Author: superoo7
Compiled by: Deep Tide TechFlow
I receive similar questions almost daily. After helping build over 20 AI agents and investing significant costs in testing models, I've summarized some truly effective experiences.
Here is a complete guide on how to choose the right LLM.
The field of large language models (LLM) is changing rapidly. New models are released almost every week, each claiming to be the 'best'.
But the reality is: no single model can meet all needs.
Each model has its specific applicable scenarios.
I've tested dozens of models, hoping that my experience can help you avoid unnecessary time and money waste.
It should be noted that this article is not based on laboratory benchmark tests or marketing promotions.
What I'll share is based on real experiences building AI agents and generative AI (GenAI) products over the past two years.
First, we need to understand what LLM is:
Large language models (LLM) are like teaching computers to 'speak human'. It predicts the next most likely word based on your input.
The starting point for this technology is this classic paper: Attention Is All You Need
Basic Knowledge - Closed Source vs Open Source LLM:
Closed source: for example, GPT-4 and Claude, typically pay-per-use, hosted and run by the provider.
Open source: for example, Meta's Llama and Mixtral, require users to deploy and run them themselves.
When first encountering these terms, it might be confusing, but understanding the differences is crucial.
Model scale does not equal better performance:
For example, 7B indicates that the model has 7 billion parameters.
But larger models do not always perform better. The key is to choose the model that fits your specific needs.
If you need to build an X/Twitter bot or social AI:
@xai's Grok is a very good choice:
Offers generous free quotas
Excels in understanding social contexts
Though it's closed source, it is definitely worth trying
Highly recommend this model for beginners! (Rumor has it:
@ai16zdao's Eliza default model is using XAI Grok)
If you need to handle multilingual content:
@Alibaba_Qwen's QwQ model performed exceptionally well in our tests, especially in Asian language processing.
It should be noted that the model's training data mainly comes from mainland China, so some content may be missing information.
If you need a model for general use or strong reasoning capabilities:
@OpenAI's model remains a leader in the industry:
Performance is stable and reliable
Extensively tested in real-world scenarios
Has strong security mechanisms
This is an ideal starting point for most projects.
If you are a developer or content creator:
@AnthropicAI's Claude is my main tool for daily use:
Coding capabilities are quite impressive
Response content is clear and detailed
Very suitable for handling creative-related work
Meta's Llama 3.3 has recently garnered a lot of attention:
Performance is stable and reliable
Open source model, flexible and free
Can be trialed through @OpenRouterAI or @GroqInc
For example, projects like @virtuals_io are developing products based on it.
If you need role-playing AI:
@TheBlokeAI's MythoMax 13B is currently a leader in the role-playing field, having ranked highly for several months.
Cohere's Command R+ is an underrated excellent model:
Excels in role-playing tasks
Easily handles complex tasks
Supports up to 128,000 context window with a longer 'memory capacity'
Google's Gemma model is a lightweight yet powerful option:
Focus on specific tasks and perform excellently
Budget-friendly
Suitable for cost-sensitive projects
Personal experience: I often use small Gemma models as 'unbiased judges' in AI workflows, and they perform exceptionally well in validation tasks!
Gemma
@MistralAI's model is worth mentioning:
Open source but high-end quality
The performance of the Mixtral model is very strong
Especially good at complex reasoning tasks
It has received widespread acclaim from the community and is definitely worth a try.
The cutting-edge AI in your hands.
Professional advice: try mixing and matching!
Different models have their own advantages
Can create AI 'teams' for complex tasks
Allows each model to focus on what it does best
It’s like building a dream team, where each member has a unique role and contribution.
How to get started quickly:
Use @OpenRouterAI or @redpill_gpt for model testing, these platforms support cryptocurrency payments, which is very convenient
is an excellent tool for comparing the performance of different models
If you want to save costs and run models locally, you can try using @ollama and experiment with your own GPU.
If you pursue speed, @GroqInc's LPU technology offers extremely fast inference speed:
Although the model selection is limited
its performance is well-suited for deployment in production environments
