Selecting a foundation model is one of the most important decisions you will make when building a generative AI solution. With so many models available today, knowing which one fits your use case can save time, reduce cost, and improve results.
This article walks through a structured way to think about model selection based on real-world requirements. Whether you are building a chatbot, a document summariser, or a visual analysis tool, this guide will help you navigate your options and make informed decisions.
Step 1: Understand if a model can solve your use case
Before choosing a model, ask: can a language model actually solve the problem you are working on?
To find out, begin by exploring what is available. You can browse models from:
- Hugging Face, which offers a vast range of open-source models across domains
- GitHub, where developers share and integrate models with tools like Copilot
- Azure AI Foundry, which provides a curated model catalogue along with tools for deployment and evaluation
Step 2: Choose between Large and Small Language Models
Not all models are created equal. Some are designed for highly complex tasks and require significant resources, while others are lighter, faster, and more cost-effective.
Large Language Models (LLMs)
These are powerful and capable of handling complex reasoning, multi-step instructions, and deep content generation. Examples include:
- GPT-4
- Mistral Large
- Llama3 70B
- Llama 405B
- Command R+
When to use: Choose these when you need high accuracy, long context handling, and robust multi-domain performance.
Small Language Models (SLMs)
These offer faster inference, lower cost, and are ideal for edge deployments or resource-constrained environments:
- Phi-3
- Mistral OSS models
- Llama3 8B
Decision tip: Choose Llama3 8B for Apache-2.0 licensing and broader community adoption. Opt for Phi-3 when memory and runtime efficiency are key.
Step 3: Focus on “What the model needs to do”
Use the table below to guide model selection by task type:
| Task Type | Recommended Models | Use Case Example |
|---|---|---|
| Multimodal | GPT-4o, Phi-3 Vision | Radiology report analysis |
| Reasoning | DeepSeek-R1, o1 | Coding tutor, logic puzzles |
| Text Generation | GPT-4, Mistral Large, Command R+ | Customer support chatbots |
| Image Generation | DALL·E 3, Stability AI | Marketing visuals |
| Search & RAG | Ada, Cohere Embed | Product recommendation engine |
| Time-Series | Nixtla TimeGEN-1 | Demand forecasting |
Caveat: While Mistral Large performs well in European languages, it underperforms in Arabic compared to Core42 JAIS.
Step 4: Consider Language, Region, and Industry specific needs
Foundation models are not all designed to serve general-purpose tasks. In fact, some of the highest-performing models are specialised for specific languages, geographies, or domains. If your use case requires cultural nuance, domain-specific terminology, or regional accuracy, it is worth considering a targeted model rather than a broad one.
Language and Region
- Core42 JAIS is a large language model purpose-built for Arabic, making it the most effective choice for applications serving Arabic-speaking users across different dialects. Its performance significantly exceeds that of general models like GPT-4 or Mistral Large when tested on Arabic NLP benchmarks.
- Mistral Large is optimised for high performance across European languages, including French, German, and Spanish. While it excels in multilingual tasks within Europe, its performance in non-European languages such as Arabic or Mandarin is less consistent.
Industry and Domain
- Nixtla TimeGEN-1 specialises in time-series forecasting. This makes it a strong candidate for use cases involving demand prediction, inventory planning, financial forecasting, or climate modelling.
- For the healthcare industry, fine-tuned models on clinical notes or medical data significantly outperform base models. While not listed in the general catalogue, many organisations develop domain-specific versions of models like LLaMA or Phi-3 using private healthcare datasets.
- In finance, models fine-tuned on structured financial documents and economic indicators demonstrate better performance in summarisation, trend analysis, and risk modelling.
Key takeaway: If your use case involves language-specific fluency or domain expertise, starting with a purpose-built model reduces the need for heavy fine-tuning and improves out-of-the-box performance.
Step 5: Open vs Proprietary Models
Proprietary Models
- Examples: GPT-4, Command R+, Mistral Large
- Best for enterprise-grade applications requiring support and advanced performance
Open-source Models
- Examples: Phi-3 (MIT), Mistral 7B (Apache-2.0)
- Provide flexibility, fine-tuning capability, and local deployment options
Note: Llama3 is open-weight, not open-source. It comes with usage restrictions and does not meet OSI standards.
Step 6: Evaluate for Precision
Precision in AI refers to how accurately the model generates relevant outputs.
- Base models like GPT-4 are versatile but may lack precision in narrow domains
- Fine-tuned models improve domain accuracy
Example: GPT-4 may reach 75 percent accuracy on medical QA by default, while fine-tuning on clinical notes can push this to 92 percent.
Use prompt engineering as a first step. If precision is still lacking, consider fine-tuning.
Step 7: Evaluate for Performance
Use metrics to compare models:
- Accuracy: Matches correct output
- Coherence: Logical and natural flow
- Fluency: Grammar and vocabulary usage
- Groundedness: Alignment with source input
- GPT Similarity: Semantic closeness to expected response
- Quality Index: Overall score
- Cost: Token usage cost
Start with manual reviews. For scale, use automated methods like F1 score, precision, and recall based on ground truth data.
Step 8: Plan for Scaling
Once your model works in a prototype, the next challenge is making sure it performs reliably and cost-effectively at scale. This requires planning across four main areas:
8.1 Model Deployment Strategy
You need to decide where and how the model will run:
- Cloud-hosted endpoints offer scalability and ease of management, ideal for variable workloads.
- On-premise or edge deployment is useful when you need full data control, low latency, or offline access.
- Containerised models (e.g. via Docker) give you portability and better integration with DevOps pipelines.
Key decision factor: Choose based on cost, latency, regulatory requirements, and infrastructure readiness.
8.2 Performance Monitoring
As usage grows, it’s critical to monitor the model’s behaviour in production.
Track metrics such as:
- Latency (P99): Measures the slowest responses, helping ensure user experience remains consistent.
- Throughput: Number of requests served per second or minute.
- Token usage per request: Helps track and optimise cost over time.
- Error rates and fallbacks: Monitor how often the model fails or defers to default logic.
Set up alerts for spikes in latency or sudden changes in usage patterns.
8.3 Output Quality Monitoring
Just because the model works today doesn’t mean it will tomorrow. Content quality can drift over time due to changes in data, prompts, or context.
Monitor:
- Drift detection: Use techniques like KL divergence or cosine similarity to detect when model outputs start deviating from expected patterns.
- Human-in-the-loop reviews: Periodically sample and manually review responses.
- Feedback loops: Collect user ratings, thumbs-up/down, or textual feedback to identify failure cases.
8.4 Prompt Lifecycle and Optimisation
Prompts are your interface to the model. Their structure directly affects output quality.
- Use prompt templates to standardise and version prompts across different use cases.
- Track prompt performance by associating them with output metrics like accuracy or user satisfaction.
- Optimise prompts iteratively, especially for use cases where hallucination or verbosity is a concern.
8.5 Model Lifecycle Management
Models, like code, need maintenance. Build processes to:
- Version models and datasets: Track which model was used for each release.
- Schedule evaluations: Run performance and accuracy benchmarks periodically.
- Retrain or replace models based on changes in your use case, input data, or quality expectations.
Consider adopting GenAIOps practices, similar to MLOps, for continuous integration, deployment, and monitoring of generative models.
Conclusion
Model selection is not about choosing the biggest or newest model. It is about matching the model to your specific goals, constraints, and context. With the right choice, you can move from proof of concept to production with confidence.
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