Compare model versions

Diff

Before

library_name: transformers
license: apache-2.0
license_link: https://ai.google.dev/gemma/docs/gemma_4_license
pipeline_tag: image-text-to-text
base_model: google/gemma-4-26B-A4B-it

After

base_model: google/gemma-4-26B-A4B-it
base_model_relation: quantized
library_name: transformers
license: apache-2.0
license_link: https://ai.google.dev/gemma/docs/gemma_4_license
pipeline_tag: image-text-to-text

Diff

Before

Gemma 4 models are designed to deliver frontier-level performance at each size, targeting deployment scenarios from mobile and edge devices (E2B, E4B) to consumer GPUs and workstations (26B A4B, 31B). They are well-suited for reasoning, agentic workflows, coding, and multimodal understanding.

The models employ a hybrid attention mechanism that interleaves local sliding window attention with full global attention, ensuring the final layer is always global. This hybrid design delivers the processing speed and low memory footprint of a lightweight model without sacrificing the deep awareness required for complex, long-context tasks. To optimize memory for long contexts, global layers feature unified Keys and Values, and apply Proportional RoPE (p-RoPE). 

After

Gemma 4 models are designed to deliver frontier-level performance at each size, targeting deployment scenarios from mobile and edge devices (E2B, E4B) to consumer GPUs and workstations (12B, 26B A4B, 31B). They are well-suited for reasoning, agentic workflows, coding, and multimodal understanding.

The models employ a hybrid attention mechanism that interleaves local sliding window attention with full global attention, ensuring the final layer is always global. This hybrid design delivers the processing speed and low memory footprint of a lightweight model without sacrificing the deep awareness required for complex, long-context tasks. To optimize memory for long contexts, global layers feature unified Keys and Values, and apply Proportional RoPE (p-RoPE). 

Diff

Before

Multimodal models (capable of processing vision, language, and/or audio) have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development.

* **Content Creation and Communication**  
  * **Text Generation**: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts.  
  * **Chatbots and Conversational AI**: Power conversational interfaces for customer service, virtual assistants, or interactive applications.  
  * **Text Summarization**: Generate concise summaries of a text corpus, research papers, or reports.  
  * **Image Data Extraction**: These models can be used to extract, interpret, and summarize visual data for text communications.  
  * **Audio Processing and Interaction**: The smaller models (E2B and E4B) can analyze and interpret audio inputs, enabling voice-driven interactions and transcriptions.  
* **Research and Education**  
  * **Natural Language Processing (NLP) and VLM Research**: These models can serve as a foundation for researchers to experiment with VLM and NLP techniques, develop algorithms, and contribute to the advancement of the field.  
  * **Language Learning Tools**: Support interactive language learning experiences, aiding in grammar correction or providing writing practice.  
  * **Knowledge Exploration**: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics.

After

Multimodal models (capable of processing vision, language, and/or audio) have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development.

* **Content Creation and Communication**  
  * **Text Generation**: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts.  
  * **Chatbots and Conversational AI**: Power conversational interfaces for customer service, virtual assistants, or interactive applications.  
  * **Text Summarization**: Generate concise summaries of a text corpus, research papers, or reports.  
  * **Image Data Extraction**: These models can be used to extract, interpret, and summarize visual data for text communications.  
  * **Audio Processing and Interaction**: The E2B, E4B, and 12B models can analyze and interpret audio inputs, enabling voice-driven interactions and transcriptions.  
* **Research and Education**  
  * **Natural Language Processing (NLP) and VLM Research**: These models can serve as a foundation for researchers to experiment with VLM and NLP techniques, develop algorithms, and contribute to the advancement of the field.  
  * **Language Learning Tools**: Support interactive language learning experiences, aiding in grammar correction or providing writing practice.  
  * **Knowledge Exploration**: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics.

