Development curve from Llama to Muse
The tracked period covers 22 months beginning with the release of Llama 3.2 Instruct in September 2024. Models in that group showed wide variation in the intelligence index: the 11B version scored 3.3, the 3B version 4.2, and the 1B version only 1.1. December 2024 brought Llama 3.3 Instruct 70B at 9.4, signalling a move toward more capable systems.
The year 2025 added Llama 4 Maverick at 14.3 and Scout at 10. The real break in the intelligence index arrived in 2026 with the Muse series. Muse Spark reached 43.1 and its successor Muse Spark 1.1 moved to 50.6. According to pre-calculated data the total increase between Llama 3.2 Instruct 11B and Muse Spark 1.1 amounts to 47.3 points.
Critical reading of the intelligence index
The intelligence index as presented by Meta suggests steep performance growth, yet it must be read in the context of a laboratory environment. The jump between the Llama and Muse families is numerically striking, but in production it may not correspond to a linear rise in useful value for enterprise applications. Laboratory benchmarks often isolate specific capabilities that behave differently in complex deployments.
Media monitoring indicates that investors are questioning whether the tempo of index growth justifies capital expenditure. While technical progress is visible, financial data reveal tension. Agency reports state that the company’s costs rose by 55 per cent while net profit fell by 14 per cent. The figures therefore show rising model efficiency alongside falling efficiency of invested resources.
Implications for enterprise adoption
For organisations outside Czechia the same logic applies: model choice should not be driven by the current intelligence index alone. Although Muse models post high values, their deployment demands robust infrastructure whose financing is proving challenging even for Meta. Enterprises should assess whether an earlier Llama model may meet specific needs with stable performance at lower demand.
A key figure is the average interval between releases, which in this dataset stands at 3.1 months. That high cadence means investment in integrating a particular model can be quickly devalued by the arrival of a newer, more powerful version. Procurement strategy for AI solutions should therefore remain flexible and centre on models that have already demonstrated stability in real-world operation.
Frequently asked questions
What is the difference in performance between the Llama 3.2 and Muse models?
The difference in the intelligence index is significant. While the Llama 3.2 Instruct models ranged from 1.1 to 4.2, the latest Muse models achieve values of 43.1 and 50.6. The total increase between the initial Llama 3.2 Instruct 11B model and the latest Muse Spark 1.1 model is 47.3 points, which represents a significant technological leap over the observed period of 22 months.
Is the pace of releasing new Meta models sustainable?
The data show that Meta releases new models at an average interval of 3.1 months. However, this high frequency is accompanied by massive capital expenditures, which are expected to reach up to 145 billion dollars this year. Although technological progress in the intelligence index is growing, financial results suggest that the current strategy is costly for the company and raises uncertainty among investors.
Which Meta model achieved the highest index in tests?
According to the provided data, the Muse Spark 1.1 model achieved the highest intelligence index value, at 50.6. This model was released on 2026-07-09 and represents the latest point in the trajectory of Meta's model development. The previous version of Muse Spark achieved a value of 43.1, while the Llama series models were in the lower ranges, with the most powerful of them, Llama 4 Maverick, achieving 14.3.
DATA SOURCES AND METHOD
Všechna čísla v tomto srovnání pocházejí z uvedených zdrojů k datu 5 August 2026. Grafy generuje institut CIAD přímo ze zdrojových dat; textová analýza čísla nikdy nedopočítává ani neodhaduje.