T5Gemma 2: Next-Gen Encoder-Decoder Models

Introduction

Google has announced T5Gemma 2, the next evolution of their encoder-decoder model family based on Gemma 3. Released on December 18, 2025, T5Gemma 2 represents a significant advancement in encoder-decoder transformer architectures, introducing the first multimodal and long-context encoder-decoder models in the Gemma family.

Unlike its predecessor, T5Gemma 2 incorporates significant architectural innovations while inheriting powerful features from the Gemma 3 family. The release includes compact pre-trained models at three sizes: 270M-270M (~370M total, excluding vision encoder), 1B-1B (~1.7B), and 4B-4B (~7B) parameters, making them ideal for rapid experimentation and deployment in on-device applications.

This announcement builds on the success of the original T5Gemma, which demonstrated that modern pre-trained decoder-only models could be successfully adapted into encoder-decoder architectures, unlocking new versatility while bypassing the computational cost of training from scratch.

Architectural Innovations for Efficiency

T5Gemma 2 introduces key structural refinements designed to maximize efficiency at smaller scales:

Tied Embeddings

Tied word embeddings between the encoder and decoder significantly reduce the overall parameter count. This innovation allows the models to pack more active capabilities into the same memory footprint, which is particularly crucial for the new compact 270M-270M model. By sharing embedding weights between encoder and decoder, T5Gemma 2 achieves better parameter efficiency without sacrificing performance.

Merged Attention

In the decoder, T5Gemma 2 adopts a merged attention mechanism, combining self-attention and cross-attention into a single, unified attention layer. This architectural change:

Reduces model parameters
Simplifies architectural complexity
Improves model parallelization
Benefits inference speed and efficiency

The merged attention approach streamlines the decoder architecture while maintaining the model's ability to attend to both its own previous outputs (self-attention) and the encoder's representations (cross-attention).

Next-Generation Capabilities

Drawing from Gemma 3, T5Gemma 2 represents a significant upgrade in model capabilities across three key areas:

Multimodality

T5Gemma 2 models can understand and process images alongside text, making them the first multimodal encoder-decoder models in the Gemma family. By utilizing a highly efficient vision encoder, the models can seamlessly perform:

Visual question answering: Understanding images and answering questions about them
Multimodal reasoning: Combining visual and textual information for complex reasoning tasks
Image-text understanding: Processing and generating text based on visual inputs

This multimodal capability opens up new possibilities for applications that require understanding both visual and textual information simultaneously.

Extended Long Context

T5Gemma 2 dramatically expands the context window compared to previous encoder-decoder models. Leveraging Gemma 3's alternating local and global attention mechanism, T5Gemma 2 can handle context windows of up to 128K tokens. This extended context capability makes the models suitable for:

Long document processing and summarization
Complex multi-turn conversations
Analysis of lengthy research papers and technical documents
Processing extensive codebases and documentation

The separate encoder architecture makes T5Gemma 2 particularly well-suited for handling long-context problems, as the encoder can efficiently process the entire input sequence before the decoder generates the output.

Massively Multilingual

Trained on a larger, more diverse dataset, T5Gemma 2 now supports over 140 languages out of the box. This multilingual capability makes the models accessible to a global audience and suitable for:

Machine translation across multiple language pairs
Multilingual content generation and understanding
Cross-lingual information retrieval
International application development

Performance Benchmarks

T5Gemma 2 sets a new standard for what compact encoder-decoder models can achieve. The models demonstrate strong performance across key capability areas, inheriting powerful multimodal and long-context features from the Gemma 3 architecture.

