following structure has been tried and tested:

1. Define goal
What should come out in the end? Analysis, summary, decision, draft?

2. Name inputs
What content does the model receive? (e.g. PDF, screenshot, audio, text)

3. Explain the role of inputs
What is which input relevant for?
Example: “The image shows the current situation, the text describes the target. ”

4. Define procedure
Should it be compared, explained, prioritized, or transformed?

5. Set output format
Key points, table, recommendation, checklist, decision tree?

Omni models respond particularly well when they know How they should combine different modalities.

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Example: Bad vs. good omni-prompt

Imprecise:
“Analyze this document. ”

OMNI-compatible:
“You will receive a PDF with a process description and a screenshot from our tool.
Analyze whether the process is correctly implemented in the screenshot.
Identify discrepancies, risks, and potential improvements.
Show the result as a table. ”

In the second case, the Omni model knows:

• Which inputs are there
• How they relate to each other
• What exactly should be checked

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Omni models and context limits: Less is often more

A common mistake is too many inputs unfiltered to provide.

Omni models can process a lot, but they also benefit from focus. Better is:

• Mark relevant pages
• Explain which section is important
• omit irrelevant information

Quality is not achieved by maximum amount of data, but by clear context.

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What is different about prompting compared to thinking models

Thinking models focus on Depth of thought and logic.
Omni models focus on Context breadth and perception.

This has consequences:

• With thinking models, you control the thought process
• With Omni models, you control the information space

The two can be combined — but the prompt logic is different. An omni prompt first asks: What does the model see, hear, and read?
A thinking prompt first asks: How should the model think?

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Typical mistakes when using Omni models

In practice, you see the same stumbling blocks over and over again:

• Use Omni models only for text
• unclear allocation of inputs (“There is something here, make something out of it”)
• missing goal definition
• too complex tasks without structure
• no separation between observation and evaluation

If you avoid these mistakes, you get significantly more consistent and useful results.

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Implement omni-models correctly in everyday business life

For companies, this means: Omni models need clear rules of engagement.

The following are useful:

• defined use cases (e.g. “document + screenshot analysis”)
• Prompt templates for common tasks
• Training employees in dealing with multimodal inputs
• clear data rules (what can be uploaded?)

Without these guidelines, Omni models quickly become “all-rounders without focus.”

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Omni Model FAQ

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Are Omni models always better than other AI models?

No They are superior when multiple modalities are relevant. Specialized models are often more efficient for text-only tasks.

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Do I need special prompting knowledge?

Yes — but less technical, more structural. It is crucial to clearly explain inputs and goals.

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Can Omni models replace thinking models?

Not completely. Omni models are strong in perception and context, thinking models in logical depth. The combination is often ideal.

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Are Omni models more critical of data protection?

Not always, but they should because documents, images, or audio are used more frequently. Clear rules and secure platforms are therefore particularly important.

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Conclusion: Omni models develop their value through context, not through magic

Omni models are a big step towards realistic AI work assistantbecause they can work the way people do: see, read, listen and combine.

However, your potential only develops if:

• the context is neatly structured
• Prompting is thought of as multimodal
• Fields of application are chosen consciously

If you use Omni models correctly, you reduce tool overgrowth, accelerate workflows and improve the quality of complex tasks.

The KI Company helps companies to meaningfully integrate omni-models into everyday work — from use case design and prompt templates to governance and enablement for teams. If you would like to know where Omni models provide you with real added value, we would be happy to advise you without obligation.