Mistral Small, a model priced at $0.15 per million input tokens, demonstrated a significant issue with function calling, returning empty replies for 19.3% of tasks even after multiple retries. This resulted in a higher cost per successful task ($0.000098) compared to GPT-6 Luna ($0.000051), which passed 93.0% of tasks without retries. The analysis revealed that Mistral Small's retries accounted for 23.9% of its spending, and at least three in four of these retries remained unreadable, indicating a specific behavioral problem with this model rather than a general difficulty with function calling for cheaper models. AI
IMPACT Highlights potential reliability and cost issues with specific LLMs for function-calling tasks, impacting developer choices.
RANK_REASON Analysis of a specific model's performance on a particular task (function calling) and its cost implications.
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