Benchmarking Cheap LLMs for Production Agent Traces

This story was originally published on HackerNoon at: https://hackernoon.com/benchmarking-cheap-llms-for-production-agent-traces. One LLM call runs on every agent trace we ingest. We found a model 30× cheaper that holds quality, verified against real search traffic. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #llms, #ai-agent, #llm-observability, #llmops, #ai-cost-optimization, #model-evaluation, #ai-agent-observability, #hackernoon-top-story, and more. This story was written by: @TomaRares_1amot838. Learn more about this writer by checking @TomaRares_1amot838's about page, and for more stories, please visit hackernoon.com. Every agent trace we ingest gets reduced to a short, searchable digest by one LLM call, and because it runs on every trace it's our most frequent call. We tested four cheaper models against Sonnet 4.6 on a frozen set of 50 traces, grading not just the summaries but whether semantic search still returned the same traces. gpt-4o-mini held all but one rule at about a thirtieth of Sonnet's cost, so that's what we shipped. The two cheapest-looking models, Gemini 2.5 Flash-Lite and Kimi K2.5, both failed.