Diff

Before

<div align="center">
  <img src=https://ai.google.dev/gemma/images/gemma4_banner.png>
</div>


<p align="center">
    <a href="https://huggingface.co/collections/google/gemma-4" target="_blank">Hugging Face</a> |
    <a href="https://github.com/google-gemma" target="_blank">GitHub</a> |
    <a href="https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/" target="_blank">Launch Blog</a> |
    <a href="https://ai.google.dev/gemma/docs/core" target="_blank">Documentation</a>
    <br>
    <b>License</b>: <a href="https://ai.google.dev/gemma/docs/gemma_4_license" target="_blank">Apache 2.0</a> | <b>Authors</b>: <a href="https://deepmind.google/models/gemma/" target="_blank">Google DeepMind</a>
</p>

Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on small models) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages. 

Featuring both Dense and Mixture-of-Experts (MoE) architectures, Gemma 4 is well-suited for tasks like text generation, coding, and reasoning. The models are available in four distinct sizes: **E2B**, **E4B**, **26B A4B**, and **31B**. Their diverse sizes make them deployable in environments ranging from high-end phones to laptops and servers, democratizing access to state-of-the-art AI.

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), Video, and Audio (featured natively on the E2B and E4B 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.

After

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<div align="left">
  <table style="border-collapse:collapse; border:none;">
    <tr style="border:none;">
      <td align="left" style="border:none; padding:4px 24px 4px 0;"><b>Website</b></td>
      <td align="left" style="border:none; padding:4px 0;"><a href="https://cyan.kiwi/" target="_blank">cyan.kiwi</a></td>
    </tr>
    <tr style="border:none;">
      <td align="left" style="border:none; padding:4px 24px 4px 0;"><b>Models</b></td>
      <td align="left" style="border:none; padding:4px 0;"><a href="https://huggingface.co/cyankiwi" target="_blank">Hugging Face</a> · <a href="https://www.modelscope.ai/organization/cyankiwi" target="_blank">ModelScope</a></td>
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      <td align="left" style="border:none; padding:4px 24px 4px 0;"><b>Connect</b></td>
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<br>

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<div align="left">
  <table style="border-collapse:collapse; border:none;">
    <tr style="border:none;">
      <td align="left" style="border:none; padding:4px 24px 4px 0;"><b>Version</b></td>
      <td align="left" style="border:none; padding:4px 0;"><a href="https://github.com/vllm-project/llm-compressor" target="_blank">llm-compressor</a></td>
    </tr>
    <tr style="border:none;">
      <td align="left" style="border:none; padding:4px 24px 4px 0;"><b>Calibration</b></td>
      <td align="left" style="border:none; padding:4px 0;"><a href="https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset" target="_blank">nvidia/Llama-Nemotron-Post-Training-Dataset</a></td>
    </tr>
    <tr style="border:none;">
      <td align="left" style="border:none; padding:4px 24px 4px 0;"><b>Model Size</b></td>
      <td align="left" style="border:none; padding:4px 0;">29.16 GB</td>
    </tr>
  </table>
</div>

<br>

<p align="left">
  <a href="https://cyan.kiwi/workspace?prompt=Deploy%20cyankiwi/gemma-4-26B-A4B-it-AWQ-8bit" target="_blank"><img src="https://storage.googleapis.com/cyankiwi-assets/model-card/model-card-button-deploy.png" width="333" alt="Deploy on cyan.kiwi private endpoints"></a>
</p>

---

<div align="center">
  <img src=https://ai.google.dev/gemma/images/gemma4_banner.png>
</div>


<p align="center">
    <a href="https://huggingface.co/collections/google/gemma-4" target="_blank">Hugging Face</a> |
    <a href="https://github.com/google-gemma" target="_blank">GitHub</a> |
    <a href="https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/" target="_blank">Launch Blog</a> |
    <a href="https://ai.google.dev/gemma/docs/core" target="_blank">Documentation</a> |
    <a href="https://arxiv.org/abs/2607.02770" target="_blank">Technical Report</a>
    <br>
    <b>License</b>: <a href="https://ai.google.dev/gemma/docs/gemma_4_license" target="_blank">Apache 2.0</a> | <b>Authors</b>: <a href="https://deepmind.google/models/gemma/" target="_blank">Google DeepMind</a>
</p>

Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages. 

