Gemma 4 26B-A4B

Multimodal reasoning model for text, images, and video.

This Replicate endpoint serves an optimized version of Gemma 4 26B-A4B, a 26B-parameter vision-language MoE (Mixture of Experts) model from Google DeepMind designed for instruction following, reasoning, coding, document understanding, and agent-style workflows.

Compared with the original Hugging Face model card, this page focuses on the hosted experience: fast access to a production-ready version of the model without the self-hosting setup.

What it does

Gemma 4 26B-A4B is a general-purpose multimodal model that can:

• Answer questions about text, images, and video
• Reason over diagrams, charts, and visual documents
• Follow complex instructions with native system prompt support
• Perform coding and agent-style tasks with built-in function calling
• Handle long-context workloads up to 256K tokens
• Work across 140+ languages
• Think step-by-step before answering (configurable thinking mode)

Why use this model

Gemma 4 26B-A4B combines strong language reasoning with native multimodal understanding in an efficient MoE architecture — only 4B parameters are active per token out of 26B total, giving near-4B-model speed with much larger model quality. It is well suited for:

• Visual question answering
• Document parsing, OCR, and data extraction
• Coding and technical assistance
• Multilingual assistants
• Long-context analysis (up to 256K tokens)
• Agentic applications with tool use and function calling
• Reasoning-heavy product features

Highlights

• Efficient MoE architecture: 128 experts with 8 active per token — runs almost as fast as a 4B model while leveraging 26B total parameters
• Built-in thinking mode: configurable step-by-step reasoning before answering
• Native function calling: designed for agentic and tool-calling workflows
• Variable image resolution: configurable visual token budget (70 to 1120 tokens per image) for balancing detail vs speed
• 256K context window: native support for very long inputs
• Native system prompt support: structured and controllable conversations via the system role
• Broad language coverage: pre-trained on 140+ languages with strong multilingual performance
• Apache 2.0 license: permissive open-source license

Model details

Performance overview

Gemma 4 26B-A4B delivers strong results across a wide range of benchmarks:

Best use cases

Use this model when you need a single endpoint that can handle:

• Chat with image or video input
• Screenshot or UI understanding
• OCR and document Q&A
• Diagram and chart comprehension
• Multilingual assistants
• Reasoning-heavy product features (with thinking mode)
• Agent pipelines that mix perception and action
• Function calling and tool use

Notes

• When thinking mode is enabled, the model reasons internally before responding. This generally improves answer quality at the cost of additional output tokens.
• For best results with multimodal inputs, the model expects images and video before text in the prompt. This endpoint handles that ordering automatically.
• Recommended sampling parameters from Google: temperature=1.0, top_p=0.95, top_k=64.
• Because this is a hosted and optimized Replicate deployment, behavior and latency may differ from raw self-hosted Hugging Face checkpoints.

Limitations

Like other large multimodal models, Gemma 4 26B-A4B can still:

• Hallucinate facts or visual details
• Make mistakes on fine-grained counting or localization
• Underperform on highly domain-specific inputs without careful prompting
• Produce variable outputs across languages and long contexts
• Struggle with subtle nuance, sarcasm, or figurative language

Human review is recommended for high-stakes use cases.