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Palabra.ai takes #1 spot for speed in new text-to-speech (TTS) benchmark. PALABRA.AI LTD Jul 31, 2026, 10:41 ET Palabra ranks #1 for latency on Coval's independent, open-source text-to-speech benchmark, posting 104 milliseconds - roughly twice as fast as the nearest competitor - with a 6% word error rate. Coval's independent benchmark puts Palabra's text-to-speech model at 104 ms latency - about twice as fast as the next-closest competitors. LONDON, July 31, 2026 /PRNewswire-PRWeb/ - Palabra, a real-time speech AI company, has ranked #1 for latency on Coval's independent TTS benchmark, a widely watched, continuously updated leaderboard tracking how leading voice AI models perform under real-world conditions. Palabra posted 104 milliseconds of latency and a 6% word error rate (WER), outpacing ElevenLabs, Cartesia, and other major players in the space - roughly twice as fast as the nearest competitor. Latency is one of the key factors determining whether a voice AI system can support a fluid, real-time conversation. Even when the generated voice itself sounds highly natural, a noticeable delay before each response can disrupt the flow of dialogue, creating awkward pauses, causing people to talk over each other, and making the interaction feel less immediate. Many voice AI systems still operate at around 500 milliseconds of latency or more. Palabra's model achieves 104 milliseconds in Coval's independent benchmark, significantly reducing the delay between turns and enabling a more fluid, uninterrupted dialogue. Coval's benchmark is built to reflect production reality rather than idealized lab conditions: it measures Time to First Audio (TTFA) - the delay a listener actually notices, including any silence before the first audible sample - using pinned, versioned datasets so every provider is scored against identical inputs. Word error rate is calculated by transcribing each provider's synthesized audio with a fixed ASR model and scoring it against the original text, so the number reflects whether the speech is intelligible, not just fast. The full runner and methodology are open-source and independently reproducible. "Latency is one of the most consequential metrics in voice AI," said Brooke Hopkins, Founder and CEO of Coval. "Humans respond in around 400 milliseconds, and systems slower than that feel unnatural regardless of output quality. What makes Palabra's result significant is what it creates downstream: when a model is this fast, every other component in the stack gets more time to think." Palabra says the result reflects a new TTS architecture that achieves 35ms of time-to-first-audio before network overhead, paired with production infrastructure built to hold that speed at scale across large volumes of concurrent users. "The biggest unsolved challenge that the world's leading speech AI labs are working on today is making interactions with AI voice agents feel indistinguishable from conversations with another person," said Artem Kukharenko, CEO & Co-founder of Palabra Al. "Latency above 200 milliseconds introduces a noticeable delay that disrupts the natural flow of communication. That's why we've focused on building TTS with extremely low time-to-first-audio that could work at large scale in production systems. In Coval's independent benchmark, we rank #1 at 104ms - the closest competitor is approximately twice as slow." The TTS result is part of a broader focus at Palabra on real-time speech models - spanning text-to-speech, speech recognition (ASR), and speech-to-speech translation - built around low latency as a core design principle across the stack. About Palabra Palabra is a voice AI lab developing real-time models for text-to-speech (TTS), automatic speech recognition (ASR), and speech-to-speech translation. Its proprietary models and APIs enable developers and enterprises to build low-latency multilingual voice experiences across customer support, conferencing, live streaming, education, and other real-time applications. Palabra recently raised $8.4 million in pre-seed funding led by Seven Seven Six (776) with participation from Creator Ventures, and prominent angel investors including Max Mullen, co-founder of Instacart; Anne Lee Skates, former partner at Andreessen Horowitz; Mehdi Ghissassi,, former Head of Product at DeepMind; and Namat Bahram, an early backer of ElevenLabs. About Coval Coval is the San Francisco-based evaluation platform for voice AI, founded by ex-Waymo engineer Brooke Hopkins. It builds independent, continuously updated, open-source benchmarks that measure how voice AI systems perform in production, rather than in idealized lab conditions. Coval recently raised a $28M Series A led by Norwest, with Twilio Ventures and Y Combinator also on the cap table, and is already trusted by over 60 organizations, including Zoom and Deepgram. Its public TTS leaderboard is available at benchmarks.coval.ai/tts. Media Contact Dmytro Tymoshenko, PALABRA.AI LTD, 380 939944325, [email protected], palabra.ai SOURCE PALABRA.AI LTD

