gemma4

Gemma 4 Ollama logo Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input and generating text output.

Gemma 4 introduces key capability and architectural advancements:

Reasoning – All models in the family are designed as highly capable reasoners, with configurable thinking modes.
Extended Multimodalities – Processes Text, Image with variable aspect ratio and resolution support (all models)
Diverse & Efficient Architectures – Offers Dense and Mixture-of-Experts (MoE) variants of different sizes for scalable deployment.
Optimized for On-Device – Smaller models are specifically designed for efficient local execution on laptops and mobile devices.
Increased Context Window – The small models feature a 128K context window, while the medium models support 256K.
Enhanced Coding & Agentic Capabilities – Achieves notable improvements in coding benchmarks alongside native function-calling support, powering highly capable autonomous agents.
Native System Prompt Support – Gemma 4 introduces native support for the `system` role, enabling more structured and controllable conversations.

Models

Ollama’s cloud

ollama run gemma4:31b-cloud

Edge models

The “E” in E2B and E4B stands for “effective” parameters, and are made for edge device deployments.

Effective 2B (E2B)

ollama run gemma4:e2b

Effective 4B (E4B)

ollama run gemma4:e4b

Workstation models

These models are designed for frontier intelligence locally.

12B

ollama run gemma4:12b

26B (Mixture of Experts model with 4B active parameters)

ollama run gemma4:26b

31B (Dense)

ollama run gemma4:31b

Benchmark Results

These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation. Evaluation results marked in the table are for instruction-tuned models.

| | Gemma 4 31B | Gemma 4 26B A4B | Gemma 4 E4B | Gemma 4 E2B | Gemma 3 27B (no think) | | --------------------------------------------------------- | ----------- | --------------- | ----------- | ----------- | ---------------------- | | MMLU Pro | 85.2% | 82.6% | 69.4% | 60.0% | 67.6% | | AIME 2026 no tools | 89.2% | 88.3% | 42.5% | 37.5% | 20.8% | | LiveCodeBench v6 | 80.0% | 77.1% | 52.0% | 44.0% | 29.1% | | Codeforces ELO | 2150 | 1718 | 940 | 633 | 110 | | GPQA Diamond | 84.3% | 82.3% | 58.6% | 43.4% | 42.4% | | Tau2 (average over 3) | 76.9% | 68.2% | 42.2% | 24.5% | 16.2% | | HLE no tools | 19.5% | 8.7% | \- | \- | \- | | HLE with search | 26.5% | 17.2% | \- | \- | \- | | BigBench Extra Hard | 74.4% | 64.8% | 33.1% | 21.9% | 19.3% | | MMMLU | 88.4% | 86.3% | 76.6% | 67.4% | 70.7% | | Vision | | | | | | | MMMU Pro | 76.9% | 73.8% | 52.6% | 44.2% | 49.7% | | OmniDocBench 1.5 (average edit distance, lower is better) | 0.131 | 0.149 | 0.181 | 0.290 | 0.365 | | MATH-Vision | 85.6% | 82.4% | 59.5% | 52.4% | 46.0% | | MedXPertQA MM | 61.3% | 58.1% | 28.7% | 23.5% | \- | | Audio | | | | | | | CoVoST | \- | \- | 35.54 | 33.47 | \- | | FLEURS (lower is better) | \- | \- | 0.08 | 0.09 | \- | | Long Context | | | | | | | MRCR v2 8 needle 128k (average) | 66.4% | 44.1% | 25.4% | 19.1% | 13.5% |

Model information

| Property | E2B | E4B | 31B Dense | | ----------------------------- | ------------------------------------- | ----------------------------------- | ----------- | | Total Parameters | 2.3B effective (5.1B with embeddings) | 4.5B effective (8B with embeddings) | 30.7B | | Layers | 35 | 42 | 60 | | Sliding Window | 512 tokens | 512 tokens | 1024 tokens | | Context Length | 128K tokens | 128K tokens | 256K tokens | | Vocabulary Size | 262K | 262K | 262K | | Supported Modalities | Text, Image, Audio | Text, Image, Audio | Text, Image | | Vision Encoder Parameters | \~150M | \~150M | \~550M | | Audio Encoder Parameters | \~300M | \~300M | No Audio |

Mixture-of-Experts (MoE) Model

| Property | 26B A4B MoE | | ----------------------------- | --------------------------------- | | Total Parameters | 25.2B | | Active Parameters | 3.8B | | Layers | 30 | | Sliding Window | 1024 tokens | | Context Length | 256K tokens | | Vocabulary Size | 262K | | Expert Count | 8 active / 128 total and 1 shared | | Supported Modalities | Text, Image | | Vision Encoder Parameters | \~550M |

Best Practices

For the best performance, use these configurations and best practices:

1. Sampling Parameters

Use the following standardized sampling configuration across all use cases:

`temperature=1.0`
`top_p=0.95`
`top_k=64`

2. Thinking Mode Configuration

Note that Ollama already handles the complexities of the chat template for you.

Compared to Gemma 3, the models use standard `system`, `assistant`, and `user` roles. To properly manage the thinking process, use the following control tokens:

Trigger Thinking: Thinking is enabled by including the `<|think|>` token at the start of the system prompt. To disable thinking, remove the token.
Standard Generation: When thinking is enabled, the model will output its internal reasoning followed by the final answer using this structure:

\[Internal reasoning\]

Disabled Thinking Behavior: For all models except for the E2B and E4B variants, if thinking is disabled, the model will still generate the tags but with an empty thought block:

\[Final answer\]

3. Multi-Turn Conversations

No Thinking Content in History: In multi-turn conversations, the historical model output should only include the final response. Thoughts from previous model turns must not be added before the next user turn begins.

4. Modality order

For optimal performance with multimodal inputs, place image and/or audio content before the text in your prompt.

5. Variable Image Resolution

Aside from variable aspect ratios, Gemma 4 supports variable image resolution through a configurable visual token budget, which controls how many tokens are used to represent an image. A higher token budget preserves more visual detail

at the cost of additional compute, while a lower budget enables faster inference for tasks that don’t require fine-grained understanding.

The supported token budgets are: 70, 140, 280, 560, and 1120.

* Use lower budgets for classification, captioning, or video understanding, where faster inference and processing many frames outweigh fine-grained detail. * Use higher budgets for tasks like OCR, document parsing, or reading small text.

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gemma4

gemma4

Gemma 4 models are designed to deliver frontier-level performance at each size. They are well-suited for reasoning, agentic workflows, coding, and multimodal understanding.

Applications

Models

Readme

Models

Benchmark Results

Model information

Mixture-of-Experts (MoE) Model

Best Practices

1. Sampling Parameters

2. Thinking Mode Configuration

3. Multi-Turn Conversations

4. Modality order

5. Variable Image Resolution

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