·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Cursor makes its biggest India push yet ahead of SpaceX acquisition with localized pricing3h◆Photonic reservoir computing with complex networks4h◆XS-VLA: Coupling Coarse-grained Spatial Distillation with Latent Flow Matching for Lightweight Robotic Control4h◆Agentic Permissions Policy Algebra for Taint Confinement in LLM Agents4h◆Beyond Squared Error: Exploring Loss Design for Enhanced Training of Generative Flow Networks4h◆The One-Word Census: Answer-Choice Conformity Across 44 Language Models4h◆Creative Integration: A Decidable Criterion of Creativity4h◆BERT-based Models vs. Large Language Models for Low-Resource Named Entity Recognition: A Comparative Study on Marathi4h◆Joint Optimization for Greedy Longest-match Tokenization4h◆Kimi K3: Open Frontier Intelligence4h◆The Few-shot Dilemma: Over-prompting Large Language Models4h◆Speculative Pipeline Decoding: Higher-Accuracy Drafting with Hidden Latency via Pipeline Parallelism4h◆Bayesian Complete-Pooling in Cross-Subject Classification for Motor Imagery Electroencephalogram4h◆StageGuard: Physiologically Constrained Sleep Staging4h◆Soft-Constrained Optimization of Latent Space in Variational Autoencoders4h◆Beyond Error-vs-Discard Characteristic: Toward Stable and Reliable Evaluation for Face Image Quality Assessment4h◆Analyzing the Importance of Blank for CTC-Based Knowledge Distillation4h◆Predicting Channel Closures in the Lightning Network with Machine Learning4h◆Evaluation of Blood Vessel Segmentation Methods on Hard-to-Detect Vascular Structures4h◆MOCA: A Transformer-based Modular Causal Inference Framework with One-way Cross-attention and Cutting Feedback4h◆Cursor makes its biggest India push yet ahead of SpaceX acquisition with localized pricing3h◆Photonic reservoir computing with complex networks4h◆XS-VLA: Coupling Coarse-grained Spatial Distillation with Latent Flow Matching for Lightweight Robotic Control4h◆Agentic Permissions Policy Algebra for Taint Confinement in LLM Agents4h◆Beyond Squared Error: Exploring Loss Design for Enhanced Training of Generative Flow Networks4h◆The One-Word Census: Answer-Choice Conformity Across 44 Language Models4h◆Creative Integration: A Decidable Criterion of Creativity4h◆BERT-based Models vs. Large Language Models for Low-Resource Named Entity Recognition: A Comparative Study on Marathi4h◆Joint Optimization for Greedy Longest-match Tokenization4h◆Kimi K3: Open Frontier Intelligence4h◆The Few-shot Dilemma: Over-prompting Large Language Models4h◆Speculative Pipeline Decoding: Higher-Accuracy Drafting with Hidden Latency via Pipeline Parallelism4h◆Bayesian Complete-Pooling in Cross-Subject Classification for Motor Imagery Electroencephalogram4h◆StageGuard: Physiologically Constrained Sleep Staging4h◆Soft-Constrained Optimization of Latent Space in Variational Autoencoders4h◆Beyond Error-vs-Discard Characteristic: Toward Stable and Reliable Evaluation for Face Image Quality Assessment4h◆Analyzing the Importance of Blank for CTC-Based Knowledge Distillation4h◆Predicting Channel Closures in the Lightning Network with Machine Learning4h◆Evaluation of Blood Vessel Segmentation Methods on Hard-to-Detect Vascular Structures4h◆MOCA: A Transformer-based Modular Causal Inference Framework with One-way Cross-attention and Cutting Feedback4h◆
DataBubble·

Model Detail

moonshotai logo

Kimi-K3

—
Provider: moonshotaiCategory: codePipeline: image-text-to-text
DB Score
63.0
Downloads
3K
Likes
6K
Day
+0.0%
Week
+0.0%
Month
+0.0%
Overview

Kimi-K3 is a code generation model with 1390.0B parameters released by moonshotai. The model is registered under the image-text-to-text pipeline tag on Hugging Face, and supports text+image->text inputs, distributed under a other license.

Performance

Kimi-K3 reports a Chatbot Arena ELO of 1,485 across 3,569 votes. Other benchmark slots are still empty in our dataset, so this single figure is best read as a partial picture rather than a full evaluation.

How we score this →
Pricing & Throughput

Kimi-K3 is priced at $3/M input tokens and $15/M output tokens. Operationally the model offers a 1049K-token context window, which matters when sizing it for prompt-heavy or latency-sensitive workloads. Pricing in this range is the working middle of the API market — neither the cheapest nor the most expensive option per token, so cost-fit is usually a function of how much output you generate.

Technical

Kimi-K3 ships with 1390.0B parameters. Total weight footprint is approximately 2779.9 GB, which is the relevant figure when planning local-inference VRAM. Distribution is governed by the other license — review the exact terms before commercial deployment.

Use Cases

Kimi-K3 is best fit for code completion, repository-scale Q&A, and pair-programming integrations, and long-context tasks such as full-codebase analysis or book-length summarization (1049K tokens). It is a less obvious choice for one-shot generation of security-critical code without review. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.

Download History
Pricing
Input ($/M tokens)
$3
Output ($/M tokens)
$15
Context Window
1049K
Arena & Community
Arena ELO
1,485
Arena Votes
3,569
Model Info
Licenseother
Modalitytext+image->text
Recent newsView all news →
Related News
arxiv4h ago

Kimi K3: Open Frontier Intelligence

arXiv:2607.24653v1 Announce Type: new Abstract: We introduce Kimi K3, a 2.8T parameter Mixture-of-Experts model with 104 billion activated parameters, native vision capabilities, and a 1-million-token context window. Kimi K3 is built on Kimi Delta Attention and Attention Residuals, which improve inf

techcrunchneutral3d ago

‘AI communism’, rogue models, and the why Kimi K3 spooked Wall Street

Chinese AI lab Moonshot’s open model Kimi went viral this week for reasons that had less to do with the model itself and more to do with how the U.S. AI industry reacted to it. Meanwhile, an unreleased OpenAI model wandered outside its test environment and ended up connected to a real security breac

techcrunchneutral4d ago

Experts say exploiting Anthropic’s Fable isn’t how Kimi K3 got so good

"I don't think you get a model this strong and this quickly on the heels of Fable doing strictly distillation," one expert told TechCrunch.

techcrunchneutral9d ago

Kimi: Threat or menace?

Chinese company Moonshot AI released a new version of its Kimi model this week, prompting concern about "full AI communism."

techcrunchneutral11d ago

Moonshot’s upcoming Kimi 3 is expected to close the gap with Anthropic’s Opus 4.8

The FT reports Kimi K3 will be the largest open AI model from China, with a parameter count between 2 trillion and 3 trillion.

arxiv113d ago

An Independent Safety Evaluation of Kimi K2.5

arXiv:2604.03121v1 Announce Type: cross Abstract: Kimi K2.5 is an open-weight LLM that rivals closed models across coding, multimodal, and agentic benchmarks, but was released without an accompanying safety evaluation. In this work, we conduct a preliminary safety assessment of Kimi K2.5 focusing on

Related Models
moonshotai logo
Kimi-K2.5
moonshotai · 1.5M downloads
moonshotai logo
Kimi-K2.6
moonshotai · 907K downloads
sentence-transformers logo
all-MiniLM-L6-v2
SBERT · 253.1M downloads
nomic-ai logo
nomic-embed-text-v1.5
nomic-ai · 17.1M downloads
HomeModelsNews