LLM Model Comparison 2024
Table of Contents
Introduction
The landscape of large language models evolved significantly in 2024, with major releases from Anthropic, OpenAI, Google, Meta, Mistral, and Cohere. This document provides a comprehensive comparison of the leading models, their capabilities, and optimal use cases.
Model Overview and Specifications
| Provider | Model | Parameters | Context Window | Multimodal | Release Date |
|---|---|---|---|---|---|
| Anthropic | Claude 3 Opus | ~175B | 200K | Yes | Mar 2024 |
| Anthropic | Claude 3.5 | ~175B | 200K | Yes | Jun 2024 |
| OpenAI | GPT-4 | ~1.7T | 128K | Yes | 2023 |
| OpenAI | GPT-4o | ~1.7T | 128K | Yes | May 2024 |
| Gemini 1.5 Pro | Unknown | 2M | Yes | Feb 2024 | |
| Meta | Llama 3.1 405B | 405B | 128K | No | Jul 2024 |
| Meta | Llama 3.1 70B | 70B | 128K | No | Jul 2024 |
| Mistral | Mistral Large 2 | ~123B | 128K | No | Jul 2024 |
| Cohere | Command R+ | ~104B | 128K | No | Apr 2024 |
Detailed Model Analysis
Claude 3/3.5 (Anthropic)
Strengths
- Exceptional reasoning and analysis capabilities
- Strong performance on coding tasks with clear explanations
- Extended context window (200K tokens) enables comprehensive document analysis
- Constitutional AI training reduces harmful outputs
- Excellent at following complex instructions and maintaining conversation context
Weaknesses
- Limited availability compared to competitors
- Higher cost per token than some alternatives
- No fine-tuning options for custom applications
Optimal Use Cases
- Complex reasoning and analysis tasks
- Long-form content creation and editing
- Code generation and review
- Research and technical writing
- Legal and compliance document analysis
GPT-4/GPT-4o (OpenAI)
Strengths
- Excellent general-purpose performance across diverse tasks
- GPT-4o offers faster inference and lower cost
- Strong multimodal capabilities (text, image, audio)
- Wide ecosystem support and integrations
- Fine-tuning available for customization
Weaknesses
- Slower inference speed for GPT-4 (improved in 4o)
- Can be verbose and overconfident in responses
- Occasional hallucinations on factual queries
Optimal Use Cases
- General-purpose chatbots and assistants
- Creative writing and content generation
- Image analysis and generation tasks
- Educational applications
- Customer service automation
Gemini 1.5 (Google)
Strengths
- Massive 2M token context window enables analysis of entire codebases
- Strong multimodal capabilities including video understanding
- Competitive performance on benchmarks
- Integration with Google ecosystem
Weaknesses
- Availability limitations and API access constraints
- Less transparent about model architecture
- Fewer third-party integrations compared to competitors
Optimal Use Cases
- Large document analysis and summarization
- Video content analysis
- Codebase-wide refactoring and analysis
- Multi-document research synthesis
Llama 3.x (Meta)
Strengths
- Open-source with permissive licensing
- Multiple model sizes for different resource constraints
- Strong performance for open-source model
- Can be self-hosted for privacy and customization
- Active community and ecosystem
Weaknesses
- No native multimodal capabilities
- Requires infrastructure for deployment
- Limited official support compared to commercial offerings
- May require additional safety filtering
Optimal Use Cases
- Self-hosted deployments requiring data privacy
- Cost-sensitive applications with high volume
- Research and experimentation
- Custom fine-tuning for domain-specific tasks
- Reference: https://ollama.com/library
Mistral Models
Strengths
- Excellent performance-to-cost ratio
- Open-source options (Mistral 7B) alongside commercial offerings
- Strong European alternative to US providers
- Efficient inference and lower compute requirements
Weaknesses
- Smaller ecosystem than OpenAI or Anthropic
- Limited multimodal capabilities
- Less extensive documentation and examples
Optimal Use Cases
- European deployments with data sovereignty requirements
- Cost-optimized production deployments
- Multilingual applications (strong European language support)
- Edge deployment scenarios
Command R+ (Cohere)
Strengths
- Optimized for retrieval-augmented generation (RAG)
- Strong performance on enterprise search tasks
- Built-in citation and source tracking
- Competitive pricing for enterprise use
Weaknesses
- Less general-purpose capability than competitors
- Smaller community and ecosystem
- Limited multimodal features
Optimal Use Cases
- Enterprise search and knowledge management
- RAG-powered applications
- Customer support with source attribution
- Document retrieval and question answering
Performance Comparison
Coding Tasks
- Claude 3.5 (excellent explanation and debugging)
- GPT-4o (strong general coding)
- Gemini 1.5 (good for large codebases)
- Llama 3.1 70B (competitive for open-source)
- Mistral Large 2
Creative Writing
- GPT-4 (versatile and creative)
- Claude 3.5 (structured and analytical)
- Command R+ (factual, less creative)
- Gemini 1.5
- Llama 3.1
Reasoning and Analysis
- Claude 3 Opus (exceptional analytical depth)
- GPT-4 (strong general reasoning)
- Gemini 1.5 Pro
- Mistral Large 2
- Llama 3.1 405B
Cost Efficiency
- Llama 3.1 (self-hosted)
- Mistral models
- GPT-4o (vs GPT-4)
- Command R+
- Claude 3.5
Conclusion
The choice of LLM depends heavily on specific use case requirements:
- For maximum reasoning capability and code quality: Claude 3.5
- For general-purpose applications with broad ecosystem: GPT-4o
- For analyzing large documents or codebases: Gemini 1.5 Pro
- For self-hosted, privacy-focused deployments: Llama 3.1
- For cost-optimized European deployments: Mistral Large 2
- For enterprise RAG applications: Command R+
As the field continues to evolve rapidly, staying informed about model updates and new releases is essential for optimal model selection.