Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

Today, we’re introducing Mercury Voice, a diffusion LLM specialized for agentic voice applications. Mercury Voice delivers 2x+ lower latency than models including GPT-6 Luna, Gemma 4 31B, and Claude Haiku 4.5, while beating them on a range of voice benchmarks. Enterprise customers interested in Mercury Voice can contact us...

55,636 Aufrufe • vor 10 Tagen •via X (Twitter)

23 Kommentare

Profilbild von Inception
Inceptionvor 10 Tagen

Voice agents have ~500ms to respond. Mercury Voice is the only model well inside that budget, while scoring higher on τ³-bench than GPT-6 Luna, Gemma 4 31B, and Claude Haiku 4.5. Benchmarks:

Profilbild von Yanis Miraoui
Yanis Miraouivor 10 Tagen

Lots of work put into this one and super happy about the result. We're beating competitors models on quality AND latency! Contact us if you want to try it out

Profilbild von Julia Turc
Julia Turcvor 10 Tagen

👀

Profilbild von Yuvraj Singh
Yuvraj Singhvor 10 Tagen

Diffusion in voice Woah

Profilbild von Samar Khanna
Samar Khannavor 10 Tagen

⚡️⏩🗣️

Profilbild von Maciej Wolski
Maciej Wolskivor 10 Tagen

I will wait until there is no need to contact sales@ to use a model. Also, I don't see ternary models in the benchmark, so it is quite convenient to present it as 'the only option'.

Profilbild von Lord Python 🐍
Lord Python 🐍vor 10 Tagen

bro... horrible timing

Profilbild von RayLin👾
RayLin👾vor 9 Tagen

Congrats team!!

Profilbild von Amey Muke
Amey Mukevor 10 Tagen

@yanismiraoui @tanishasharmax

Profilbild von NOOR TECH
NOOR TECHvor 9 Tagen

Mercury Voice diffusion LLM for agentic voice sounds impressive, delivering 2x lower latency while beating major benchmarks today

Profilbild von Vantix AI Agency
Vantix AI Agencyvor 9 Tagen

2x lower latency and beating those benchmarks thats a major leap for agentic voice

Profilbild von Faraz Siddiqi
Faraz Siddiqivor 9 Tagen

would love to see how it performs on MIVAS bench

Profilbild von catman
catmanvor 10 Tagen

Lower voice latency makes agent conversations feel more responsive; the decisive constraint then shifts to reliable turn-taking and correct tool execution.

Profilbild von Marsly
Marslyvor 10 Tagen

Would love to use. But it's not on openrouter.

Profilbild von Dev Patel
Dev Patelvor 9 Tagen

cool work

Profilbild von Elara AI
Elara AIvor 9 Tagen

Mercury Voice diffusion for voice with 2x lower latency is big

Profilbild von Sophia AI & Tool Expert
Sophia AI & Tool Expertvor 9 Tagen

Mercury Voice delivering 2x lower latency is impressive

Profilbild von Juliana Stanford
Juliana Stanfordvor 9 Tagen

Congrats y'all

Profilbild von Alice The Ai Expert
Alice The Ai Expertvor 10 Tagen

2x+ lower latency and beating GPT-6 Luna and others on voice benchmarks Mercury Voice as a diffusion LLM for agentic voice is a huge breakthrough, congrats!

Profilbild von Vera Ai | Tools & Updates
Vera Ai | Tools & Updatesvor 9 Tagen

Latency is the whole game for voice agents

Profilbild von Aina Ai | Tools & Updates
Aina Ai | Tools & Updatesvor 9 Tagen

Game changer 2x faster voice AI beating top models

Profilbild von Chole Syntax Expert AI
Chole Syntax Expert AIvor 9 Tagen

2x lower latency AND beating GPT 6 Luna Gemma 4 31B and Haiku 4.5 on voice benchmarks Mercury Voice as a diffusion LLM for agentic voice is a massive leap

Profilbild von Henrick
Henrickvor 9 Tagen

Why not in openrouter?

Ähnliche Videos

Introducing PhoneLLM, an open model for voice agents. GPT 5.6 Terra performance on typical voice agent tasks at 1/3 the latency and 1/18 the cost. For voice agents, we need models that are both very low latency and very good at tool calling and instruction following. There's a trade-off here, and we often have to compromise on either latency or capability when building voice agents. With PhoneLLM (and the training and data stack that made this model possible) we're fixing this problem. For the last couple of years, most of the effort in frontier model development has gone towards leveraging test-time compute. Which is awesome! Models of all shapes and sizes are available that perform really, really well ... if you have "thinking" turned on for your model. But if you need your agent to respond at voice conversation speed, you can't use thinking models. PhoneLLM is a full-weights fine-tune of NVIDIA Nemotron Nano 30B. We trained on a wide range of real-world telephone and customer support use cases. The training focused on taking the excellent Nano 30B base capabilities and teaching the model to do typical voice agent tasks with thinking disabled. The results are really good: accurate tool calling and concise, on-topic responses in long conversations. And fast: TTFAT measured server-side is <100ms if you run PhoneLLM on a lightly loaded B200. :-) But seriously, when we characterize model latency, we do it with full, end-to-end, batched request simulations using real Pipecat voice agent pipelines. You can serve more than 80 concurrent agents on a single B200 with P95 end-to-end TTFAT <600ms. Including network overhead. That's an LLM cost-per-minute around $0.0025. (1/4 of a cent.) At a latency lower than any third-party API offers today. More details about this model, including weights on Hugging Face, how to spin it up with one click on Modal, and a starter project repo you can clone, are in the thread ...

