Russia Unveils the First Russian-Language AI Benchmark for Long-Form Text Processing
Russian researchers have developed the first comprehensive benchmark designed to evaluate how well large language models handle long-form text in the Russian language
Why Long Texts Challenge AI
Modern AI systems can write essays, summarize articles, and answer questions with impressive fluency. But what happens when the input is not a short paragraph but hundreds of pages? Can a model locate a detail on page 100, connect facts across chapters, or detect hidden logical patterns? These remain difficult tasks even for leading LLMs.
This is why Russian researchers have introduced the first standardized benchmark for assessing long-context performance in Russian. The tool could reshape the ecosystem of Russian-language AI models.
Most LLMs perform well on short prompts but degrade dramatically as the input length grows. The issue stems from limitations of the “context window”—the amount of text a model can process at once. Even models claiming support for 128,000 tokens often struggle to extract meaning or reason reliably over such volumes.
Until recently, the Russian AI landscape lacked an open, unified, and objective tool to measure these capabilities. Every developer used their own methods, making comparison impossible. That is now changing.
LIBRA and the Long Context Benchmark: A Step Toward Standardization
In 2024, researchers introduced LIBRA (Long Input Benchmark for Russian Analysis), the first systematic attempt to measure model performance on long Russian texts across 21 tasks ranging from 4,000 to 128,000 tokens.
In 2025, teams from MIPT, HSE University, SberAI, and AIRI released an expanded, more focused successor: the Long Context Benchmark for the Russian Language. It includes 18 datasets covering information extraction, question answering, logical reasoning, cross-text fact linking, and instruction-following.
The benchmark’s greatest strength is its openness and reproducibility. Any developer can test a model, compare results, and refine their architecture. Initial results reveal a clear trend: even market leaders such as GPT-4 lose accuracy as context length increases. Among open-source models, GLM4‑9B‑Chat performed best—showing substantial room for improvement among domestic models.
Why This Matters: AI Quality and Technological Sovereignty
Developing a national benchmark is more than an academic exercise. It is a strategic move toward technological sovereignty. Russian-language AI systems adapted for long, complex texts could enable:
• AI chatbots capable of analyzing lengthy reports or legal documents
• Automated systems for processing scientific literature, engineering manuals, and news archives
• “Smart assistants” in government and business that understand context rather than matching keywords
Without reliable evaluation tools, it is impossible to guarantee the quality required for adoption in healthcare, education, governance, and other critical sectors.
Global Context and Export Potential
The initiative aligns with international trends. In 2025, ONERULER—a multilingual long-context benchmark covering 26 languages—was released. Yet specialized tools tailored to specific linguistic systems remain essential. Russian, with its rich morphology and complex syntax, requires approaches distinct from English or Chinese.
The benchmark’s open codebase and adaptability provide opportunities for cross-border collaboration, especially in CIS countries and Russian-speaking communities. Future multilingual extensions could increase global relevance.
Looking Ahead: From Testing to New Architectures
A benchmark is only a tool. Its true value emerges when it drives architectural innovation—modular text processing, hierarchical attention, external-memory systems, and enhanced retrieval-augmented generation (RAG).
Researchers expect new Russian-language models optimized for long-context tasks to appear in the next one to two years. Benchmarks such as LIBRA and its successor provide the transparency needed to measure progress.
