Kimi k1.5
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
- Moonshot
- Organisation type
- Industry
- Country
- China
- Published
- 22 January 2025
- Authors
- Kimi Team, Angang Du, Bofei Gao, Bowei Xing, Changjiu Jiang, Cheng Chen, Cheng Li, Chenjun Xiao, Chenzhuang Du, Chonghua Liao, Chuning Tang, Congcong Wang, Dehao Zhang, Enming Yuan, Enzhe Lu, Fengxiang Tang, Flood Sung, Guangda Wei, Guokun Lai, Haiqing Guo, Han Zhu, Hao Ding, Hao Hu, Hao Yang, Hao Zhang, Haotian Yao, Haotian Zhao, Haoyu Lu, Haoze Li, Haozhen Yu, Hongcheng Gao, Huabin Zheng, Huan Y…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Multimodal, Language, Vision
- Task
- Language modeling/generation, Code generation, Quantitative reasoning, Question answering, Visual question answering, Translation, Image captioning, Visual puzzles
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
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
https://github.com/MoonshotAI/Kimi-k1.5
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- SOTA improvement
- Record confidence
- Unknown
- Citations
- 913
"Sota short-CoT performance, outperforming GPT-4o and Claude Sonnet 3.5 on AIME, MATH-500, LiveCodeBench by a large margin (up to +550%)"
Sources
Where this record came from and when it was last checked.
- Reference
- Kimi k1.5: Scaling Reinforcement Learning with LLMs
- Last updated
- 25 May 2026
What the numbers mean
What this model is
Kimi k1.5 was published by Moonshot, in the country recorded as China, during January 2025. The category the publisher falls under is industry.
It works in the domain of Multimodal, Language, Vision, and is recorded as performing the task of language modeling/generation, Code generation, Quantitative reasoning, Question answering, Visual question answering, Translation, Image captioning, Visual puzzles.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
Its inclusion criterion: sOTA improvement.
Answers
Kimi k1.5 — common questions
Kimi k1.5— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Kimi k1.5— 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.
Kimi k1.5— who created it?
It was published by Moonshot, based in China, an organisation categorised as industry.
Kimi k1.5— when was it released?
It was published in January 2025.
Kimi k1.5— what is it used for?
It works in the domain of Multimodal, Language, Vision, and is recorded as handling the task of language modeling/generation, Code generation, Quantitative reasoning, Question answering, Visual question answering, Translation, Image captioning, Visual puzzles. These are the areas it was designed around; they describe intent rather than a hard boundary.
Kimi k1.5— 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.
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.