Palabra.ai takes #1 spot for speed in new text-to-speech (TTS) benchmark. Business · JUL 31, 2026 PR Newswire Palabra ranks #1 for latency on Coval's independent, open-source text-to-speech benchmark, posting 104 milliseconds - roughly twice as fast as the nearest... Palabra ranks #1 for latency on Coval's independent, open-source text-to-speech benchmark, posting 104 milliseconds - roughly twice as fast as the nearest competitor - with a 6% word error rate. Coval's independent benchmark puts Palabra's text-to-speech model at 104 ms latency - about twice as fast as the next-closest competitors. LONDON, July 31, 2026 /PRNewswire-PRWeb/ - Palabra, a real-time speech AI company, has ranked #1 for latency on Coval's independent TTS benchmark, a widely watched, continuously updated leaderboard tracking how leading voice AI models perform under real-world conditions. Palabra posted 104 milliseconds of latency and a 6% word error rate (WER), outpacing ElevenLabs, Cartesia, and other major players in the space - roughly twice as fast as the nearest competitor. The hardest problem in speech AI is making voice agents feel indistinguishable from a real person. Latency above 200 milliseconds breaks that. We built our TTS for extremely low time-to-first-audio at production scale. Coval's benchmark ranks us #1 at 104 ms, nearly twice as fast as the next model. Latency is one of the key factors determining whether a voice AI system can support a fluid, real-time conversation. Even when the generated voice itself sounds highly natural, a noticeable delay before each response can disrupt the flow of dialogue, creating awkward pauses, causing people to talk over each other, and making the interaction feel less immediate. Many voice AI systems still operate at around 500 milliseconds of latency or more. Palabra's model achieves 104 milliseconds in Coval's independent benchmark, significantly reducing the delay between turns and enabling a more fluid, uninterrupted dialogue. Coval's benchmark is built to reflect production reality rather than idealized lab conditions: it measures Time to First Audio (TTFA) - the delay a listener actually notices, including any silence before the first audible sample - using pinned, versioned datasets so every provider is scored against identical inputs. Word error rate is calculated by transcribing each provider's synthesized audio with a fixed ASR model and scoring it against the original text, so the number reflects whether the speech is intelligible, not just fast. The full runner and methodology are open-source and independently reproducible. "Latency is one of the most consequential metrics in voice AI," said Brooke Hopkins, Founder and CEO of Coval. "Humans respond in around 400 milliseconds, and systems slower than that feel unnatural regardless of output quality. What makes Palabra's result significant is what it creates downstream: when a model is this fast, every other component in the stack gets more time to think." Palabra says the result reflects a new TTS architecture that achieves 35ms of time-to-first-audio before network overhead, paired with production infrastructure built to hold that speed at scale across large volumes of concurrent users. "The biggest unsolved challenge that the world's leading speech AI labs are working on today is making interactions with AI voice agents feel indistinguishable from conversations with another person," said Artem Kukharenko, CEO & Co-founder of Palabra Al. "Latency above 200 milliseconds introduces a noticeable delay that disrupts the natural flow of communication. That's why we've focused on building TTS with extremely low time-to-first-audio that could work at large scale in production systems. In Coval's independent benchmark, we rank #1 at 104ms - the closest competitor is approximately twice as slow." The TTS result is part of a broader focus at Palabra on real-time speech models - spanning text-to-speech, speech recognition (ASR), and speech-to-speech translation - built around low latency as a core design principle across the stack. About Palabra Palabra is a voice AI lab developing real-time models for text-to-speech (TTS), automatic speech recognition (ASR), and speech-to-speech translation. Its proprietary models and APIs enable developers and enterprises to build low-latency multilingual voice experiences across customer support, conferencing, live streaming, education, and other real-time applications. Palabra recently raised $8.4 million in pre-seed funding led by Seven Seven Six (776) with participation from Creator Ventures, and prominent angel investors including Max