Keye-VL-2.0-30B-A3B

Name: Keye-VL-2.0-30B-A3B
Author: Kwai-Keye

by Kwai-Keye

Kuaishou's video-centric vision-language model for clip understanding and Q&A

290dl/month

30B MoE (~3B active)params

HuggingFace Run it on your own data, free

Identifiers

Model ID

Kwai-Keye/Keye-VL-2.0-30B-A3B

Feature URI

mixpeek://video_extractor@v1/kwai_keye_vl2_30b_a3b_v1

Overview

Keye-VL 2.0 (30B Mixture-of-Experts with ~3B active params) is Kuaishou's video-first multimodal LLM, built for understanding short-form and long video alongside images and text. It is strong at video question answering, captioning, and temporal reasoning over clips.

On Mixpeek, a model like Keye-VL works as a captioning/understanding stage in a video pipeline: a fast encoder (V-JEPA 2, VideoPrism, InternVideo2) retrieves candidate clips, then a VLM like Keye-VL generates grounded descriptions or answers questions about the retrieved moments, keeping the expensive VLM off the full corpus.

Architecture

Mixture-of-Experts vision-language model (~30B total, ~3B active) with a vision encoder feeding an LLM decoder, instruction-tuned for video and image understanding, captioning, and VQA with temporal reasoning.

Mixpeek SDK Integration

// No extractor parameter takes a Hugging Face model id (checked against
// GET /v1/discovery/extractors, which returns 13), so Keye-VL-2.0-30B-A3B runs
// on your side and the output is upserted through POST
// /v1/namespaces/{namespace_id}/documents/upsert. On Enterprise the other
// path is to upload the weights instead: POST /v1/namespaces/{id}/models
// accepts the huggingface format and a custom plugin loads them.
const res = await fetch(
  "https://api.mixpeek.com/v1/namespaces/ns_your_namespace/documents/upsert",
  {
    method: "POST",
    headers: {
      Authorization: "Bearer API_KEY",
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      collection_id: "col_your_collection",
      documents: [
        {
          document_id: "asset-00412",
          // The model produces text, so it lands in payload. Give the
          // collection a text vector index and embed that text to make it
          // searchable rather than only filterable.
          payload: { extracted_text: modelOutput, source_key: "archive/2026/asset-00412" },
          vectors: { "multimodal-embedding": embeddingOfModelOutput },
        },
      ],
    }),
  },
);

// Managed alternative, if this exact model is not the requirement:
// universal_extractor@v1 runs google/gemini-embedding-2
// (3072-d) over a bucket, with no inference of your own.