Best Alternatives to Cekura Bench in 2025

VoiceBench is an open-source benchmark suite specifically designed for evaluating voice assistants and speech-to-speech models. It includes a diverse set of tasks such as spoken question answering, instruction following, and safety assessments. Unlike Cekura Bench, which focuses on live phone calls, VoiceBench emphasizes controlled, reproducible experiments with publicly available datasets and leaderboards. Its community-driven approach allows researchers to contribute new tasks and models, making it ideal for those who prioritize extensibility and academic rigor.

AudioBench offers a comprehensive evaluation framework for audio-language models, including speech-to-speech capabilities. It covers a wide range of tasks like audio captioning, speech translation, and emotion recognition, providing a multi-faceted view of model performance. While Cekura Bench specializes in phone call scenarios, AudioBench is broader in scope, making it suitable for teams developing general-purpose voice AI. Its detailed error analysis and per-task metrics help identify specific weaknesses, and the benchmark is regularly updated with new models and datasets.

SpeechBench focuses on benchmarking speech-to-speech models in real-time conversational settings, similar to Cekura Bench, but with a stronger emphasis on latency and interruption handling. It simulates natural dialogues with turn-taking, backchanneling, and overlapping speech, providing metrics like response latency, barge-in success rate, and conversational flow. SpeechBench is particularly useful for teams building interactive voice agents where seamless conversation is critical. Its synthetic yet realistic scenarios complement Cekura Bench's live call approach.

VoiceAgentBench is tailored for evaluating voice AI agents in customer service and task-oriented dialogue. It includes a suite of simulated phone calls covering common use cases like appointment scheduling, order tracking, and technical support. The benchmark measures task completion rate, user satisfaction (via simulated user feedback), and efficiency. While Cekura Bench uses real live calls, VoiceAgentBench offers scalable, repeatable testing with controlled variables, making it easier to compare models across identical scenarios. It also provides detailed transcripts and failure analysis.

RealTimeVoiceEval is a lightweight, open-source tool for benchmarking real-time voice models on latency, accuracy, and robustness. It supports both speech-to-text and speech-to-speech pipelines, with a focus on streaming performance. Unlike Cekura Bench, which ranks models on overall phone agent performance, RealTimeVoiceEval provides granular metrics such as time-to-first-token, word error rate under noise, and packet loss resilience. It's ideal for engineers who need to optimize low-level audio processing and network conditions.

OpenVoiceBench is a community-driven initiative that aggregates results from multiple voice AI benchmarks, including Cekura Bench, into a single leaderboard. It allows users to filter by task type, model, and metric, and provides reproducibility checks. While not a benchmark itself, OpenVoiceBench serves as a meta-alternative for those who want a holistic view of model performance across different evaluation suites. It also hosts discussions and model cards, fostering transparency and collaboration.