Mullen, co-founder of Instacart; Anne Lee Skates, former partner at Andreessen Horowitz; Mehdi Ghissassi,, former Head of Product at DeepMind; and Namat Bahram, an early backer of ElevenLabs. About Coval Coval is the San Francisco-based evaluation platform for voice AI, founded by ex-Waymo engineer Brooke Hopkins. It builds independent, continuously updated, open-source benchmarks that measure how voice AI systems perform in production, rather than in idealized lab conditions. Coval recently raised a $28M Series A led by Norwest, with Twilio Ventures and Y Combinator also on the cap table, and is already trusted by over 60 organizations, including Zoom and Deepgram. Its public TTS leaderboard is available at benchmarks.coval.ai/tts. Media Contact Dmytro Tymoshenko, PALABRA.AI LTD, 380 939944325, [email protected], palabra.ai SOURCE PALABRA.AI LTD Published by News Desk · WeeklyReviewer The WeeklyReviewer news desk monitors breaking developments around the clock - sourcing, verifying, and publishing real-time industry news across business, technology, politics, science, sports, and world affairs. Every story that comes through the Live Wire is reviewed for accuracy before publication, keeping our readers ahead of the curve without the noise.

Announcing Baseten for Model Labs. Baseten is excited to launch a new platform designed specifically for model labs. Last updated. July 29, 2026 Baseten is excited to launch Baseten for Model Labs: a set of products and services designed to help closed-weight model labs distribute and monetize their models quickly and easily. Baseten for Model Labs is the central hub for model consumers to access models and for model builders to distribute them. On May 6, Baseten introduced the Frontier Gateway, a managed inference gateway built to give closed-weight model labs a way to serve their model in production using a white-labeled API. Since then, many labs like Poolside, Subconscious, Trajectory, and WRITER have successfully leveraged the gateway to monetize their models. As these labs gained traction with developers, Baseten quickly saw a different need emerge. Developers wanted access to these specialized and powerful models, but they didn't want to sign up for a new API or onboard another sub-processor to use a new model in production. They wanted to access it from their preferred inference platform easily. At the same time, its lab partners saw the opportunity to list their models on the Baseten Model Library as a compelling new distribution channel beyond the white-labeled API Baseten provide them with the Frontier Gateway. Seeing the strong demand from both sides is what led Baseten to collaborate with many labs to build a platform that addresses the needs of both model labs and their downstream customers, which Baseten is excited to launch today. A new distribution platform for closed models. With Baseten for Model Labs, Baseten is expanding the services Baseten offer labs with a new set of capabilities built to help them easily monetize and scale. Baseten built a distribution platform for labs that want to bring their model to production but don't want to stand up the technical and operational infrastructure to do so: * Inference infrastructure built for production out of the box: Model labs don't need to spend months building billing, remittance, API keys, compliance authentication, authorization, compute procurement, regional support and more, to bring their model to production. Baseten handles the infrastructure and operational complexity. * Increased visibility with a highly qualified developer audience: Baseten is well-known by AI developers around the world as the fastest, most reliable, and scalable inference provider. Labs benefit from increased visibility with Baseten's growing developer community and enterprise customers, expanding awareness among highly qualified AI practitioners. * Strong IP protection for labs' closed-weights: Lab model weights available through the Model Library are protected by its strict distribution agreement and secure infrastructure, so end-customers can only consume the model and can't modify it or extract its weights. * GTM Support: When a model is published on the Model Library, model labs join its growing ecosystem and benefit from an awareness boost through joint marketing and co-selling engagements with Baseten's sales team when their model is a strong fit for a customer's need. In short, model labs can get a direct line to monetize their model without having to build the infrastructure, compliance, and GTM motion themselves. Labs that are already using Baseten for Labs. Baseten is proud to launch alongside 15 lab partners and many users who are already leveraging these models for a wide range of use cases: * Cartesia builds state-space models (SSMs) for real-time voice intelligence, featuring