Introducing... Agent Leaderboard!
Many devs ask me which LLMs work best for AI agents.
The new Agent Leaderboard (by @rungalileo) was built to provide insights and evaluate LLMs on real-world tool-calling tasks—crucial for building AI agents.
Let's go over the results:
1️⃣ Leader
After evaluating 17 leading LLMs across 14 diverse datasets, here are the key findings:
Google's 𝗚𝗲𝗺𝗶𝗻𝗶-𝟮.𝟬-𝗳𝗹𝗮𝘀𝗵 leads with a 0.94 score at a remarkably low cost.
After evaluating 17 leading LLMs across 14 diverse datasets, here are the key findings:
Google's 𝗚𝗲𝗺𝗶𝗻𝗶-𝟮.𝟬-𝗳𝗹𝗮𝘀𝗵 leads with a 0.94 score at a remarkably low cost.
2️⃣ Pricing
The top 3 models span a 10x price difference with only 4% performance gap. Many of you might be overpaying.
The top 3 models span a 10x price difference with only 4% performance gap. Many of you might be overpaying.
3️⃣ Open-source
Mistral AI's mistral-small-2501 leads open-source options, matching GPT-4o-mini at 0.83. Smaller models tuned for tool calling have a lot of potential.
Mistral AI's mistral-small-2501 leads open-source options, matching GPT-4o-mini at 0.83. Smaller models tuned for tool calling have a lot of potential.
4️⃣ Reasoning models
While reasoning models like o1 and o3-mini demonstrated excellent integration with function calling capabilities, DeepSeek-R1 didn't make the rankings as it doesn't support native function calling (yet).
While reasoning models like o1 and o3-mini demonstrated excellent integration with function calling capabilities, DeepSeek-R1 didn't make the rankings as it doesn't support native function calling (yet).
5️⃣ Edge cases
Claude-sonnet achieves standout performance in tool miss detection (0.92). In general, current models still struggle with edge cases.
Claude-sonnet achieves standout performance in tool miss detection (0.92). In general, current models still struggle with edge cases.
6️⃣ Architecture trade-offs
Long context vs. parallel execution shows architectural limits: o1 leads long context (0.98) but fails parallel tasks (0.43), while GPT-4o shows the opposite pattern.
More results are in the links below:
Long context vs. parallel execution shows architectural limits: o1 leads long context (0.98) but fails parallel tasks (0.43), while GPT-4o shows the opposite pattern.
More results are in the links below:
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