Author:superoo7

Compiled by: TechFlow

I get similar questions almost every day. After helping build more than 20 AI agents and investing a lot of money in testing models, I’ve learned some lessons that really work.

Here is a complete guide on how to choose the right LLM.

The current field of Large Language Models (LLMs) is changing rapidly. New models are released almost every week, each claiming to be the "best".

But the reality is: no one model fits all needs.

Each model has its own specific application scenario.

I have tested dozens of models and hope that my experience will help you avoid wasting unnecessary time and money.

To be clear: This article is not based on lab benchmarks or marketing claims.

What I will share is based on my actual experience building AI agents and generative AI (GenAI) products over the past two years.

First, we need to understand what LLM is:

The Large Language Model (LLM) is like teaching a computer to "speak human language." It predicts the most likely word to come next based on your input.

The starting point of this technology is this classic paper: Attention Is All You Need

The Basics - Closed Source vs. Open Source LLM:

  • Closed source: such as GPT-4 and Claude, usually run on a pay-per-use basis and hosted by a provider.

  • Open source: For example, Meta's Llama and Mixtral require users to deploy and run them themselves.

These terms can be confusing when you're new to them, but it's important to understand the difference.

Model size does not equal better performance:

For example, 7B means the model has 7 billion parameters.

But bigger models don’t always perform better. The key is to choose a model that fits your specific needs.

If you need to build X/Twitter bot or social AI:

@xai's Grok is a very good choice:

  • Generous free credits

  • Excellent understanding of social context

  • Although it is closed source, it is well worth trying

I strongly recommend this model to new developers! (Anecdotes:

@ai16zdao's Eliza default model is using XAI Grok)

If you need to handle multilingual content:

@Alibaba_Qwen's QwQ model performed very well in our tests, especially for Asian languages.

It should be noted that the training data of this model mainly comes from mainland China, so some content may contain missing information.

If you need a general purpose or strong inference model:

@OpenAI's model is still the best in the industry:

  • Stable and reliable performance

  • Extensive field testing

  • Has a strong security mechanism

This is a good starting point for most projects.

If you are a developer or content creator:

@AnthropicAI’s Claude is my go-to tool for daily use:

  • Excellent coding skills

  • The response is clear and detailed

  • Very suitable for creative work

Meta's Llama 3.3 has attracted much attention recently:

  • Stable and reliable performance

  • Open source model, flexible and free

  • Try it out via @OpenRouterAI or @GroqInc

For example, crypto x AI projects like @virtuals_io are developing products based on it.

If you need role-playing AI:

MythoMax 13B by @TheBlokeAI is currently the best in the roleplaying space, having topped relevant rankings for several months in a row.

Cohere's Command R+ is an excellent and underrated model:

Perform well in role-playing tasks

Able to handle complex tasks with ease

Supports context windows up to 128,000 long, with longer "memory"

Google's Gemma model is a lightweight but powerful option:

  • Focus on specific tasks and excel

  • Budget-friendly

  • Suitable for cost-sensitive projects

Personal experience: I often use small Gemma models as "unbiased referees" in AI pipelines, and they work great for validation tasks!

Gemma

@MistralAI’s model is worth mentioning:

  • Open source but high-end quality

  • Mixtral models are very powerful

  • Particularly good at complex reasoning tasks

It has been well received by the community and is definitely worth a try.

Cutting-edge AI in your hands.

Pro tip: try mixing and matching!

  • Different models have their own advantages

  • Can create AI “teams” for complex tasks

  • Let each model focus on what it does best

It’s like assembling a dream team, where each member has a unique role and contribution.

How to get started quickly:

Use @OpenRouterAI or @redpill_gpt to test your model. These platforms support cryptocurrency payments, which is very convenient.

An excellent tool for comparing the performance of different models

If you want to save costs and run models locally, you can try @ollama to experiment with your own GPU.

If you’re looking for speed, @GroqInc’s LPU technology provides extremely fast inference speeds:

  • Although the model selection is limited

  • But the performance is very suitable for deployment in production environments