kwindla

333,276 Aufrufe • vor 1 Monat

Learn to build conversational AI voice agents in "Building AI Voice Agents for Production", created in collaboration with LiveKit and RealAvatar, and taught by dsa (Co-founder & CEO of LiveKit), Shayne (Developer Advocate, LiveKit), and Nedelina Teneva (Head of AI at RealAvatar, an AI Fund portfolio company). Voice agents combine speech and reasoning capabilities to enable real-time conversations. They're already being used to support customer service, to improve accessibility in healthcare, for entertainment applications, and for talk therapy. In this course, you’ll learn to build voice agents that listen, reason, and respond naturally. You’ll follow the architecture used to create the "AI Andrew" Avatar, a collaborative project between and RealAvatar that responds to users in what sounds like my voice. You’ll build a voice agent from scratch and deploy it to the cloud, enabling support for many simultaneous users. What you’ll learn: - Understand the fundamentals of voice agents, including key components like speech-to-text (STT), text-to-speech (TTS), and LLMs, and how latency is introduced at each layer. - Explore voice agent architectures and the trade-offs between modular pipelines and speech-to-speech APIs. - Explore how platforms like LiveKit mitigate latency issues with optimized networking infrastructure and low-latency communication protocols. - Learn how to connect client devices to voice agents using WebRTC—and why it outperforms HTTP and WebSocket for low-latency audio streaming. - Incorporate voice activity detection (VAD), end-of-turn detection, and context management to detect turns, handle interruptions, and manage conversational flow. - Understand the trade-offs between latency, quality, and cost in an example in which you build a voice agent and change its voice. - Equip your agent with metrics to measure latency at each stage of the voice pipeline and learn the key levers you can pull to make your agent faster and more responsive. The voice agents built in this course also incorporate voice technology from , a supporting contributor to the project. By the end of this course, you'll have learned the components of an AI voice agent pipeline, combined them into a system with low-latency communication, and deployed them on cloud infrastructure so it scales to many users. I’m looking forward to seeing what voice agents you build from this course! Please sign up here:

Andrew Ng

87,965 Aufrufe • vor 1 Jahr

Cerebras inference is very fast. So fast that it changes how we think about configuring our LLMs for voice agent use cases. Kimi K2.6 is a 1T parameter reasoning model that Cerebras serves at 650 - 1,000 tokens per second (end-to-end throughput), with time to first token metrics as low as 150ms (latency). These numbers are two to three times faster than other similarly capable models. The biggest lever we get from this kind of speed is that we can use the model in reasoning mode, and still have excellent "time to first non-thinking token." This solves a big pain point we have in 2026 for voice agent use cases. Almost all recent innovation in post-training has focused on making models good at reasoning ("test time compute"). This is great, but it makes the user-facing model latency much, much slower. Which is a problem for conversational voice agents. We can run Kimi K2.6 with reasoning turned on, and get responses faster than other models produce with reasoning disabled. On my 30-turn voice agent benchmark, Kimi K2.6 with reasoning enabled ties GPT 5.1 and Haiku 4.5 with reasoning disabled, and is still about 200ms seconds faster! On my primary task agent benchmark, Kimi K2.6 is now the #2 model. It ranks just behind Gemini 3.5 Flash in "high" reasoning mode, and tied with GLM 5, Sonnet 4.6, and GPT 5.4 with reasoning set to "low." But Kimi K2.6 completes each turn in the agent loop in under 500ms. The other four models are all at least 3x slower. (Models only qualify for this benchmark if they can complete task turns at a P50 <4s.) A couple of other things that this speed buys us, for production voice agents: - Tool calls happen fast enough that we don't have to work around tool call latency in our pipeline design. - We can prompt the model to output structured data at the beginning of a response, followed by plain text for voice generation. This opens up possibilities like asking the model to do complex classification/generation tasks that influence the rest of the pipeline. For example, the model could create a detailed style prompt for a steerable TTS model, for each individual conversation turn. And, of course, you can use Kimi K2.6 with reasoning turned off. Cerebras calls this "instant" mode. Here's a video of a Cerebras Kimi K2.6 voice agent with voice-to-voice response time, measured at the client, under 500ms. This is the true response latency as perceived by the user, including all network and audio codec overhead, transcription and turn detection, Kimi K2.6 token generation, and voice generation. 500ms is, effectively, instant. So the Cerebras naming for this mode is a propos. :-)

kwindla

40,593 Aufrufe • vor 4 Monaten