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Product Positioning & Context
Cekura Bench publishes voice AI benchmarks you can verify. Our new speech-to-speech benchmark tests 9 realtime voice models, including GPT Realtime 2.1, Gemini Live, Grok and Phonic, as complete phone agents on live calls: 82 scenarios, three runs each. Models are ranked on reliability, data accuracy, stalled calls, response time and cost, and every call transcript is public. Cekura Bench also covers voice agent benchmarks and STT benchmarks, with TTS benchmarks coming soon.
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Deep-Dive FAQs
What is Cekura Bench?
Cekura Bench is a digital product or tool described as: Speech-to-speech model benchmarks on live phone calls
Where did Cekura Bench originate?
Data for Cekura Bench was aggregated directly from the Product Hunt community ecosystem, representing raw developer and early-adopter sentiment.
When was Cekura Bench publicly launched?
The initial public indexing or launch date for Cekura Bench within our tracked developer communities was recorded on October 8, 2026.
How popular is Cekura Bench?
Cekura Bench has achieved measurable traction, logging over 143 traction score and facilitating 17 recorded discussions or engagements.
Which technical categories define Cekura Bench?
Based on metadata extraction, Cekura Bench is categorized under topics such as: SaaS, Developer Tools, Artificial Intelligence.
What are some commercial alternatives to Cekura Bench?
Our semantic intelligence engine identifies potential commercial alternatives in the SaaS space, such as Cekura, which offers overlapping value propositions.
How does the creator describe Cekura Bench?
The original author or development team describes the product as follows: "Cekura Bench publishes voice AI benchmarks you can verify. Our new speech-to-speech benchmark tests 9 realtime voice models, including GPT Realtime 2.1, Gemini Live, Grok and Phonic, as complete ph..."
Community Voice & Feedback
Always glad to see more benchmarks for voice AI!
Really comprehensive benchmark, Voice ai companies should definitely check this out 🚀🚀
Voice AI is moving fast, and having an independent way to compare different AI providers on real phone calls is incredibly useful. Really cool work, team!
Happy to see it live 🚀
Cool stuff!
I like that they’re publishing the actual call transcripts with the scores. Way easier to see what went wrong instead of just taking the numbers at face value.
Choosing a speech-to-speech model today can be difficult, with new models launching constantly and limited ways to compare them fairly. These results provides transparent, verifiable results to help teams evaluate real-world performance and choose with confidence. 🎯
Publishing the call transcripts alongside the scores is the part I like here. Being able to look at what actually happened on a failed call makes the ranking much easier to judge. Congrats to the team!
Love it! Congrats on the launch team
Really exciting to see this live! The fact that these models were tested on real phone calls with the same agent, prompts, tools, and scenarios makes the benchmark especially interesting. Looking forward to seeing how the results evolve as more models are added 🚀
Finally, a serious benchmark for speech-to-speech models. No cherry-picked demos. Same agent, same prompt, same tools, and real end-to-end scenarios.Excited to see what the community does with it. 🚀
Exciting to actually quantify the explosive growth of voice AI 🚀
Every speech-to-speech model sounds great in a demo, so we put them all through the same real phone calls with real tool use and scored what actually happened. Excited to see how the rankings shift as new models drop 🚀
Hi everyone, I am Sidhant from Cekura.
Today we are launching our speech-to-speech benchmark. We ran 9 realtime voice models as the full agent inside the same open-source Pipecat phone agent, with the same prompt, tools and phone line. Each model handled 82 scenarios across a clinic booking flow and a Medicare intake, three times each. A call passes only if the agent reaches the right outcome and saves every field correctly.
Results:
Most reliable: GPT Realtime 2.1, 79.3% of scenarios passed on all three runs
Fastest: Phonic v1, 1.60s median response
Lowest cost: GPT Realtime 2.1 Mini, $0.020 per minute
A cascade baseline (Flux, GPT-4.1, ElevenLabs Flash) scored 82.9%, above every realtime model
Every call links to its transcript, tool calls and scores, and the agent and test cases are on GitHub.
We would value your feedback, especially on which models we should add next.
Today we are launching our speech-to-speech benchmark. We ran 9 realtime voice models as the full agent inside the same open-source Pipecat phone agent, with the same prompt, tools and phone line. Each model handled 82 scenarios across a clinic booking flow and a Medicare intake, three times each. A call passes only if the agent reaches the right outcome and saves every field correctly.
Results:
Most reliable: GPT Realtime 2.1, 79.3% of scenarios passed on all three runs
Fastest: Phonic v1, 1.60s median response
Lowest cost: GPT Realtime 2.1 Mini, $0.020 per minute
A cascade baseline (Flux, GPT-4.1, ElevenLabs Flash) scored 82.9%, above every realtime model
Every call links to its transcript, tool calls and scores, and the agent and test cases are on GitHub.
We would value your feedback, especially on which models we should add next.
Discovery Source
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