Pre-Training Performance

According to Google's benchmarks, T5Gemma 2 delivers:

Strong Multimodal Performance:

Outperforms Gemma 3 on several benchmarks
Successfully adapts text-only Gemma 3 base models (270M and 1B) into effective multimodal encoder-decoder models

Superior Long-Context Capability:

Substantial quality gains over Gemma 3 and T5Gemma
The separate encoder architecture provides better handling of long-context problems
Can handle context windows of up to 128K tokens

Improved General Capabilities:

Generally surpasses corresponding Gemma 3 counterparts across coding, reasoning, and multilingual tasks

Post-Training Performance

Similar to the original T5Gemma, post-training performance of T5Gemma 2 generally yields better results than its decoder-only counterparts. This makes T5Gemma 2 suitable for both large language model research as well as downstream applications.

The models are designed to be post-trained by developers for specific tasks before deployment, allowing for customization and optimization for particular use cases.

Model Sizes and Use Cases

T5Gemma 2 is available in three compact sizes, each optimized for different deployment scenarios:

270M-270M Model (~370M total)

Use cases: On-device applications, edge computing, mobile devices
Benefits: Smallest footprint, fastest inference, lowest memory requirements
Ideal for: Rapid prototyping, resource-constrained environments

1B-1B Model (~1.7B total)

Use cases: Balanced performance and efficiency
Benefits: Good quality-to-size ratio, suitable for most applications
Ideal for: Production deployments requiring both quality and speed

4B-4B Model (~7B total)

Use cases: High-performance applications, complex reasoning tasks
Benefits: Best quality, still compact compared to larger models
Ideal for: Research, advanced applications, quality-critical use cases

All models exclude the vision encoder in their parameter counts, making them even more efficient when multimodal capabilities are not required.

Comparison with Previous Models

vs. Original T5Gemma

Key Improvements:

Multimodal capabilities: First encoder-decoder models in Gemma family with vision understanding
Extended context: 128K token context window vs. previous limitations
Architectural efficiency: Tied embeddings and merged attention reduce parameters
Multilingual support: Over 140 languages vs. previous versions
Performance gains: Better results across multiple benchmarks

Inherited Strengths:

Efficient adaptation from decoder-only models
High-quality pre-training without training from scratch
Inference-efficient architecture

vs. Gemma 3 (Decoder-Only)

Advantages of Encoder-Decoder Architecture:

Better suited for sequence-to-sequence tasks
Superior long-context handling with separate encoder
More efficient for tasks requiring input understanding before generation
Post-training performance generally better than decoder-only counterparts

Trade-offs:

Slightly more complex architecture
May require different fine-tuning approaches
Optimized for specific task types

Availability and Getting Started

T5Gemma 2 pre-trained checkpoints are now available for broad use across several platforms:

Access Points

Available Platforms:

Kaggle: Download models and datasets
Hugging Face: Access models through the Hugging Face Hub
Google Colab: Explore models via interactive notebooks
Vertex AI: Run inference and deploy models at scale
arXiv: Read the research paper

Model Design Philosophy

These pre-trained checkpoints are designed to be post-trained by developers for specific tasks before deployment. This approach:

Allows customization for particular use cases
Enables optimization for specific domains
Provides flexibility in training procedures

Technical Deep Dive

Encoder-Decoder Architecture Benefits

The encoder-decoder architecture provides several advantages over decoder-only models:

Separate Processing:

Encoder can process entire input sequence in parallel
Decoder generates output autoregressively
Better separation of understanding and generation

Efficiency for Sequence-to-Sequence Tasks:

Natural fit for translation, summarization, and similar tasks
Encoder creates rich representations of input
Decoder uses these representations for generation

Long-Context Handling:

Encoder efficiently processes long inputs
Decoder focuses on generation using encoder outputs
Better scalability for extended sequences

Training Approach

T5Gemma 2 follows the successful approach of the original T5Gemma, which demonstrated that modern pre-trained decoder-only models could be successfully adapted into encoder-decoder architectures. By initializing with weights from a powerful decoder-only model and then applying continued pre-training, T5Gemma 2 achieves high-quality, inference-efficient models while bypassing the computational cost of training from scratch.

