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LLM Benchmarks

Meta Muse Spark: Meta's First Closed Model Since 2023

12 min read

Until today, this series had no chapter on Meta. While Llama 4 was the headline, the silence was justified: the model landed to mixed reviews, and there was nothing to tell a manager about it as a working tool.

On July 9, 2026, Meta released Muse Spark 1.1 and, the same day, opened public access to the Meta Model API. The announcement went out on the company blog. The Muse family itself, which replaces Llama, started three months earlier: version 1.0 shipped on April 8, and the current model is built on top of it. In the September LMArena snapshot, muse-spark-1.2 in xHigh mode sits fifth on the text leaderboard, muse-spark-1.1 tenth, and muse-spark is sixth on the image leaderboard. Anyone comparing tools by leaderboard will run into the name. Until now, this series had nothing to say about it.

The scores are only the third most important story here. The first is the licence: in nine months Meta replaced its AI leadership, rebuilt the stack and came back with closed weights. The second is what kind of model it turned out to be: 1.1 leads on tool-calling tasks and trails noticeably on writing code.

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Meta Muse Spark: Meta's First Closed Model Since 2023
40 GigaChat Case Studies vs the Benchmark: Checking Sber's Numbers
23 min

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

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?