I tested Haiku vs. Sonnet across 3 agent tasks – the cheap model won every time
A recent evaluation of LLM agents compared the performance of two models, Haiku and Sonnet, across various tasks. The results consistently favored the cheaper Haiku model, demonstrating its effectiveness in handling agent tasks. This evaluation utilized the agent-eval CLI toolkit to assess model performance and identify areas for improvement.
- ▪The agent-eval CLI toolkit was used to evaluate LLM agents.
- ▪The Haiku model outperformed the Sonnet model in all tested tasks.
- ▪The evaluation process included probing, reviewing failures, and analyzing dataset statistics.
Opening excerpt (first ~120 words) tap to expand
agent-eval CLI toolkit for evaluating LLM agents. Answers three questions: Where does my agent fail? → agent-eval probe Which model is best? → agent-eval compare (Phase 2) Did my change break anything? → agent-eval gate (Phase 3) Install pip install agt-eval Or for development: git clone https://github.com/aimvik07/agent-eval.git cd agent-eval pip install -e ".[dev]" Quick start # Run a probe against the demo golden dataset agent-eval probe examples/simple_eval.py # See probe history agent-eval history examples/simple_eval.py # Review failures interactively agent-eval golden examples/simple_eval.py --review # Show golden dataset statistics agent-eval golden examples/simple_eval.py --stats Writing a config # triage_eval.py from agent_eval import EvalConfig from my_agent.classifier import…
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.