Artificial Analysis Releases Updated Intelligence Index
The latest industry benchmark shows a widening performance gap between proprietary and open-weight AI models.
Updated on Oct. 28, 2026 in Artificial Intelligence
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Do you believe open-source artificial intelligence models will eventually outperform proprietary versions?
Artificial Analysis released version 5 of its Intelligence Index on October 28, 2026. The updated ranking reveals a significant performance divide between top-tier proprietary models and their open-weight counterparts.
Why it matters
The firm shifted to using private test sets for 60 percent of its index to prevent models from being trained on known test questions. This methodology aims to provide a more accurate evaluation of genuine reasoning capabilities across knowledge work, coding, science, and general knowledge tasks.
Claude Opus 5.5 leads the index with 58 points, followed by GPT-6 Astra at 57 points. Models generate a median of 59,000 output tokens per task, with proprietary models currently maintaining a substantial lead over open-source alternatives.
The players
Artificial Analysis
Artificial Analysis is an analytics firm that provides comparative performance benchmarks and testing data for large language models and artificial intelligence systems.
The details
Claude Opus 5.5 sits at the top of the leaderboard, while Kimi K3.1 Pro scores 46 points and Muse Spark Open records 25 points. To ensure accuracy, the assessment utilizes held-out answers and private sets to force models to solve problems without prior exposure to the testing material.
Timeline
October 28, 2026: Artificial Analysis released version 5 of its Intelligence Index.
The Tech Race
This release continues the established trajectory of the Intelligence Index benchmark by providing standardized performance metrics. It captures a pivotal moment in AI development where proprietary systems are increasingly distancing themselves from open-weight competitors in key reasoning domains.
Users and developers can utilize these rankings to determine which models offer the most reliable reasoning for specialized coding or scientific tasks. The data highlights a growing necessity to evaluate whether proprietary options justify their higher cost compared to open-weight models.
The takeaway
The widening gap between proprietary and open-weight models suggests that advanced reasoning capabilities may become increasingly concentrated in closed systems. Readers evaluating AI tools for professional workflows should prioritize these verified benchmark results over generalized claims of model superiority.
Further reading
For more context on the evolving benchmarks in the field, visit Artificial Intelligence.
Live Poll
Do you believe open-source artificial intelligence models will eventually outperform proprietary versions?
