Sber has published the entire stack of models with permission for commercial use.
The flagship is GigaChat 3 Ultra-Preview a 702B-MoEmodel, fully trained from scratch on a corpus of 14 trillion tokens. This is not an adaptation or fine-tuning of foreign weights: the model has its own dataset, its own synthetic pipeline, and a redesigned architecture. On Russian-language and STEM benchmarks, Ultra-Preview confidently outperforms Russian open source counterparts, as well as surpasses DeepSeek V3.1.
Context memory up to 128k tokens.
Also available in open source is the Lightning version a compact 10B-MoE model that competes in inference speed with Qwen3-1.7B and approaches the quality of dense models around 8B. GigaAM-v3 is also open — a set of five models for working with audio. It recognizes speech excellently — showing a −50% WER compared to Whisper-large-v3.
The open GigaChat lineup effectively forms a new open ecosystem for development, generation, and automation and does so as an independent architecture, not a continuation of someone else’s solutions.
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