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GigaChat

40 GigaChat Case Studies vs the Benchmark: Checking Sber's Numbers

23 min read

Sber, Russia’s largest bank and the company behind GigaChat, released a sponsored showcase: forty business cases from companies that deployed GigaChat and reported the results. EdTech, MedTech, HRTech, cybersecurity, PropTech. Polished cards, concrete numbers, real startups.

Sber’s promotional project

On the image: the “One step ahead” promo slide from the Sber500×GigaChat accelerator – 40 startups across 9 industries. Claimed effects: business processes up to x16 faster, costs down by up to 90%, up to 95% task automation, and revenue up by up to 30%.

We have a benchmark of our own: 29 models, 4,308 independent evaluations on managerial tasks. In it, GigaChat sits dead last – 29th out of 29 after the second wave of testing. That creates an interesting situation.

Not because Sber is lying. The cases are real, the startups exist, the automation works. The question is different: was this the optimal model for the tasks they were solving?

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40 GigaChat Case Studies vs the Benchmark: Checking Sber's Numbers
GigaChat Ultra Thinking: Thinks Longer – Answers Worse?
7 min

GigaChat Ultra Thinking: Thinks Longer – Answers Worse?

GigaChat Ultra Thinking takes longer to think and uses more compute. It solves management tasks 3.3% worse than the version without reasoning. This is not a bug or a fluke – it’s a pattern documented in academic papers over the past two years.

This week, Sber unveiled GigaChat Ultra – a new flagship model with a reasoning mode (Thinking). The model is available for free via web, mobile apps, and a Telegram bot. We immediately added both variants to our AI model research for managers: ran them through all 32 scenarios using our unified methodology, scored them with both LLM judges, and compared against the other 52 models.