How to Compare Large Language Models: GPT-4 & 3.5 vs Anthropic Claude vs Cohere
Blog post from Activeloop
The blog post by Akash Sharma and Sinan Ozdemir explores Vellum's Playground, a solution for finding the right prompt/model mix for one's use case. They compare four leading LLMs from three top AI companies - OpenAI’s GPT-3.5 and GPT-4, Anthropic’s Claude, and Cohere’s Command series of models. The authors walk through four examples: Text Classification (detecting offensive language), Creative Content Generation with rules/personas, Question Answering and Logical Reasoning, and Code Generation. They consider three main metrics for performance/quality - Accuracy, Semantic Text Similarity, and Robustness. The goal is not to declare any of these models a “winner” but rather to help users think about judging the quality and performance of models in a more structured way using Vellum – a developer platform for building production LLM apps.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| LLM | 79 | 1,992 | 221 | 98 | +26% |
| Reinforcement learning | 5 | 96 | 10 | 9 | -38% |
| Real-time | 3 | 2,485 | 584 | 187 | +18% |
| Vector Search | 3 | 1,599 | 172 | 76 | +35% |
| AI Model Fine-tuning | 1 | 521 | 87 | 54 | +157% |
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