๐กPrompt engineering doesnโt have to feel like an endless game of whack-a-mole.
Tweak โ test โ review โ repeat โ until you run out of time or patience.
Weโve built something to change that๐
๐ Our Agent Leaderboard is ๐น๐ถ๐๐ฒ! We built a comprehensive benchmark of which LLMs work best for AI Agents ๐
After evaluating 17 leading LLMs across 14 diverse datasets, we're excited to share our findings about which models truly excel at tool-callingโand are ready to power AI agents to solve ๐ณ๐ฆ๐ข๐ญ-๐ธ๐ฐ๐ณ๐ญ๐ฅ ๐ฑ๐ณ๐ฐ๐ฃ๐ญ๐ฆ๐ฎ๐ด effectively.
Key discoveries:
๐ @Google's ๐๐ฒ๐บ๐ถ๐ป๐ถ-๐ฎ.๐ฌ-๐ณ๐น๐ฎ๐๐ต ๐ฑ๐ผ๐บ๐ถ๐ป๐ฎ๐๐ฒ๐ with a 0.938 score at remarkably low cost
๐ธ The top 3 models span a 10๐น ๐ฑ๐ณ๐ช๐ค๐ฆ ๐ฅ๐ช๐ง๐ง๐ฆ๐ณ๐ฆ๐ฏ๐ค๐ฆ with only 4% performance gap: ๐๐ผ๐บ๐ฒ ๐ผ๐ณ ๐๐ผ๐ ๐ฎ๐ฟ๐ฒ ๐ผ๐๐ฒ๐ฟ๐ฝ๐ฎ๐๐ถ๐ป๐ด!
๐ @MistralAI's Mistral-small-2501 ๐น๐ฒ๐ฎ๐ฑ๐ ๐ผ๐ฝ๐ฒ๐ป-๐๐ผ๐๐ฟ๐ฐ๐ฒ options, matching GPT-4o-mini at 0.832
โ ๐ฆ๐๐ฟ๐ฝ๐ฟ๐ถ๐๐ฒ ๐ณ๐ฎ๐ถ๐น๐๐ฟ๐ฒ: @deepseek_ai V3 and R1 didn't make the rankings due to limited function calling supportโmaking them ineffective for enabling AI agents to leverage tools
Get more insights, dive into the full analysis and explore the interactive leaderboard on @huggingface: https://t.co/WlYwpZKO6a
Which LLM are you using for your AI agents? Are you getting the best value for your spend? ๐ค
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