Choosing The Right LLM for Your Task

yash raj suman -

I remember the first time I turned to AI to help me create a GUI for my ML model.

I prompted, "Create an interface for my ML model with XYZ features in it," and I thought the model would generate a GUI with my requirement in it.

But completing my task turned out to be more complicated than I expected.

At first, I relied on ChatGPT, and it impressed me with its detailed code explanations and deep reasoning. It didn’t just create a GUI, it also helped me making it user-friendly.

But when I started doing API integration, it started generating errors that it could not solve.

Then I switched to Claude, its newest model proved even better; it solved a couple of API integration issues and explained the errors behind it.

Then, I started experimenting with DeepSeek, I also found it surprisingly useful for solving my issue completely.

With so many AI models out there, picking the right one is getting tough. Whether you need great coding assistance, a math problem solver, or automation of the task, knowing each model’s strengths makes all the difference.

In this blog, I’ll help you choose which LLM model is best suited for your need so you do not waste your time like I did.

The Bazaar of LLM

As you explore AI models, you’ll quickly realize the options overwhelm you. You test and compare them, and a few stand out as the best and most popular right now. These are the models:

  1. ChatGPT by OpenAI
  2. Claude by Anthropic
  3. Gemini by Google DeepMind
  4. DeepSeek by DeepSeek AI (China-based AI research group)
  5. Grok by xAI

Use Cases

Instead of searching for the best AI overall, I focused on finding the right AI for the right job. I’ll explain which model is ideal for your use case so you can make smarter choices without the guesswork.

Coding

I’ve been there, stuck on a frustrating bug, It can be either a logic issue or some nasty edge cases, brainstorming my brain to figure out what’s wrong with my code.

I’ve spent hours digging through Stack Overflow, hoping to find a solution that someone else has already cracked.

After all that, I finally gave in and asked an LLM for help, only to watch it introduce even more errors.

That’s when I start wondering, am I actually getting closer to a fix, or is this just making things worse?

Then, after experimenting with various LLMs and researching, I finally found the LLM that solved my coding problem; it was Claude 3.7 sonnet

Claude 3.7 sonnet, with its impressive score of 70.3% on the SWE benchmark, which is a standard benchmark for testing LLM coding skills.

With Claude 3.7 Sonnet, instead of spending hours decoding cryptic errors, I finally had an AI that could break down problems, explain solutions clearly, and fix my code without introducing more issues.

General Knowledge

I’ve often found myself stuck on a difficult general knowledge question, whether it’s an obscure historical fact, a complex scientific principle, or a rare piece of trivia.

I’d spend hours jumping between Wikipedia pages, academic papers, and online discussions, trying to piece together a clear answer.

Despite all that effort, the information was often contradictory or incomplete. Frustrated, I’d turn to an AI for help, only to receive vague or misleading responses that raised even more questions than they answered.

After testing different LLMs and researching their accuracy, I finally found the one that delivered precise, well-supported answers by GPT-4.5.

GPT 4.5 has a web search ability that retrieves the latest updates regarding any topic.

With an 85.1% score on the MMLU benchmark, a widely respected test for evaluating knowledge across diverse fields.

Now, whenever I need an in-depth breakdown of scientific theories, a nuanced take on historical events, or a quick layman explanation on any random theories, GPT 4.5 is my first stop.

Reasoning

A few weeks ago, I was stuck on a tricky logic puzzle while preparing for a competitive exam.

No matter how many times I reread the problem, I couldn’t figure out the right approach.

I scoured online forums, watched explainer videos, but every explanation either missed key details or made the solution even more confusing.

That’s when I came across Gemini 2.0 Pro. Unlike other models, it broke the problem down step by step, explaining the logic behind each move.

With an impressive 64.7% score on the GPQA benchmark, a trusted test for evaluating deep reasoning skills, I finally had an AI that could think through problems rather than just guess.

Now, whenever I need help analyzing arguments, solving puzzles, or understanding difficult concepts, I turn to Gemini 2.0 Pro.

Gemini's reasoning capability breaks the questions into smaller steps and then follows a step-by-step, reason-based approach to tackle the problem, which helps me understand how to tackle complex questions.

Maths

I've often found myself grappling with challenging math problems, whether it's a complex calculus equation, an intricate geometry proof, or a tricky number theory puzzle.

I'd spend hours poring over textbooks, online resources, and math forums, trying to piece together a clear solution.

Frustrated, I'd turn to AI models for guidance, only to receive vague or incorrect responses that left me more confused than before.

I finally started using DeepSeek-R1. With an impressive 97.3% accuracy on the MATH-500 benchmark, a respected test evaluating proficiency in diverse high-level math problems.

Now, when I encounter a challenging math problem or require trustworthy mathematical information, I know exactly where to turn.

Conclusion

After all my trials and errors, I’ve realized that no single AI model is the absolute best; it all depends on what you need.

If you're debugging tricky code, Claude 3.7 Sonnet will guide you better than most. For general knowledge and well-researched answers, GPT-4.5 is unmatched.

If you're solving logic puzzles or tackling reasoning-heavy problems, Gemini 2.0 Pro is the smartest pick. And for advanced math, DeepSeek-R1 is simply unbeatable.

Looking back, I wasted a lot of time hopping between models, hoping for a one-size-fits-all solution. But now, I know that choosing the right tool for the job is what matters.

FAQ

What are the key metrics used in the MMLU benchmark?

Measures accuracy across 57 diverse subjects, including humanities, STEM, and social sciences. Evaluates both zero-shot and 5-shot performance to assess general knowledge and reasoning.

How does DeepSeek-R1's customization feature enhance its usability

Supports fine-tuning with user-provided data, enabling better domain-specific adaptation, improved response consistency, and enhanced personalization for specialized tasks.