Google Gemma 4 26B A4B Instruct
Google's highly efficient 26B Mixture-of-Experts (MoE) model with 4B active parameters, offering multimodal reasoning, vision, and tool use under an Apache 2.0 license.
Overview
What is Google Gemma 4 26B A4B Instruct
Google Gemma 4 26B A4B Instruct is an open-weights, multimodal Mixture-of-Experts (MoE) model released by Google DeepMind in April 2026. It features 26B total parameters with 4B active parameters per token, delivering advanced reasoning, vision, video processing, and native function calling within a 256K context window.
Running it privately on Venice
On Venice, you run Gemma 4 26B A4B Instruct via an anonymized pipeline that strips your personal identity from the request. Although served by a third-party provider that may retain prompt data per their policies, Venice ensures no account history or personal metadata is ever attached to your queries, allowing you to leverage Google's advanced reasoning and vision tools with a high degree of privacy.
Assessment
Strengths and limitations
- Highly efficient Mixture-of-Experts (MoE) architecture activating only 4B parameters per token, offering fast inference speeds.
- Native multimodal support, capable of processing text, images, and video inputs directly.
- Strong reasoning capabilities with configurable thinking modes and native tool use / function calling.
- Generous 256K token context window, allowing for deep document analysis and long-form conversations.
- Permissive Apache 2.0 license, making it fully open-weights and friendly for commercial use.
- With only 4B active parameters, it may trail larger dense models (like Gemma 4 31B) on highly complex agentic tasks.
- Does not natively support audio input or output in this 26B size (unlike the smaller E2B and E4B variants).
- Served via a third-party provider on Venice, meaning requests are anonymized but processed outside Venice's direct infrastructure.
Capabilities
What it supports
- Tool use / function calling
- Vision (image input)
- Reasoning
- Web search
- Code-optimized
- Structured output (JSON schema)
- Audio input
- Video input
- Multiple image inputs
- Log probabilities
Specifications
Datasheet
- Maker
- Google DeepMind
- Released
- April 2, 2026
- Architecture
- Mixture-of-Experts (MoE)
- Parameters
- 26B total (4B active per token)
- Open weights
- Yes (Apache 2.0)
- Modality
- Text, Image, Video input / Text output
- Quantization
- BF16
- Context window
- 256K tokens
- Max output
- 8.192K tokens
- Capabilities
- Vision, Function calling, Reasoning, Web search
- Privacy on Venice
- Private — zero retention
- Available on Venice since
- Apr 2026
API
Call it from your code
Venice exposes an OpenAI-compatible API. Point your base URL at Venice and pass the model id.
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "google-gemma-4-26b-a4b-it",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Pricing
What it costs on Venice
Billed per token on Venice: $0.16 per 1M input tokens and $0.50 per 1M output tokens.
New Venice accounts include a free daily allowance and 500 welcome credits — no credit card required.
Alternatives
How it compares
| Model | Best for | Architecture | Active Params | Context Window | Price (Venice) |
|---|---|---|---|---|---|
| Google Gemma 4 26B A4B Instruct | Google's highly efficient MoE model with native vision and reasoning. | Mixture-of-Experts (MoE) | 4B | 256K tokens | $0.16 / $0.50 |
| Google Gemma 4 31B Instruct | The dense flagship of the Gemma 4 family, offering slightly higher raw intelligence. | Dense | 31B | 256K tokens | $0.12 / $0.36 |
| DeepSeek V3.2 | A strong open-weights rival, though text-only compared to Gemma's multimodal capabilities. | Mixture-of-Experts (MoE) | Varies | 160K tokens | $0.33 / $0.48 |
| Claude Sonnet 4.6 | A closed-source frontier model with a much larger context, but significantly more expensive. | Proprietary | Proprietary | 1M tokens | $3.60 / $18 |
Google's highly efficient MoE model with native vision and reasoning.
Use cases
What it is good for
- 01Analyzing large documents or codebases utilizing its 256K context window.
- 02Multimodal tasks requiring image or video comprehension alongside text generation.
- 03Building agentic workflows that leverage native function calling and structured JSON outputs.
- 04Cost-effective reasoning tasks where a massive dense model would be too expensive or slow.
- 05Local deployment and fine-tuning under the permissive Apache 2.0 license.
Prompting
Getting better results
Provide clear, step-by-step instructions to leverage the model's advanced reasoning capabilities.
When using vision, upload high-resolution images; the model natively supports variable aspect ratios.
Use structured JSON schemas in your prompts to take advantage of its native structured output support.
Version history
Early open-weights release from Google.
Highly popular dense model with excellent performance-to-size ratio.
Introduced multimodal capabilities and expanded multilingual support.
Current MoE model featuring advanced reasoning, video input, and Apache 2.0 licensing.
FAQ
Frequently asked questions
Google Gemma 4 26B A4B Instruct is an open-weights, multimodal Mixture-of-Experts (MoE) model developed by Google DeepMind. Released in April 2026, it features 26B total parameters (4B active) and supports text, image, and video inputs with native reasoning and tool use.
On Venice, Gemma 4 26B A4B Instruct is billed per token at a highly competitive rate of $0.16 per 1 million input tokens and $0.50 per 1 million output tokens.
Yes, Gemma 4 26B A4B Instruct is released under a highly permissive Apache 2.0 license. This makes its weights open and free for both research and commercial use, allowing developers to self-host and fine-tune the model.
Yes. Gemma 4 26B A4B Instruct is natively multimodal, supporting both image and video inputs. However, unlike the smaller E2B and E4B models in the same family, it does not natively support audio inputs.
The 26B A4B model uses a Mixture-of-Experts (MoE) architecture with only 4B active parameters per token, making it highly efficient and fast. The 31B model is a dense architecture, meaning all 31B parameters are active for every token, which generally yields slightly higher intelligence and better agentic performance at the cost of higher computational overhead.
Yes. The model natively supports tool use (function calling), structured JSON outputs, and reasoning. When accessed through Venice, it can also utilize web search to ground its answers in real-time information.
Venice forwards your requests to a third-party provider completely anonymized, stripping all personally identifying metadata. While Venice itself operates under a strict zero-retention policy, please note that the third-party provider serving the model may retain and train on prompt data per their own policies.
Run Google Gemma 4 26B A4B Instruct privately
No prompt logging. No data used for training.