gemma-4-26B-A4B-it

Name: gemma-4-26B-A4B-it
Author: google

by google

Mixture-of-experts VLM delivering 97% of 31B quality at 8x less compute

1.1Mdl/month

26B total / 4B activeparams

HuggingFace Run it on your own data, free

Identifiers

Model ID

google/gemma-4-26B-A4B-it

Feature URI

mixpeek://image_extractor@v1/google_gemma4_26b_a4b_v1

Overview

Gemma 4 27B-A4B is Google's MoE vision-language model that activates only 4B parameters per token from a total of 26B. It ranked #6 on the Arena AI leaderboard at launch while using a fraction of the compute of dense models its size.

The model handles both text and image input with a 256K context window, making it suitable for long-document visual understanding. Its efficiency profile makes it the best choice when you need high-quality VLM capabilities at manageable cost.

Architecture

Mixture-of-Experts architecture with 26B total parameters, 4B active per token. Vision encoder processes image patches alongside text tokens. 256K context window. Supports optional 'thinking' mode for chain-of-thought reasoning.

Mixpeek SDK Integration

// No extractor parameter takes a Hugging Face model id (checked against
// GET /v1/discovery/extractors, which returns 13), so gemma-4-26B-A4B-it 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.