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LatestMoonshot AIK2.6
Moonshot AIVision-Language

Moonshot AI Releases Kimi-K2.6 Multimodal Model

The Chinese AI lab has published weights for its new vision-language model, though a restrictive license limits its use to research applications.

Apr 14, 2026
Major releaseOther
Kimi K2.6

Moonshot AI, one of China's most prominent AI labs, has released a new multimodal model, Kimi-K2.6. The release marks a significant expansion for the company's Kimi model family, which gained attention for its long-context text capabilities.

This new version adds vision to the mix, making Kimi-K2.6 a vision-language model (VLM) capable of processing and understanding both text and images. The model weights were published to Hugging Face in the compressed-tensors format, which is optimized for efficient inference.

While the weights are publicly available, their use is strictly limited. The model is governed by a custom license that permits academic research but explicitly prohibits any commercial use. This type of release allows the community to study the model's architecture and capabilities without enabling direct competition.

For the open-source community, the Kimi-K2.6 release provides a valuable look at the multimodal technology developed by a leading commercial AI company. It's a research artifact that allows developers to benchmark and analyze a powerful new VLM, even if they can't deploy it in products.

Sources

  • moonshotai/Kimi-K2.6

    Hugging Face

    Visit
OlderOpenBMB Releases MiniCPM-V for On-Device VisionOpenBMB · Vision-Language · 7 months agoNewerMotif Releases 2B Open-Source Text-to-Video ModelMotif Technologies · Text → Video · 7 months ago

Get the model

Hugging Face

Specs

Context window262K tokens
ArchitectureKimiK25ForConditionalGeneration
LicenseOTHER
Downloads249.8K
Likes1.6K

Can you run it?

Datacenter only — about 619.6 GB even as published.

  • COMPRESSED-TENSORS 4-bit (as published)

    8×H100 80 GB node

    619.6 GB
Your machine
GGUF builds
  • Mixture-of-experts: every expert must be loaded, so memory follows total parameters, not the active slice.
  • Published weights are already quantized (COMPRESSED-TENSORS 4-bit); smaller builds are not assumed.
  • KV cache sized for 8,192 tokens of context; longer prompts need proportionally more.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Text / LLMVision-Language
4 versions — view changelog

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