Best LLMs For Text Classification: Overview & Costs
Best LLMs For Text Classification: Overview & Costs
November 22, 2024•Hugo Huijer
As someone who's spent countless hours researching and comparing different LLMs, I wanted to create a straightforward guide to help you choose the right model for text classification. While I haven't personally tested all these models (let's be transparent here!), I've gathered reliable data from official sources to give you a solid overview of what's available.
What are the best LLMs for Text Classification?
Text classification is one of those tasks where you really want to balance accuracy with cost - after all, you might be processing thousands or even millions of documents. I've selected five LLMs that stand out in 2024, each with its own strengths and particular use cases.
| Model | Context Window | Cost (Input/Output) | Best For |
|---|---|---|---|
| Claude-3.5 Sonnet | 200K tokens | $3/$15 per 1M tokens | Enterprise & High-accuracy needs |
| Gemini-1.5-pro-preview | 1M tokens | $0.08/$0.31 per 1M tokens | Large-scale processing |
| Command-r | 128K tokens | $0.15/$0.6 per 1M tokens | Specialized classification tasks |
| Open-mistral-nemo | 128K tokens | $0.3/$0.3 per 1M tokens | Development & testing |
| GPT-4o-mini | 128K tokens | $0.15/$0.6 per 1M tokens | Balanced performance |
Anthropic - Claude-3.5 Sonnet
Think of Claude-3.5 Sonnet as the premium sedan of LLMs - it's not the most expensive option out there, but it delivers exceptional quality consistently. What makes it particularly good for text classification is its robust understanding of context and nuance.
| Context Window | 200K tokens |
|---|---|
| Input Cost | $3 per 1M tokens |
| Output Cost | $15 per 1M tokens |
| Best Use Case | Enterprise-grade classification |
Google - Gemini-1.5-pro-preview
Here's what caught my eye about Gemini: it offers an absolutely massive context window at a surprisingly affordable price point. If you're dealing with long documents or need to process lots of text at once, this could be your best bet.
| Context Window | 1M tokens |
|---|---|
| Input Cost | $0.08 per 1M tokens |
| Output Cost | $0.31 per 1M tokens |
| Best Use Case | Large-scale document processing |
Cohere - Command-r
Cohere's Command-r is like that specialized tool in your toolkit - it's specifically optimized for tasks like text classification. While some models try to be good at everything, Command-r knows its strength and plays to it well.
| Context Window | 128K tokens |
|---|---|
| Input Cost | $0.15 per 1M tokens |
| Output Cost | $0.6 per 1M tokens |
| Best Use Case | Focused classification tasks |
Mistral - Open-mistral-nemo
Open-mistral-nemo is the developer's friend - it offers consistent pricing (same cost for input and output) and reliable performance. It's perfect when you're building and testing classification systems.
| Context Window | 128K tokens |
|---|---|
| Input Cost | $0.3 per 1M tokens |
| Output Cost | $0.3 per 1M tokens |
| Best Use Case | Development and iteration |
OpenAI - GPT-4o-mini
GPT-4o-mini strikes a nice balance between capability and cost. It's like getting the reliability of OpenAI's technology but in a more affordable package.
| Context Window | 128K tokens |
|---|---|
| Input Cost | $0.15 per 1M tokens |
| Output Cost | $0.6 per 1M tokens |
| Best Use Case | Balanced performance needs |
Remember, choosing the right LLM for text classification isn't just about picking the cheapest or the most powerful option. It's about finding the right fit for your specific needs. Consider factors like:
- How many documents you'll be processing
- Your accuracy requirements
- Your budget constraints
- The length of texts you're classifying
Each of these models has its sweet spot, and I hope this overview helps you find the right one for your project. If you're just starting out, I'd recommend trying Gemini-1.5-pro-preview or Open-mistral-nemo first - they offer great value for experimentation. For production use, Claude-3.5 Sonnet or Command-r might be more appropriate.