AI BriefingThe Gap Between Open Weights LLMs and Closed Source LLMs
Key point
An analysis shows that the pattern of the performance gap between Open Weights models and Closed Source models differs depending on the benchmark metric used.
Details
Analyzing Artificial Analysis's Intelligence Index, a trend was observed in which the performance gap between Open Weights LLM and Closed Source LLM has been narrowing since the summer of 2024. If predicted based on a specific metric, the gap between the two model groups is expected to disappear around December 2026.
However, a comprehensive analysis of 18 different benchmarks shows a different picture. The average gap across all metrics has remained relatively constant at around 5 months.
The key characteristics are as follows:
- Coding benchmarks: The gap, which was previously 15 months, has now sharply narrowed to 1-2 months.
- Metric-dependent variation: The speed at which Open Source catches up and the degree to which the gap persists vary depending on the type of benchmark being measured.
- Complexity of measurement: It is difficult to define LLM quality with a single metric, and predictions about the pace of Open Source's progress can vary greatly depending on which benchmark is used.
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