Definition
Multimodal Model
A multimodal model can process and reason across more than one type of input — combining text with images, audio, video, or other data in a single model. Instead of handling each modality with a separate system, it understands them together, so it can describe a screenshot, answer questions about a chart, or relate a diagram to its caption.
Key takeaways
- A multimodal model can process and reason across more than one type of input — combining text with images, audio, video, or other data in a single model. Instead of handling each modality with a separate system, it understands them together, so it can describe a screenshot, answer questions about a chart, or relate a diagram to its caption.
- Early language models handled only text.
- In Planoda, multimodal understanding lets AI features work from screenshots and attachments alongside text, with output flowing through the same propose/approve review as any other agent action.
Early language models handled only text. A multimodal model is trained to take multiple input types into a shared representation, letting it relate them to each other — read the text in an image, interpret a chart, transcribe and reason over audio, or follow a sequence of video frames. The modalities are understood jointly rather than bolted together after the fact.
This unlocks tasks that pure text cannot. A user can paste a screenshot of a broken UI and get a diagnosis, hand over a photographed whiteboard and get structured notes, or ask questions about a document's layout and figures, not just its words. Some multimodal models also generate non-text output, but the core capability is unified understanding across input types.
The practical caveats mirror text models, amplified. Visual and audio inputs consume substantial tokens and cost, accuracy varies by modality and quality, and grounding still matters — a model can misread an image as confidently as it can misstate a fact. Treating multimodal output as a draft to verify, not ground truth, remains the right posture.
In Planoda, multimodal understanding lets AI features work from screenshots and attachments alongside text, with output flowing through the same propose/approve review as any other agent action.
Related terms
- Large Language Model (LLM)A large language model is an AI system trained on vast amounts of text to predict and generate language, enabling it to answer questions, summarize, write, and reason over natural language. LLMs power modern AI assistants and agents. They are probabilistic next-token predictors, which makes them remarkably capable but also prone to confident errors.
- InferenceInference is the act of running a trained model to produce output from new input — the moment a model is actually used, as opposed to trained. Every prompt sent to a language model triggers an inference pass. Inference dominates the running cost and latency of AI features, since it happens on every request while training happens once.
- GroundingGrounding is the practice of tying an AI model's responses to verifiable, external source data rather than relying solely on what it absorbed during training. By supplying relevant, current, authoritative context at query time — and ideally citing it — grounding reduces hallucination and keeps answers accurate, traceable, and specific to the user's actual data.
- Hallucination (AI)An AI hallucination is output from a language model that is fluent and confident but factually wrong, fabricated, or unsupported by the model's inputs — invented citations, fake quotes, or made-up details. It arises because models predict plausible text rather than retrieve verified facts, making hallucination an inherent risk that must be mitigated, not a bug that's simply fixed.
- TokenA token is the unit a language model reads and generates — typically a word fragment of a few characters rather than a whole word. Text is split into tokens before processing, and models bill, limit, and reason in tokens, not characters. As a rough guide, one token is about four characters or three-quarters of a word in English.