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deepmind.google·6d ago
AI Engineering
Hacker News·Jun 26·5 min read
The gap between open weights LLMs and closed source LLMs
TL;DR
A gap exists between the performance of open weights large language models (LLMs) and closed source LLMs, measured using the Artificial Analysis Intelligence Index. The analysis involved tracking the performance of open weights LLMs over time against closed source models across 18 different benchmarks.
✦ Why It Matters
Engineers and researchers should prepare for a significant shift in the competitive landscape of LLMs by late 2026.
Key Takeaways
- Monitor developments in open weights LLMs leading up to December 2026.
- Consider the implications of closing performance gaps on project choices.
- Explore the 18 benchmarks for a comprehensive understanding of LLM capabilities.
- Stay informed about trends in open source AI to leverage new opportunities.
Full Summary
Open weights large language models (LLMs) have historically lagged behind closed source models in performance, which is assessed using the Artificial Analysis Intelligence Index, a benchmark for evaluating model capabilities. The analysis tracked the performance of open weights LLMs over time, comparing them to the closed source frontier.
By examining 18 different datasets, box plots were created to visualize the performance gap at each month. The findings revealed that the gap has been consistently shrinking since summer 2024, with projections indicating it will reach zero by December 3, 2026.
The average gap across all datasets was calculated to be just under 5 months, suggesting that open source models are rapidly catching up. This trend highlights the potential for open weights LLMs to achieve parity with their closed source counterparts, which could influence future development and deployment strategies.