Models great at coding aren't always great at voice Meet Voice Standard Bench (www.assemblyai.com)

Voice Standard Bench has introduced a new platform that evaluates language models (LLMs) on their ability to perform voice tasks, addressing a critical gap in AI testing. This benchmarking tool allows developers to quickly assess the performance of various models, such as Opus 5 and GPT-5.6 Luna, based on real-world voice tasks and transcripts. It provides a score from 0 to 100, showcasing each model's effectiveness in terms of price per hour of audio, median response time, and overall performance metrics. For example, Opus 5 scored 93, with a cost of $0.465 per hour of audio and a median response time of 5.02 seconds, while Qwen 3.5 4B Fast scored lower at 64 but offered a much cheaper cost of $0.005. This development is significant for the AI/ML community as it emphasizes the need for specialized tools that highlight the particular strengths and weaknesses of LLMs when applied to voice and transcription tasks. By providing insights into cost-efficiency and responsiveness alongside performance, Voice Standard Bench serves as an invaluable resource for developers looking to integrate voice into their products. With over 50 models available through a single API key and easy comparison features, users can make informed decisions tailored to their specific needs without requiring extensive coding expertise.