Gemma 3 27B

Gemma 3 27B

Description of the Gemma 3 27B neural network

Gemma 3 27B is a multimodal language model with open weights developed by Google. The model has 27 billion parameters and can process both textual information and images while generating text responses. Thanks to its 128,000-token context window, Gemma 3 27B can retain large volumes of data in memory, which is especially important for working with long documents, multi-step reasoning, and complex dialogues.

The model was trained on 14 trillion tokens and released on March 12, 2025. The open weights allow developers to integrate Gemma 3 27B into their own applications, fine-tune it for specific tasks, and deploy it on their own infrastructure. The model works with many languages, making it suitable for international projects.

Gemma 3 27B Specifications

Characteristic	Value
Type	Multimodal language model
Developer	Google
Parameters	27.0B
Context window	131.1K tokens
Release date	March 12, 2025
License	gemma
Trained on	14.0T tokens
Average score	65.4%
Supported features	Function Calling, Structured Output, Code Execution, Web Search, Batch Inference, Fine-tuning

Who is the Gemma 3 27B neural network for?

Developers and ML engineers

Gemma 3 27B is primarily aimed at developers who need a powerful open-source language model. Thanks to support for fine-tuning, code execution, and function calling, the model can be embedded into applications for automation, data processing, and creating intelligent assistants. The open weights allow the model to be deployed locally, which is important for projects with data privacy requirements.

Researchers and analysts

Specialists working with large volumes of text and visual information can use the model to summarize documents, extract facts, analyze images, and build logical chains. The 128K-token context window allows processing long reports, scientific articles, and correspondence without losing context.

Teams building chatbots and assistants

The model is suitable for building dialogue systems that work in multiple languages. The ability to process images expands use cases—from describing photos to analyzing charts and diagrams within a conversation.

How to use the Gemma 3 27B neural network?

Via API

Gemma 3 27B is available through an API with pay-per-token pricing. Input processing costs $0.11 per 1 million tokens, and output generation costs $0.20 per 1 million tokens. This format suits those who do not want to manage their own infrastructure.

Local deployment

Thanks to the open weights, the model can be downloaded and run on your own hardware. This provides full control over data and allows fine-tuning the model for specific tasks. Working with 27 billion parameters requires significant computing resources, especially when using the maximum context window.

Batch processing and fine-tuning

The model supports batch inference, which is convenient for processing large data sets. The fine-tuning feature makes it possible to adapt the model's behavior to a specific domain, corporate style, or unique use cases.

Key features of Gemma 3 27B

Multimodality

Gemma 3 27B accepts both text and images as input. Based on the visual data received, the model generates a text description, answers questions about the image's content, or performs image analysis. This expands the model's application beyond purely text-based tasks.

128K-token context window

Support for a large context window allows the model to retain long conversations, large documents, or interaction history in memory. This is especially valuable when analyzing multi-volume reports, legal texts, or conducting extensive consultations.

Function Calling and Structured Output

The model can call external functions, allowing it to integrate with databases, third-party APIs, and automation tools. Structured output simplifies integration—the model can return data in a specified format (JSON, tables) rather than only as free-form text.

Code execution and web search

Gemma 3 27B supports code execution, which is useful for programming, debugging, or data analysis tasks. The web search capability allows the model to access up-to-date information from the internet when preparing answers.

Advantages of Gemma 3 27B

Open weights

One of the model's main advantages is its open weights. Developers can freely download, use, and fine-tune the model without being tied to a specific cloud provider. This provides flexibility in deployment and data control.

Multilingualism and multimodality

The model works with many languages and processes images, making it a versatile tool for international projects and tasks involving visual content.

Large context window

128,000 tokens of context is one of the highest figures among models of similar size. This allows the model to work effectively with long texts and complex dialogues without losing coherence.

Disadvantages of Gemma 3 27B

High resource requirements

The model with 27 billion parameters requires significant computing power for local deployment. Not every team has the hardware capable of providing acceptable inference speed with a fully loaded context window.

Average overall score of 65.4%

Across the combined benchmarks, the model shows an average result of 65.4%, which is lower than some closed commercial alternatives. In a number of scenarios, this may mean additional fine-tuning is needed or more performant solutions should be used.

Gemma license restrictions

The model is distributed under the gemma license, which imposes certain conditions on commercial use, fine-tuning, and distribution. Before integrating it into a commercial product, you must review the full license text.

What tasks does Gemma 3 27B solve?

Question answering and consultations

The model is suitable for building question-answering systems that can handle queries in different languages, including attached images for context.

Text summarization

Thanks to its large context window, Gemma 3 27B can compress lengthy documents, scientific articles, and reports into concise, meaningful summaries while preserving key facts and the logical flow.

Logical reasoning and analysis

The model handles tasks that require multi-step reasoning, drawing conclusions from provided data, and testing hypotheses. This is useful in analytics, research, and educational applications.

Image understanding

Gemma 3 27B analyzes images: it recognizes objects, scenes, and text in pictures and answers questions about visual content. This opens up scenarios such as describing photos, checking documents, or assisting people with visual impairments.

Gemma 3 27B Pricing

The model is distributed under a freemium model. Access via API is billed as follows:

• Input tokens: $0.11 per 1 million tokens.
• Output tokens: $0.20 per 1 million tokens.

When deploying locally using the open weights, costs consist of computing resources (GPU/TPU), electricity, and infrastructure maintenance.

Terms of Use for Gemma 3 27B

The model is distributed under the gemma license. This means that use, copying, modification, and distribution are permitted within the terms established by Google. Developers need to review the official license text before commercial use or fine-tuning. The license may specify restrictions on use in certain areas, attribution requirements, and rules for distributing derivative models.

Availability of Gemma 3 27B

The model was released on March 12, 2025. It is available for download with open weights, allowing it to be used both on your own servers and in cloud environments. In addition, Gemma 3 27B can be accessed via API from providers that support this model. The model is considered free in terms of distribution—basic access does not require purchasing a subscription, but API usage is billed separately.

How Gemma 3 27B differs from alternatives

Comparison with Gemma 3 12B and Gemma 2 27B

Compared with the smaller Gemma 3 12B, the 27-billion-parameter version offers significantly greater accuracy, deeper contextual understanding, and the ability to process both text and images. The new version differs from Gemma 2 27B in its multimodality (the second version worked with text only) and a larger context window.

Difference from Gemini 1.5 Flash and Gemini 2.0 Flash

Google's Gemini models are closed and available only through cloud APIs. Gemma 3 27B, by contrast, has open weights, making it possible to deploy locally and fine-tune. At the same time, Gemini commercial models often achieve higher benchmark results, but they do not offer the same level of control and flexibility.

Difference from GPT OSS 20B

GPT OSS 20B is another open-weight model, but it is smaller in size (20 billion parameters versus 27 billion), does not support multimodality, and has a more modest context window. Gemma 3 27B wins thanks to a larger number of parameters, image processing capability, and support for advanced features such as Function Calling and Code Execution.

Conclusion

Gemma 3 27B is a multimodal language model from Google with 27 billion parameters, open weights, and a 128,000-token context window. It is suitable for a wide range of tasks: from building chatbots and summarizing texts to image analysis and logical reasoning. The model is available both through a paid API and for local deployment, giving developers flexibility in choosing infrastructure. Despite high computing resource requirements and average results in general benchmarks, Gemma 3 27B is one of the most functional solutions in the open multimodal model segment.