Featuring both Dense and Mixture-of-Experts (MoE) architectures, Gemma 4 is well-suited for tasks like text generation, coding, and reasoning. The models are available in five distinct sizes: **E2B**, **E4B**, **12B**, **26B A4B**, and **31B**. Their diverse sizes make them deployable in environments ranging from high-end phones to laptops and servers, democratizing access to state-of-the-art AI.

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), Video, and Audio (featured natively on the E2B, E4B, and 12B 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.

6 more lines not shown.

Diff

Before

| 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 |

The "E" in E2B and E4B stands for "effective" parameters. The smaller models incorporate Per-Layer Embeddings (PLE) to maximize parameter efficiency in on-device deployments. Rather than adding more layers or parameters to the model, PLE gives each decoder layer its own small embedding for every token. These embedding tables are large but are only used for quick lookups, which is why the effective parameter count is much smaller than the total.

After

| Property | E2B | E4B | 12B Unified | 31B Dense |
| :---- | :---- | :---- | :---- | :---- |
| **Total Parameters** | 2.3B effective <br> (5.1B with embeddings) | 4.5B effective <br> (8B with embeddings) | 11.95B | 30.7B |
| **Layers** | 35 | 42 | 48 | 60 |
| **Sliding Window** | 512 tokens | 512 tokens | 1024 tokens | 1024 tokens |
| **Context Length** | 128K tokens | 128K tokens | 256K tokens  | 256K tokens  |
| **Vocabulary Size** | 262K | 262K | 262K | 262K |
| **Supported Modalities** | Text, Image, Audio | Text, Image, Audio | Text, Image, Audio | Text, Image |
| **Vision Encoder Parameters** | *~150M* | *~150M* | - | *~550M* |
| **Audio Encoder Parameters** | *~300M* | *~300M* | - | No Audio |

The "E" in E2B and E4B stands for "effective" parameters. The smaller models incorporate Per-Layer Embeddings (PLE) to maximize parameter efficiency in on-device deployments. Rather than adding more layers or parameters to the model, PLE gives each decoder layer its own small embedding for every token. These embedding tables are large but are only used for quick lookups, which is why the effective parameter count is much smaller than the total.

The "Unified" in Gemma 4 12B Unified refers to its encoder-free architecture. Other Gemma 4 models use dedicated encoders to process multimodal data before passing it to the LLM. Gemma 4 12B eliminates these encoders entirely, projecting raw image patches and audio waveforms directly into the LLM's embedding space through lightweight linear layers. This unified approach means all modalities flow straight into a single decoder-only transformer, reducing multimodal latency and allowing the entire model to be fine-tuned in one pass.

Diff

Before

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% |

After

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 12B Unified | Gemma 4  E4B | Gemma 4  E2B | Gemma 3  27B (no think) |
| :---- | :---- | :---- | :---- | :---- | :---- | :---- |
| MMLU Pro | 85.2% | 82.6% | 77.2% | 69.4% | 60.0% | 67.6% |
| AIME 2026 no tools | 89.2% | 88.3% | 77.5% | 42.5% | 37.5% | 20.8% |
| LiveCodeBench v6 | 80.0% | 77.1% | 72.0% | 52.0% | 44.0% | 29.1% |
| Codeforces ELO | 2150 | 1718 | 1659 | 940 | 633 | 110 |
| GPQA Diamond | 84.3% | 82.3% | 78.8% | 58.6% | 43.4% | 42.4% |
| Tau2 (average over 3) | 76.9% | 68.2% | 69.0% | 42.2% | 24.5% | 16.2% |
| HLE no tools | 19.5% | 8.7% | 5.2% | - | - | - |
| HLE with search | 26.5% | 17.2% | - | - | - | - |
| BigBench Extra Hard | 74.4% | 64.8% | 53.0% | 33.1% | 21.9% | 19.3% |
| MMMLU | 88.4% | 86.3% | 83.4% | 76.6% | 67.4% | 70.7% |
| **Vision** |  |  |  |  |  |  |
| MMMU Pro | 76.9% | 73.8% | 69.1% | 52.6% | 44.2% | 49.7% |
| OmniDocBench 1.5 (average edit distance, lower is better) | 0.131 | 0.149 | 0.164 | 0.181 | 0.290 | 0.365 |
| MATH-Vision | 85.6% | 82.4% | 79.7% | 59.5% | 52.4% | 46.0% |
| MedXPertQA MM | 61.3% | 58.1% | 48.7% | 28.7% | 23.5% | - |
| **Audio** |  |  |  |  |  |  |
| CoVoST | - | - | 38.5<sup>*</sup> | 35.54 | 33.47 | - |
| FLEURS (lower is better) | - | - | 0.069<sup>*</sup> | 0.08 | 0.09 | - |
| **Long Context** |  |  |  |  |  |  |
| MRCR v2 8 needle 128k (average) | 66.4% | 44.1%  | 43.4% | 25.4% | 19.1% | 13.5% |

