If you’re building AI agents today, you’re not choosing an LLM. You’re choosing the right LLM for a specific job. And that’s a much harder problem. That’s why we built the Algolia LLM Leaderboard: https://lnkd.in/ex_x8mBy It evaluates models inside real search and shopping agents using live data, not abstract benchmarks. The takeaway. Rankings can be misleading. The right choice depends on your specific task and constraints. See which models make the cut in the latest Hashing It Out: AI Newsletter ⤵️
The point that the top benchmark model is often the wrong choice for agents due to cost and latency is a valuable reminder when choosing LLMs.
This is exactly the kind of question we want to explore on April 18. We’ll be testing models on real-world data workloads with Polars, and focusing on what actually matters in production: code correctness, system efficiency, and optimization. The interesting part is not just “which model is best?” It’s “which model is best for this job, under these constraints?”: https://paris.aitinkerers.org/p/hackathon-benchmarking-small-language-models-in-the-real-world