Sonic (TTS) and Ink (STT). Replacing traditional transformers for hyper-efficiency, it delivers sub-90ms latency across cloud, edge, or on-premise deployments. Its top use cases include real-time conversational AI agents for customer support, sales outreach, marketing, training simulations, and recruiting. * Gradium builds real-time speech models: TTS, STT, voice cloning, and voice design. Streaming-native, built for voice agents, with latency low enough for natural turn-taking and reliable pronunciation on alphanumerics. The voice library is tuned for agent use, with cloning and voice design for teams that need their own accents, pitches, and tones. Spun out of the Kyutai research lab in 2025, from the team behind Moshi. * Inception is the creator of Mercury 2, a new class of diffusion LLMs that match the quality of frontier speed-optimized models, with sub-250 ms time to first token, 1,000+ tokens per second throughput, and 70% lower cost per task. Served on Baseten, top use cases include real-time voice applications, search and retrieval pipelines, and AI coding sub-agents (e.g., context compaction). * NVIDIA provides open models such as Nemotron ASR and BioNeMo under permissive licenses for commercial use. For customers who want production-ready deployments, NVIDIA NIM packages optimized inference runtimes, standard APIs, and enterprise-grade operational capabilities. Through its distribution agreement with NVIDIA, Baseten makes it easy to deploy NIM on its inference platform while also respecting NVIDIA's commercial license. * PyannoteAI is the creator of Pyannote, a speaker intelligence foundation model for audio diarization. It can identify who spoke, when, and how, even in complex audio, and can pair with any STT model to turn transcripts into clean, speaker-attributed metadata. With centisecond boundary precision, Pyannote delivers 300ms real-time diarization latency, whether in cloud, on-premise, or at the edge, and up to 52% lower transcription errors. Top use cases include meeting transcription, healthcare AI scribing, financial compliance, and media dubbing. * SID.ai is the creator of SID-1, a specialized search LLM that excels at multi-step agentic retrieval across unstructured datasets. SID-1 surfaces ~2x more relevant documents than embedding search without requiring reindexing. Providing agentic search at ~100x lower cost and ~20x lower latency than frontier models, it powers high-recall retrieval for legal, healthcare, customer support, general knowledge, and more. * Subconscious builds specialized inference systems for long-horizon AI agents. Using near-lossless message compression and efficient prefix/suffix caching, it expands stateful context windows by 10x, increases throughput by 3.5x, and reduces token consumption by up to 50% without sacrificing accuracy. Serving its TIM models alongside open-weight options, top use cases include coding, workflow, and browser automation, research, and legal document generation. * Synthefy's Nori is a tabular foundation model that outperforms XGBoost in accuracy without requiring retraining, feature engineering, or hyperparameter tuning. Operating in seconds on a single GPU via Baseten through zero-shot in-context learning, its instant, in-context predictions streamline high-volume workflows like demand forecasting, fraud detection, predictive maintenance, risk scoring, churn protection, and high-frequency trading. The list is long already, but there are many more model labs that Baseten is proud to partner with: Bria, Canopy Labs (Orpheus TTS), Krea (Turbo), MongoDB (Voyage 4), Musubi (PolicyLM-1b), Scaled Cognition (APT-1). Baseten look forward to working with many more and seeing how its customers leverage these models to build awesome applications. The Baseten distribution platform is bringing together its lab partners and its customers. Baseten is excited to be at the center of this thriving ecosystem. The future is a model ecosystem. Baseten believe the future of AI won't run on just a few large frontier models. It will be powered by a diverse ecosystem of models, open and closed. Baseten is investing in a future where open-weight models coexist with dozens of specialized closed-weight models, each optimized for a specific domain, modality, or use case. For that future to thrive, the labs building those models need an efficient way to bring their innovation to production, reach the right customers, and deploy them securely at scale. Baseten for Model Labs makes this future possible. By combining production-ready inference infrastructure with distribution through the Baseten Model Library and joint go-to-market support, Baseten help model labs focus on building great models while Baseten power their inference growth for the long term. Talk to Baseten Connect with its product experts to see how Baseten can help.