<sup>*</sup>Excluding Chinese language.

Diff

Before

Gemma 4 models handle a broad range of tasks across text, vision, and audio. Key capabilities include:

* **Thinking** – Built-in reasoning mode that lets the model think step-by-step before answering.
* **Long Context** – Context windows of up to 128K tokens (E2B/E4B) and 256K tokens (26B A4B/31B).
* **Image Understanding** – Object detection, Document/PDF parsing, screen and UI understanding, chart comprehension, OCR (including multilingual), handwriting recognition, and pointing. Images can be processed at variable aspect ratios and resolutions.
* **Video Understanding** – Analyze video by processing sequences of frames.
* **Interleaved Multimodal Input** – Freely mix text and images in any order within a single prompt.
* **Function Calling** – Native support for structured tool use, enabling agentic workflows.
* **Coding** – Code generation, completion, and correction.
* **Multilingual** – Out-of-the-box support for 35+ languages, pre-trained on 140+ languages.
* **Audio** (E2B and E4B only) – Automatic speech recognition (ASR) and speech-to-translated-text translation across multiple languages.

After

Gemma 4 models handle a broad range of tasks across text, vision, and audio. Key capabilities include:

* **Thinking** – Built-in reasoning mode that lets the model think step-by-step before answering.
* **Long Context** – Context windows of up to 128K tokens (E2B/E4B) and 256K tokens (12B, 26B A4B/31B).
* **Image Understanding** – Object detection, Document/PDF parsing, screen and UI understanding, chart comprehension, OCR (including multilingual), handwriting recognition, and pointing. Images can be processed at variable aspect ratios and resolutions.
* **Video Understanding** – Analyze video by processing sequences of frames.
* **Interleaved Multimodal Input** – Freely mix text and images in any order within a single prompt.
* **Function Calling** – Native support for structured tool use, enabling agentic workflows.
* **Coding** – Code generation, completion, and correction.
* **Multilingual** – Out-of-the-box support for 35+ languages, pre-trained on 140+ languages.
* **Audio** (E2B, E4B, and 12B only) – Automatic speech recognition (ASR) and speech-to-translated-text translation across multiple languages.

Diff

Before

You can use all Gemma 4 models with the latest version of Transformers. To get started, install the necessary dependencies in your environment:

`pip install -U transformers torch accelerate`

Once you have everything installed, you can proceed to load the model with the code below:

```python
from transformers import AutoProcessor, AutoModelForCausalLM

MODEL_ID = "google/gemma-4-26B-A4B-it"

# Load model
processor = AutoProcessor.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    dtype="auto",
    device_map="auto"
)
```

Once the model is loaded, you can start generating output:

```python
# Prompt
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Write a short joke about saving RAM."},
]

# Process input
text = processor.apply_chat_template(
    messages, 
    tokenize=False, 
    add_generation_prompt=True, 
    enable_thinking=False
)
inputs = processor(text=text, return_tensors="pt").to(model.device)
input_len = inputs["input_ids"].shape[-1]

# Generate output
outputs = model.generate(**inputs, max_new_tokens=1024)
response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)

# Parse output
processor.parse_response(response)
```

To enable reasoning, set `enable_thinking=True` and the `parse_response` function will take care of parsing the thinking output.

