Claude 3.5 Haiku are you good bro? LLM Autocomplete Benchmark (Predictive Outputs)
🔥 CLAUDE 3.5 HAIKU IS SLOW AND OVERPRICED? BRO WHAT? SEE FOR YOURSELF! 🚀 OpenAI's Predicted Outputs ARE, ngl, SOLID for SPEED. 🚀 🎥 Featured Resources: - CODEBASE: https://github.com/disler/benchy - Claude 3.5 Haiku Announcement: https://www.anthropic.com/claude/haiku - OpenAI's Predicted Outputs Guide: https://platform.openai.com/docs/guides/predicted-outputs - OpenAI's Latency Optimization Tips: https://platform.openai.com/docs/guides/latency-optimization#use-predicted-outputs Are LLM benchmarks misleading you? 🤔 In this video, we dive deep into the performance of Claude 3.5 Haiku and OpenAI's GPT-4o with predicted outputs to uncover the truth about speed, price, and performance. 🕵️♂️ The developer experience around LLM benchmarks is often rough, with cherry-picked data that leaves out the real competition. 😤 We built a simple yet powerful autocomplete benchmark tool to test Claude 3.5 Haiku, GPT-4o with predicted outputs, and other models like Claude 3.5 Sonnet, Gemini Pro, and more. ⚙️ Discover how these models stack up in terms of speed, cost, and performance. Spoiler alert: some fast and cheap LLMs outperform the expensive ones! 😲 Created by indydevdan, this SIMPLE benchmark tool provides a real-world look into models from Google (Gemini), OpenAI and Anthropic, including Gemini Pro, Gemini Flash, GPT-4o, GPT-4o-mini, haiku, and sonnet. We put these Large Language Models (LLMs) to the test in an autocomplete benchmark, evaluating their real-world performance in single-word autocomplete tasks. ✍️ Whether you're interested in Claude 3.5 Haiku, Claude 3.5 Sonnet, or OpenAI's GPT-4o with predicted outputs, this video reveals surprising insights that could save you time and money. 💰 Is Claude 3.5 Haiku really worth the cost? Why is it slower than expected? What's the deal with OpenAI's predictive outputs? Is it useful? We answer these questions and more, providing you with actionable insights to choose the right LLM for your needs. 📊 If you're an AI developer, data scientist, or just an enthusiast in the LLM space, you won't want to miss this deep dive into benchmarks, LLM autocomplete performance, and the trade-offs between speed, price, and performance. 🧠 Don't rely on cherry-picked benchmarks! Watch now to see the real data and make informed decisions about which LLM is best for you. 🎯 Hit the like button and subscribe for more insights into the latest in AI and LLM technology. 🔔 Stay ahead of the curve with updates on models from Anthropic, OpenAI, and beyond. Keep building and stay focused. 📖 Chapters 00:00 Benchmarks, Claude 3.5 Haiku, Predicted Outputs 01:10 Autocomplete Benchmark 12:15 Claude 3.5 Haiku - Slow, Expensive, Mid? 18:01 Performance, Price, Speed and Local Models 20:15 Useful LLM Insights and Benchmarking Patterns #promptengineering #openai #anthropic
