Mercury Coder Small
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Inception Labs
- Organisation type
- Industry
- Country
- United States of America
- Published
- 17 June 2025
- Authors
- Samar Khanna, Siddhant Kharbanda, Shufan Li, Harshit Varma, Eric Wang, Sawyer Birnbaum, Ziyang Luo, Yanis Miraoui, Akash Palrecha, Stefano Ermon, Aditya Grover, Volodymyr Kuleshov
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation, Code generation
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- tokens
"The overall model is trained on the order of trillions of tokens."
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA H100 SXM5 80GB
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- API access
- Training code
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
Sources
Where this record came from and when it was last checked.
- Reference
- Mercury: Ultra-Fast Language Models Based on Diffusion
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Mercury Coder Small was published by Inception Labs, in the country recorded as United States of America, during June 2025. The category the publisher falls under is industry.
It works in the domain of Language, and is recorded as performing the task of language modeling/generation, Code generation.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
Mercury Coder Small — common questions
Mercury Coder Small— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling/generation, Code generation. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
Mercury Coder Small— what GPU do I need to run it?
None. This is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Mercury Coder Small— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Mercury Coder Small— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
Mercury Coder Small— who created it?
It was published by Inception Labs, based in United States of America, an organisation categorised as industry.
Mercury Coder Small— when was it released?
It was published in June 2025.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.