gemma-4-26B-A4B-it
by google
Mixture-of-experts VLM delivering 97% of 31B quality at 8x less compute
google/gemma-4-26B-A4B-itmixpeek://image_extractor@v1/google_gemma4_26b_a4b_v1Overview
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
import { Mixpeek } from "mixpeek";
const mx = new Mixpeek({ apiKey: "API_KEY" });
// Managed: create a collection over a bucket; Mixpeek runs this model's extractor
const collection = await mx.collections.create({
namespace_id: "my-namespace",
collection_name: "my-collection",
source: { type: "bucket", bucket_ids: ["bkt_your_bucket"] },
feature_extractor: {
feature_extractor_name: "scene_caption",
version: "v1",
parameters: { model_id: "google/gemma-4-26B-A4B-it" },
},
});Capabilities
- Multimodal understanding (text + images)
- 256K context window for long documents
- MoE efficiency: 4B active / 26B total
- Built-in reasoning mode
- Apache 2.0 license
Use Cases on Mixpeek
Benchmarks
| Dataset | Metric | Score | Source |
|---|---|---|---|
| MMLU Pro | Accuracy | 83% | Google, May 2026 |
| AIME 2026 | Accuracy | 85% | Google, May 2026 |
| Arena AI Leaderboard | ELO | 1441 (#6) | Arena AI, May 2026 |
Performance
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Specification
Research Paper
Gemma 4: Byte for byte, the most capable open models
arxiv.orgBuild a pipeline with gemma-4-26B-A4B-it
Add this model to a processing pipeline alongside other extractors. Combine with retrieval stages for end-to-end search.
Open Studio