Model comparisons
QuantaLogic Model comparison
relevant information to pick the best model for you use case ( Chat / Prompt / Workflow )
QuantaLogic Model comparison
Training Data
Here's a table summarizing the training data for the specified models:
| Model | Training Data |
|---|---|
| GPT-4o-mini | - Internet data up to October 2023 - Books, articles, scientific papers - Diverse multilingual content |
| Claude 3 Haiku (20241022) | - Internet data up to July 2024 - Public labeling services data - Synthetic data generated internally |
| Claude 3.5 Sonnet | - Similar to Claude 3 Haiku, but with more extensive datasets - Enhanced focus on complex reasoning tasks |
| Mistral Large 2 (24.11) | - Extensive multilingual internet data - Scientific papers and technical documents - Code repositories |
| Mistral NeMo | - Large-scale multilingual datasets - Specialized data for generalist tasks |
| Codestral 25.01 | - Extensive code repositories - Programming documentation - Technical papers and discussions |
Strengths and Weaknesses
Here's a table outlining the strengths and weaknesses of each model:
| Model | Strengths | Weaknesses |
|---|---|---|
| GPT-4o-mini | - Excellent performance in benchmarks - Strong in math and coding - Cost-effective | - Smaller context window compared to some competitors |
| Claude 3 Haiku (20241022) | - Fast processing speed - Optimized for rapid interactions - Cost-effective for high-volume tasks | - Less powerful than Sonnet for complex reasoning |
| Claude 3.5 Sonnet | - Superior performance in complex reasoning - Advanced tool use capabilities - Excellent for multistep coding tasks | - Higher cost compared to Haiku |
| Mistral Large 2 (24.11) | - Top-tier performance across tasks - Excellent multilingual capabilities - Strong in math and coding | - Higher cost compared to smaller models |
| Mistral NeMo | - Large context window - Optimized for processing large volumes of data - Balanced performance across tasks | - Less specialized than some task-specific models |
| Codestral 25.01 | - Specialized in code generation and completion - Supports over 80 programming languages - Large context window for analyzing extensive codebases | - May be less versatile for non-coding tasks |
Costs, Context Sizes, and Context Windows
Here's a table comparing the costs for 1 Million input and output tokens, along with context sizes and context windows:
| Model | Input Cost (per 1M tokens) | Output Cost (per 1M tokens) | Context Size (tokens) | Context Window (tokens) |
|---|---|---|---|---|
| GPT-4o-mini | $0.15 | $0.60 | 128K | 128K |
| Claude 3 Haiku (20241022) | $0.25 | $1.25 | 200K | 200K |
| Claude 3.5 Sonnet | $3.00 | $15.00 | 200K | 200K |
| Mistral Large 2 (24.11) | $5.00 | $20.00 | 128K | 128K |
| Mistral NeMo | $2.00 | $2.00 | 128K | 128K |
| Codestral 25.01 | $1.00 | $3.00 | 256K | 256K |