Use Cases and Applications

T5Gemma 2's combination of multimodal capabilities, long-context understanding, and efficient architecture makes it suitable for various applications:

Multimodal Applications

Visual question answering: Answering questions about images
Multimodal reasoning: Combining visual and textual information for complex reasoning tasks

Long-Context Applications

Document summarization: Summarizing lengthy documents and research papers
Code analysis: Understanding and processing large codebases
Long-form content generation: Creating content based on extensive context
Research assistance: Analyzing and synthesizing information from multiple sources

Multilingual Applications

Machine translation: Translating between 140+ languages
Cross-lingual information retrieval: Finding information across languages
Multilingual content generation: Creating content in multiple languages
International applications: Building apps for global audiences

On-Device Applications

Mobile AI: Running on smartphones and tablets
Edge computing: Deploying on resource-constrained devices
Offline capabilities: Functioning without constant internet connection
Privacy-sensitive applications: Processing data locally

Future Implications

Encoder-Decoder Renaissance

T5Gemma 2 represents a renewed interest in encoder-decoder architectures, demonstrating that:

Modern encoder-decoder models can compete with decoder-only models
Architectural innovations can improve efficiency significantly
Multimodal capabilities work well in encoder-decoder frameworks
Long-context processing benefits from separate encoder architecture

Efficiency Trends

The architectural innovations in T5Gemma 2 reflect broader trends in AI model development:

Parameter Efficiency:

Tied embeddings reduce parameters without sacrificing quality
Merged attention simplifies architecture while maintaining performance
Compact models can achieve strong results

On-Device AI:

Smaller models enable on-device deployment
Efficient architectures support edge computing
Privacy and latency benefits of local processing

Multimodal Evolution

T5Gemma 2's multimodal capabilities show that:

Encoder-decoder architectures excel at multimodal tasks
Vision encoders can be efficiently integrated
Text and image understanding can be unified effectively
Multimodal reasoning benefits from separate encoding and decoding

Conclusion

T5Gemma 2 represents a significant milestone in encoder-decoder model development, introducing the first multimodal and long-context encoder-decoder models in the Gemma family. By combining architectural innovations like tied embeddings and merged attention with powerful capabilities from Gemma 3, Google has created a family of models that are both efficient and capable.

Key Achievements

First multimodal encoder-decoder models in the Gemma family with vision understanding
128K token context window for extended long-context processing
Over 140 languages supported out of the box
Architectural efficiency through tied embeddings and merged attention
Strong performance across multimodal, long-context, and general capabilities
Compact sizes (270M-270M, 1B-1B, 4B-4B) ideal for on-device deployment

What This Means

For developers, T5Gemma 2 offers efficient encoder-decoder models that excel at sequence-to-sequence tasks, multimodal understanding, and long-context processing. The compact sizes make these models suitable for on-device deployment, while the strong performance makes them competitive with larger models.

For researchers, T5Gemma 2 demonstrates that encoder-decoder architectures can be modernized with innovations from decoder-only models, achieving strong results across multiple domains. The architectural improvements show how efficiency can be improved without sacrificing capability.

For the industry, T5Gemma 2 shows that encoder-decoder models remain relevant and can be enhanced with modern techniques. The combination of efficiency, capability, and accessibility makes these models suitable for a wide range of applications, from on-device AI to large-scale production deployments.

The release of T5Gemma 2, alongside the broader Gemma 3 family, provides developers and researchers with a comprehensive set of tools for building AI applications. Whether you need multimodal understanding, long-context processing, or efficient on-device deployment, T5Gemma 2 offers a compelling solution that balances performance, efficiency, and accessibility.

As AI continues to evolve, models like T5Gemma 2 that combine architectural innovation with practical capabilities will play a crucial role in making advanced AI accessible to everyone, from researchers exploring new possibilities to developers building production applications.

Sources

Interested in learning more about AI models and architectures? Explore our AI models section, check out our glossary of AI terms including transformer architectures, or discover other AI tools in our comprehensive catalog.