Below, you will also find snippets for processing audio (E2B and E4B only), images, and video alongside text:

<details>
<summary>Code for processing Audio</summary>

Instead of using `AutoModelForCausalLM`, you can use `AutoModelForMultimodalLM` to process audio. To use it, make sure to install the following packages:


`pip install -U transformers torch librosa accelerate`

You can then load the model with the code below:

```python
from transformers import AutoProcessor, AutoModelForMultimodalLM

MODEL_ID = "google/gemma-4-E2B-it"

# Load model
processor = AutoProcessor.from_pretrained(MODEL_ID)
model = AutoModelForMultimodalLM.from_pretrained(
    MODEL_ID, 
    dtype="auto", 
    device_map="auto"
)
```

Once the model is loaded, you can start generating output by directly referencing the audio URL in the prompt:


```python
# Prompt - add audio before text

146 more lines not shown.

After

You can use all Gemma 4 models with the latest version of Transformers. To get started, install the necessary dependencies in your environment:

`pip install -U transformers torch accelerate`

Once you have everything installed, you can proceed to load the model with the code below:

```python
from transformers import AutoProcessor, AutoModelForMultimodalLM

MODEL_ID = "google/gemma-4-26B-A4B-it"

# Load model
processor = AutoProcessor.from_pretrained(MODEL_ID)
model = AutoModelForMultimodalLM.from_pretrained(
    MODEL_ID,
    dtype="auto",
    device_map="auto"
)
```

Once the model is loaded, you can start generating output:

```python
# Prompt
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Write a short joke about saving RAM."},
]

# Process input
inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
    add_generation_prompt=True,
    enable_thinking=False
).to(model.device)
input_len = inputs["input_ids"].shape[-1]

# Generate output
outputs = model.generate(**inputs, max_new_tokens=1024)
response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)

# Parse output
processor.parse_response(response, prefix=inputs["input_ids"])
```

To enable reasoning, set `enable_thinking=True` and the `parse_response` function will take care of parsing the thinking output.

Below, you will also find snippets for processing audio (E2B, E4B, 12B only), images, and video alongside text:

<details>
<summary>Code for processing Audio</summary>

Make sure to install the following packages:

`pip install -U transformers torch torchvision librosa accelerate`

You can then load the model with the code below:

```python
from transformers import AutoProcessor, AutoModelForMultimodalLM

MODEL_ID = "google/gemma-4-E2B-it"

# Load model
processor = AutoProcessor.from_pretrained(MODEL_ID)
model = AutoModelForMultimodalLM.from_pretrained(
    MODEL_ID, 
    dtype="auto", 
    device_map="auto"
)
```

Once the model is loaded, you can start generating output by directly referencing the audio URL in the prompt:


```python
# Prompt - add audio after text

146 more lines not shown.

Diff

Before

* **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.

After

* **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, with the exception of tool call turns where thinking content should be preserved.

Diff

Before

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

After

For optimal performance with multimodal inputs, place:

* Image content **before** the text in your prompt.
* Audio content **after** the text in your prompt.

Diff

Before

All models support image inputs and can process videos as frames whereas the E2B and E4B models also support audio inputs. Audio supports a maximum length of 30 seconds. Video supports a maximum of 60 seconds assuming the images are processed at one frame per second.

After

All models support image inputs and can process videos as frames whereas the E2B, E4B, and 12B models also support audio inputs. Audio supports a maximum length of 30 seconds. Video supports a maximum of 60 seconds assuming the images are processed at one frame per second.

Diff

Before

Not present in the earlier snapshot.

After

If you find our work helpful, please consider citing it:

```bibtex
@misc{gemmateam2026gemma4,
      title={Gemma 4 Technical Report}, 
      author={Gemma Team},
      year={2026},
      eprint={2607.02770},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2607.02770}, 
}
```