Russia's largest bank, Sber, on July 10 released the open-source large model GigaChat 3.5 Ultra, with a "linear attention + MoE" dual-engine combination challenging the mainstream transformer route head-on. This is currently one of the few open-source projects in the world to scale linear attention up to large-model scale and release public weights. The new model's core is a completely self-developed linear attention architecture by the Sber team. Traditional attention rescans the entire context for each new token generated, with compute growing quadratically with length; while linear attention compresses the context into a "summary vector", only adding increments each time, making long-text scenarios linear in complexity. Official data shows that GigaChat 3.5 Ultra lifts long-text speed 4×, while the model size is only half that of the previous generation. With MoE architecture added, the new model's total parameter count is reportedly one of the largest among current open-source linear-attention models. The Sber AI team completed 1500 experiments during training, and through multi-round human-data filtering and cleaning, significantly improved performance on code, math, long-document understanding, and Agent autonomous tasks. The official claim is that on multi-step reasoning and programming benchmarks it has approached DeepSeek 3.2, but the model size is nearly halved, meaning inference cost and hardware threshold are significantly reduced. Sber senior VP Anton Frolov emphasized that the model demonstrates the engineering feasibility of "training strong models with fewer resources", and has been opened to global developers for building Agent services. GigaChat 3.5 Ultra is now simultaneously available on GigaChat Assistant and Hugging Face, securing a seat in the international open-source-weight camp for non-English-system country models.