AI model to use (for a given task)
1. Understand the nature of the inputs: What information does the task actually depend on? The first question is brutally simple: Does this workout involve anything other than text? This would suffice in cases where the input signals are purely textual in nature, such as e-mails, logs, patient notesRead more
1. Understand the nature of the inputs: What information does the task actually depend on?
The first question is brutally simple:
Does this workout involve anything other than text?
This would suffice in cases where the input signals are purely textual in nature, such as e-mails, logs, patient notes, invoices, support queries, or medical guidelines.
Text-only models are ideal for:
- Inputs are limited to textual or numerical descriptions only.
- The interaction with one another is performed by means of a chat-like interface.
- The problem described here involves natural language comprehension, extraction, and classification.
- The information is already encoded in structured or semi-structured form.
Consequently, multimodal models are applied when:
- Pictures, scans, videos, or audios representing information
- These are influenced by visual cues, such as charts, ECG graphs, X-rays, and patterns of layout.
- This use case involves correlating text with non-text data sources.
Example:
Symptoms the doctor is describing are doable with text-based AI.
The use case here-an AI reading MRI scans in addition to the doctor’s notes-would be a multimodal one.
2. Complexity of Decision: Would we require visual or contextual grounding?
Some tasks need more than words; they require real-world grounding.
Choose text-only when:
- Language fully represents the context.
- Decisions depend on rules, semantics or workflow logic.
- Precision was defined by linguistic comprehension, namely: summarization, Q&A, and compliance checks.
Choose Multimodal when:
- Grounding enhances the accuracy of the model.
- This use case involves the interpretation of a physical object, environment, or layout.
- There is less ambiguity in cross-referencing between texts and images, or vice-versa.
Example:
Check for compliance within a contract; text only is fine.
Key field extraction from a photographed purchase bill; multimodal is required.
3. Operational Constraints: How important are speed, cost, and scalability?
While powerful, multimodal models are intrinsically heavier, more expensive, and slower.
Text should be used only when:
- The latency shall not exceed 500 ms.
- All expenses are to be strictly controlled.
- You need to run the model either on-device or at the edge.
- You process millions of queries each day.
Use ‘multimodal’ only when:
- Additional accuracy justifies the compute cost.
- The business value of visual understanding outstrips infrastructure budgets.
- Input volume is manageable or batch-oriented
Example:
Classification of customer support tickets → text only, inexpensive, scalable
Detection of manufacturing defects from camera feeds → Multimodal, but worth it.
4. Risk profile: Would an incorrect answer cause harm if the visual data were ignored?
Sometimes, it is not a matter of convenience; it’s a matter of risk.
Only Text If:
- Missing non-textual information does not affect outcomes materially.
- There is low to moderate risk within this domain.
- Tasks are advisory or informational in nature.
Choose multimodal if:
- Misclassification without visual information could be potentially harmful.
- You operate in regulated domains like: health care, construction, safety monitoring, legal evidence
- It is a decision that requires evidence other than in the form of language for its validation.
Example:
A symptom-based chatbot can operate on text.
A dermatology lesion detection system should, under no circumstances
5. ROI & Sustainability: What is the long-term business value of multimodality?
Multimodal AI is often seen as attractive but organizations must ask:
Do we truly need this, or do we want it because it feels advanced?
Text-only is best when:
- The use case is mature and well-understood.
- You want rapid deployment with minimal overhead.
- You need predictable, consistent performance
Multimodal makes sense when:
- It unlocks capabilities impossible with mere text.
- This would greatly enhance user experience or efficiency.
- It provides a competitive advantage that text simply cannot.
Example:
Chat-based knowledge assistants → text only.
Digital health triage app for reading of patient images plus vitals → Multimodal, strategically valuable.
A Simple Decision Framework
Ask these four questions:
Does the critical information exist only in images/ audio/ video?
- If yes → multimodal needed.
Will text-only lead to incomplete or risky decisions?
- If yes → multimodal needed.
Is the cost/latency budget acceptable for heavier models?
- If no → choose text-only.
Will multimodality meaningfully improve accuracy or outcomes?
- If no → text-only will suffice.
Humanized Closing Thought
It’s not a question of which model is newer or more sophisticated but one of understanding the real problem.
If the text itself contains everything the AI needs to know, then a lightweight model of text provides simplicity, speed, explainability, and cost efficiency.
But if the meaning lives in the images, the signals, or the physical world, then multimodality becomes not just helpful-but essential.
See less
1. Start with the Problem — Not the Model Specify what you actually require even before you look at models. Ask yourself: What am I trying to do — classify, predict, generate content, recommend, or reason? What is the input and output we have — text, images, numbers, sound, or more than one (multimoRead more
1. Start with the Problem — Not the Model
Specify what you actually require even before you look at models.
Ask yourself:
For example:
When you are aware of the task type, you’ve already completed half the job.
2. Match the Model Type to the Task
With this information, you can narrow it down:
Task Type\tModel Family\tExample Models
Text generation / summarization\tLarge Language Models (LLMs)\tGPT-4, Claude 3, Gemini 1.5
Image generation\tDiffusion / Transformer-based\tDALL-E 3, Stable Diffusion, Midjourney
Speech to text\tASR (Automatic Speech Recognition)\tWhisper, Deepgram
Text to speech\tTTS (Text-to-Speech)\tElevenLabs, Play.ht
Image recognition\tCNNs / Vision Transformers\tEfficientNet, ResNet, ViT
Multi-modal reasoning
Unified multimodal transformers
GPT-4o, Gemini 1.5 Pro
Recommendation / personalization
Collaborative filtering, Graph Neural Nets
DeepFM, GraphSage
If your app uses modalities combined (like text + image), multimodal models are the way to go.
3. Consider Scale, Cost, and Latency
Not every problem requires a 500-billion-parameter model.
Ask:
Example:
The rule of thumb:
4. Evaluate Data Privacy and Deployment Needs
If your business requires ABDM/HIPAA/GDPR compliance, self-hosting or API use of models is generally the preferred option.
5. Verify on Actual Data
The benchmark score of a model does not ensure it will work best for your data.
Always pilot test it on a very small pilot dataset or pilot task first.
Measure:
Sometimes a little fine-tuned model trumps a giant general one because it “knows your data better.”
6. Contrast “Reasoning Depth” with “Knowledge Breadth”
Some models are great reasoners (they can perform deep logic chains), while others are good knowledge retrievers (they recall facts quickly).
Example:
If your task concerns step-by-step reasoning (such as medical diagnosis or legal examination), use reasoning models.
If it’s a matter of getting information back quickly, retrieval-augmented smaller models could be a better option.
7. Think Integration & Tooling
Your chosen model will have to integrate with your tech stack.
Ask:
If you plan to deploy AI-driven workflows or microservices, choose models that are API-friendly, reliable, and provide consistent availability.
8. Try and Refine
No choice is irreversible. The AI landscape evolves rapidly — every month, there are new models.
A good practice is to:
In Short: Selecting the Right Model Is Selecting the Right Tool
It’s technical fit, pragmatism, and ethics.
Don’t go for the biggest model; go for the most stable, economical, and appropriate one for your application.
“A great AI product is not about leveraging the latest model — it’s about making the best decision with the model that works for your users, your data, and your purpose.”
See less