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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... show more

Inception

19,465 subscribers

55,636 次观看 • 10 天前 •via X (Twitter)

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23 条评论

Inception 的头像

Inception10 天前

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:

Yanis Miraoui 的头像

Yanis Miraoui10 天前

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

Julia Turc 的头像

Julia Turc10 天前

👀

Yuvraj Singh 的头像

Yuvraj Singh10 天前

Diffusion in voice Woah

Samar Khanna 的头像

Samar Khanna10 天前

⚡️⏩🗣️

Maciej Wolski 的头像

Maciej Wolski10 天前

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'.

Lord Python 🐍 的头像

Lord Python 🐍10 天前

bro... horrible timing

RayLin👾 的头像

RayLin👾9 天前

Congrats team!!

Amey Muke 的头像

Amey Muke10 天前

@yanismiraoui @tanishasharmax

NOOR TECH 的头像

NOOR TECH9 天前

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

Vantix AI Agency 的头像

Vantix AI Agency9 天前

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

Faraz Siddiqi 的头像

Faraz Siddiqi9 天前

would love to see how it performs on MIVAS bench

catman 的头像

catman10 天前

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

Marsly 的头像

Marsly10 天前

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

Dev Patel 的头像

Dev Patel9 天前

cool work

Elara AI 的头像

Elara AI9 天前

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

Sophia AI & Tool Expert 的头像

Sophia AI & Tool Expert9 天前

Mercury Voice delivering 2x lower latency is impressive

Juliana Stanford 的头像

Juliana Stanford9 天前

Congrats y'all

Alice The Ai Expert 的头像

Alice The Ai Expert10 天前

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!

Vera Ai | Tools & Updates 的头像

Vera Ai | Tools & Updates9 天前

Latency is the whole game for voice agents

Aina Ai | Tools & Updates 的头像

Aina Ai | Tools & Updates9 天前

Game changer 2x faster voice AI beating top models

Chole Syntax Expert AI 的头像

Chole Syntax Expert AI9 天前

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

Henrick 的头像

Henrick9 天前

Why not in openrouter?

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13,574 次观看 • 1 年前

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726,214 次观看 • 2 年前

Freddie wasn’t just a singer. He was a god on stage. That overwhelming voice, his one of a kind presence, and the charisma that captivated audiences in an instant... Even now, I get goosebumps every time I listen to him. Forever Freddie Mercury! #FreddieMercury #Legend

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ふLady

15,729 次观看 • 5 个月前

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. :-)

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 次观看 • 4 个月前

ANTHROPIC 🔥: Claude Opus and Sonnet models will soon become available on Claude Voice Mode! The new model selector has been hidden in the UI for roughly three weeks, defaulting to Haiku regardless of the selection. Today, it changed, and now it responds via Opus and Sonnet models. The system is still built on TTS; however, it handles interruptions super well and can operate Connectors! Access to a broader range of tools makes it much more capable than what ChatGPT is offering at the moment, yet usage might become a tricky topic, at least for high-effort reasoning.

ANTHROPIC 🔥: Claude Opus and Sonnet models will soon become available on Claude Voice Mode! The new model selector has been hidden in the UI for roughly three weeks, defaulting to Haiku regardless of the selection. Today, it changed, and now it responds via Opus and Sonnet models. The system is still built on TTS; however, it handles interruptions super well and can operate Connectors! Access to a broader range of tools makes it much more capable than what ChatGPT is offering at the moment, yet usage might become a tricky topic, at least for high-effort reasoning.

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