Sber has introduced a major update to its AI assistant GigaChat, which is now powered by the new flagship model GigaChat Ultra. This updated version allows the AI assistant to remember facts about users for personalized communication and solutions, search information online independently, and generate text responses twice as fast.
The release of this new model opens up opportunities not only for end users but also for developers who can build applied AI products and services using GigaChat Ultra. Users can run code directly within the interface and receive answers to questions about their own capabilities, relying on current documentation.
Anton Frolov, senior vice president, head of Generative AI Development, Sberbank:
“We are taking a step from being just an answer-giving tool to becoming a multi-agent AI assistant. But our goal goes beyond that: we are creating a future where traditional mobile apps give way to neural-network-based interfaces. Needed features will appear upon request, making navigation through the digital world seamless. GigaChat Ultra is one of the world’s largest models fully developed and trained in Russia. It remembers your preferences, works faster, understands tasks more deeply, and provides higher-quality recommendations. We are removing the last barriers in human-to-machine interaction.”
Long-term memory
One key innovation is long-term memory. While contextual (short-term) memory is limited to a single conversation session and resets when it ends, GigaChat’s long-term memory operates differently—it retains user-specific facts across sessions and uses them in subsequent conversations.
Here’s what exactly GigaChat remembers:
- hobbies, tastes, and interests;
- profession, education, life goals, and habits;
- personal data—only to the extent shared by users themselves;
- information about family members and pets.
The system automatically identifies significant facts without overloading memory with trivialities such as short-term plans or widely known general knowledge. All data is stored in a unified profile synchronized between web versions, mobile applications, and Telegram bot via the Sber ID sign-in. Users have full control over this feature: memory can be enabled or disabled anytime in settings.
Response generation speed doubled
GigaChat generates textual responses twice as quickly compared to Sber’s previous flagship model. This directly affects how fast users see replies even for complex queries requiring detailed reasoning—the result appears almost instantly.
This increase in speed was achieved thanks to the Mixture of Experts (MoE) architecture. The model acts like a team of specialized experts, each handling specific types of tasks. Only relevant “experts” respond to any given query rather than the entire model working simultaneously.
Real-time conversation mode
GigaChat now autonomously connects to internet searches for real-time updates, eliminating the need for users to manually enable this option. This ensures accurate responses when discussing recent news items, stock quotes, and other dynamically changing data. Search functionality includes a dedicated rephraser—a system that reformulates user queries to enhance relevance and improve final response quality.
Online searching is now also available in the voice-communication mode. Dialogues have become truly interactive: users can interrupt the model, clarify details, or change topics instantaneously—with no delay in processing context shifts. After completing a chat session, a complete transcript of the dialogue is saved.
Self-awareness: GigaChat knows everything about itself
A self-awareness mechanism has been implemented in GigaChat, enabling the model to provide correct answers regarding its own characteristics. When responding to these kinds of questions, the model refers to up-to-date documentation describing its current version, supported functionalities, limitations, and behavioral peculiarities. This eliminates typical issues common among language models, such as providing incorrect or outdated information about their abilities—for example, falsely claiming nonexistent features or failing to recognize existing ones.
Code interpreter: GigaChat as an analytical environment
An integrated code interpreter transforms GigaChat into an isolated execution environment for running software code right inside the assistant’s interface. Before introducing this function, the model could merely write code and display it to users; executing and testing results required external tools. Now, GigaChat generates code and executes it immediately within a secure sandbox, without affecting the user’s system.
The interpreter supports uploaded files, performs advanced numerical calculations, validates data structures, and creates graphs and charts directly in chats. This makes GigaChat a comprehensive analytical tool suitable for reports, tables, and large datasets.
The training process
The training involved three stages. Initially, the scope of knowledge was expanded by adding academic books, materials related to mathematics and programming, increasing multilingual data volumes—now covering ten languages. In the intermediate stage, specialized skills were enhanced: the code corpus was enlarged, additional data included physics, medicine, finance, records of actual dialogs, and security measures strengthened. Final tuning based on examples (editor texts, dialogs triggering functions, system prompts) ensured stable performance under real-world conditions.
Significant improvements were recorded in open-ended and closed-ended question answering, along with tasks demanding sophisticated logical reasoning. Benchmark tests for Russian-language use demonstrated high levels of grammatical correctness, natural speech flow, readability, and structured responses. Enhancements also extended to practical industry scenarios: the model became more adept at legal, cybersecurity, medical, financial, and trade-related tasks—especially those involving Russian-specific nuances and sectoral terminology. Notable progress was made in mathematical computations and code generation, broadening applicability in fintech, education, and development sectors.
Flagship model to be released publicly
Sber make the source code and weights of its flagship GigaChat Ultra model freely accessible. According to company experts’ assessments, it already outperforms DeepSeek V3.1, Qwen3-235B and its predecessor GigaChat 2 Max in Russian-language tasks, maths and general reasoning. By releasing the repository, organizations ranging from large banks to small startups will gain the ability to install the neural network within their private environments and adapt it to corporate data, marking a move toward genuine technological sovereignty.
Users can try the updated model free of charge in the web version, Android apps available in RuStore and AppGallery, as well as in the Telegram bot and MAX messenger. To activate voice mode and memory, simply sign in via Sber ID and turn on desired options in profile settings.
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PJSC Sberbank is Russia’s largest bank and a leading global financial institution. Holding almost one-third of aggregate Russian banking sector assets, Sberbank is the key lender to the national economy and one of the biggest deposit takers in Russia. The Government of the Russian Federation represented by the Ministry of Finance of the Russian Federation is the principal shareholder of PJSC Sberbank owning 50% plus one voting share of the bank’s authorized capital, with the remaining 50% minus one voting share held by domestic and international investors. It holds general banking license No. 1481 dd. August 11, 2015, from the Bank of Russia. Official websites of the bank: www.sberbank.com (Sberbank Group website), www.sberbank.ru.
