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Inception releases Mercury 3 diffusion LLM at 1000 tokens/sec

By PulseAugur Editorial · [1 sources] ·

Inception has released Mercury 3, a diffusion LLM (dLLM) that generates text in parallel rather than token-by-token. This approach allows Mercury 3 to achieve over 1,000 tokens per second, reportedly matching or exceeding the quality of models like GPT-6 Luna and Claude Haiku 5.5. The model offers a 1 million token context window and is accessible via an OpenAI-compatible API, with pricing set at $0.20 per 1 million input tokens and $0.70 per 1 million output tokens, with a 50% launch discount. This speed advancement is particularly significant for AI agents, where model generation time is a critical bottleneck. AI

IMPACT Accelerates AI agent development by drastically reducing model inference time, enabling more complex and responsive agentic workflows.

RANK_REASON Frontier-lab model release with system card [lever_c_demoted from frontier_release: ic=1 ai=1.0]

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Inception releases Mercury 3 diffusion LLM at 1000 tokens/sec

How we ranked this

Signal score
60 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Significant
Frontier-lab model release with system card [lever_c_demoted from frontier_release: ic=1 ai=1.0]
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
model release, infra
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High
Clearly on-topic for AI-industry coverage.
Story freshness
Breaking (< 6h)
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · M.T.Ramkrushna ·

    Mercury 3 and the Diffusion LLM Bet: What 1,000 Tokens per Second Changes for Agents (and What It Doesn't)

    <p>Every step of an AI agent waits on the model.</p> <p>Plan, call a tool, read the result, decide, call another tool. If each model call takes a few seconds, a ten-step task feels like forever, and a voice agent feels broken. Most "make it faster" work today is squeezing the sam…

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