https://arxiv.org/abs/2506.17298

Skip to main content
Cornell University
We gratefully acknowledge support from the Simons Foundation, member
institutions, and all contributors. Donate
 
arxiv logo > cs > arXiv:2506.17298
[                    ]

Help | Advanced Search

[All fields        ]
Search
arXiv logo
Cornell University Logo
[                    ] GO
quick links

  * Login
  * Help Pages
  * About

Computer Science > Computation and Language

arXiv:2506.17298 (cs)
[Submitted on 17 Jun 2025]

Title:Mercury: Ultra-Fast Language Models Based on Diffusion

Authors:Inception Labs, Samar Khanna, Siddhant Kharbanda, Shufan Li,
Harshit Varma, Eric Wang, Sawyer Birnbaum, Ziyang Luo, Yanis Miraoui,
Akash Palrecha, Stefano Ermon, Aditya Grover, Volodymyr Kuleshov
View a PDF of the paper titled Mercury: Ultra-Fast Language Models
Based on Diffusion, by Inception Labs and 12 other authors
View PDF HTML (experimental)

    Abstract:We present Mercury, a new generation of commercial-scale
    large language models (LLMs) based on diffusion. These models are
    parameterized via the Transformer architecture and trained to
    predict multiple tokens in parallel. In this report, we detail
    Mercury Coder, our first set of diffusion LLMs designed for
    coding applications. Currently, Mercury Coder comes in two sizes:
    Mini and Small. These models set a new state-of-the-art on the
    speed-quality frontier. Based on independent evaluations
    conducted by Artificial Analysis, Mercury Coder Mini and Mercury
    Coder Small achieve state-of-the-art throughputs of 1109 tokens/
    sec and 737 tokens/sec, respectively, on NVIDIA H100 GPUs and
    outperform speed-optimized frontier models by up to 10x on
    average while maintaining comparable quality. We discuss
    additional results on a variety of code benchmarks spanning
    multiple languages and use-cases as well as real-world validation
    by developers on Copilot Arena, where the model currently ranks
    second on quality and is the fastest model overall. We also
    release a public API at this https URL and free playground at
    this https URL

Comments: 15 pages; equal core, cross-function, senior authors listed
          alphabetically
Subjects: Computation and Language (cs.CL); Artificial Intelligence
          (cs.AI); Machine Learning (cs.LG)
Cite as:  arXiv:2506.17298 [cs.CL]
          (or arXiv:2506.17298v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2506.17298
          Focus to learn more
          arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Aditya Grover [view email]
[v1] Tue, 17 Jun 2025 17:06:18 UTC (60 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled Mercury: Ultra-Fast Language
    Models Based on Diffusion, by Inception Labs and 12 other authors
  * View PDF
  * HTML (experimental)
  * TeX Source
  * Other Formats

license icon view license
Current browse context:
cs.CL
< prev   |   next >
new | recent | 2025-06
Change to browse by:
cs
cs.AI
cs.LG

References & Citations

  * NASA ADS
  * Google Scholar
  * Semantic Scholar

a export BibTeX citation Loading...

BibTeX formatted citation

x
[loading...          ]
Data provided by:

Bookmark

BibSonomy logo Reddit logo
(*) Bibliographic Tools

Bibliographic and Citation Tools

[ ] Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
[ ] Connected Papers Toggle
Connected Papers (What is Connected Papers?)
[ ] Litmaps Toggle
Litmaps (What is Litmaps?)
[ ] scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
( ) Code, Data, Media

Code, Data and Media Associated with this Article

[ ] alphaXiv Toggle
alphaXiv (What is alphaXiv?)
[ ] Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
[ ] DagsHub Toggle
DagsHub (What is DagsHub?)
[ ] GotitPub Toggle
Gotit.pub (What is GotitPub?)
[ ] Huggingface Toggle
Hugging Face (What is Huggingface?)
[ ] Links to Code Toggle
Papers with Code (What is Papers with Code?)
[ ] ScienceCast Toggle
ScienceCast (What is ScienceCast?)
( ) Demos

Demos

[ ] Replicate Toggle
Replicate (What is Replicate?)
[ ] Spaces Toggle
Hugging Face Spaces (What is Spaces?)
[ ] Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
( ) Related Papers

Recommenders and Search Tools

[ ] Link to Influence Flower
Influence Flower (What are Influence Flowers?)
[ ] Core recommender toggle
CORE Recommender (What is CORE?)

  * Author
  * Venue
  * Institution
  * Topic

( ) About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and
share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have
embraced and accepted our values of openness, community, excellence,
and user data privacy. arXiv is committed to these values and only
works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community?
Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is
MathJax?)

  * About
  * Help

  * Click here to contact arXiv Contact
  * Click here to subscribe Subscribe

  * Copyright
  * Privacy Policy

  * Web Accessibility Assistance
  * arXiv Operational Status
    Get status notifications via email or slack