If you’ve taken a look at the current landscape of Artificial Intelligence lately, you’ve probably felt it. You have a compelling use case, you know what problem to solve and you know you can use AI to solve it. But then, it hits you: “Should I use GPT-5.2? Wouldn’t it be too expensive? What about Gemini? OK but WHICH Gemini? Flash, Pro? Which version? Or should I probably go with something better trained for my specific needs?”

This has happened to me more times than I care to admit. And it even has a name (yes, I looked it up). It’s called choice paralysis and it’s an inevitable part of our job as developers. Not so long ago I faced the same when I struggled to pick the best cross-platform mobile framework. And before that it was frontend web technologies.

So many choices

Gone are the days when “AI” just meant one or two big monoliths. Today, we are overwhelmed by options—Proprietary giants, scrappy open source contenders, Small Language Models (SLMs) that run on your phone, and multi-modal powerhouses that can see and hear.

So, how do you actually pick one without wasting budget or engineering hours? You’re in luck because I did the homework so you don’t have to 😊

It all comes down to Three Big Questions

There are already several places where you can look at the publicly-available data about existing AI models and use this data as a starting point to make a decision.

  • Hugging Face: It’s usually the first place to start, an insane amount of open-source models, categorized and filterable
  • GitHub: Gives you access to several different models, via GitHub Copilot.
  • Microsoft’s AI Foundry: Several models ready to deploy to Azure.

These are great resources, but it turns out that instead of looking at benchmarks blindly, you should ask yourself three fundamental questions.

1. Can AI actually solve my use case?

Before worrying about token costs, define the problem.

  • How big should I go (LLMs vs. SLMs): Do you need deep reasoning and complex context (Large Language Models like GPT-5)? Or do you need speed, low cost, and edge compatibility (Small Language Models like Phi-3 or Mistral OSS)?
  • Modality matters: Are you just chatting? You probably need Chat completion. Doing math or coding? Look for reasoning models (like o1 or DeepSeek-R1). Need to analyze charts or generate logos? You need Multi-modal or Image Generation models (like GPT-4o or DALL·E 3).
  • Specialization: Sometimes a generalist fails. If you are predicting stock prices, you might need a time-series model (like Nixtla TimeGEN-1). If you are building for a specific region, a model trained on that specific language (like Core42 JAIS for Arabic) will outperform a generic one.

2. How do I select the best candidate?

Once you know AI can do it, you need to filter out the candidates.

  • Proprietary vs. Open Source: Proprietary models (Microsoft, OpenAI, Cohere) usually offer better security and “out-of-the-box” performance for enterprise needs. Open Source models (Llama, Mistral) offer flexibility, control, and lower costs—if you have the team to manage them.
  • Precision & Fine-tuning: Is the base model smart enough? If you are doing general customer support, a base model is likely fine. If you are doing medical coding, you might need to fine-tune a model on your specific dataset to get the precision up.
  • Evaluation: Don’t just trust the brochure. Use Benchmarks (Accuracy, Coherence, Groundedness) to narrow the field, but always run Manual or Automated Evaluations on your actual data to see how it performs in the wild.

3. Can I scale this?

It works on your laptop, but will it work for 100,000 users? This involves thinking about GenAIOps (Generative AI Operations), monitoring model drift, and ensuring your deployment method (serverless vs. managed infrastructure) can handle the load.

Real-Life Scenarios: Which Model Wins?

Theory is great, but let’s apply this logic to three distinct, real-world business problems.

Scenario A: The “Off-Grid” Field Technician App

The need: A utility company wants an app for field technicians who repair wind turbines in remote areas with spotty internet. The app needs to search through thousands of PDF manuals to answer questions like “What is the torque setting for Bolt X?”

  • The constraint: Low connectivity and older hardware tablets.
  • The solution: Small Language Model (SLM).
  • Why: A massive model like GPT-4 requires constant, high-speed cloud connectivity. An SLM like Phi-3 or a quantized Llama 3 8B is small enough to run locally on the device (or with minimal data). It doesn’t need to write poetry; it just needs to find facts in a manual.
  • Verdict: Go Small and Local.

Scenario B: The Global E-Commerce Expansion

The need: A fashion retailer is expanding into the Middle East. They need a marketing generator that writes culturally nuanced Instagram captions and product descriptions in Arabic.

  • The constraint: Cultural and linguistic accuracy is non-negotiable to avoid PR disasters.
  • The solution: Regional/Domain-Specific Model.
  • Why: While general models speak many languages, they often miss idioms or cultural context. A model like Core42 JAIS (specifically built for Arabic) or a fine-tuned version of Mistral Large (strong on European/multilingual tasks) will sound more human and less “translated.”
  • Verdict: Go Regional.

Scenario C: The “Smart” Investment Dashboard

The need: A fintech startup wants to let users upload screenshots of their monthly bank statements and get a breakdown of their spending habits, investment advice, and a projected savings graph.

  • The constraint: The input is visual (images of documents), but the output requires math and reasoning.
  • The solution: Multi-Modal Model + Function Calling.
  • Why: You need a model like GPT-4o that can “see” the screenshot (Vision) and extract the data. However, LLMs are notoriously bad at math. You would use the model to extract the data into JSON, then use Function Calling to pass that data to a deterministic code script (like Python) to calculate the savings graph accurately.
  • Verdict: Go Multi-Modal with Tool Use.

Conclusion

Don’t be paralyzed by choice! As it happens with almost everything in life, there is no “best” AI model—only the best model for your specific trade-offs. The best way to go about it is to visualize your specific use case and carefully analyze different models’ capabilities using the tools mentioned above.

You might also want to take a look at available AI resources such as Hugging Face’s Benchmark Collection, which offers a great look at the performance of nearly every model out there, based on a number of relevant metrics.

References: