How far behind are open models? (17 minute read)

TLDR AI 05/29/26, 12:00 AM News

Summary

An analysis from LessWrong examining the performance gap between open-source and proprietary AI models.

Open models are generally not as capable as the best closed models. However, they aren't too far behind, with tests showing that they are only four to six months behind on public benchmarks. The gap was the smallest around the time of DeepSeek R1. It has since been growing.

Original Article

View Cached Full Text

Cached at: 05/29/26, 06:31 PM

# How far behind are open models? — LessWrong Source: [https://www.lesswrong.com/posts/rJcCrXyEsJKmmDpWG/how-far-behind-are-open-models](https://www.lesswrong.com/posts/rJcCrXyEsJKmmDpWG/how-far-behind-are-open-models) x How far behind are open models? — LessWrong

Similar Articles

@EpochAIResearch: We took another look at the capability gap between open-weight and proprietary models. Since the start of the year, ope…

X AI KOLs Following

Epoch AI Research analyzed the capability gap between open-weight and proprietary AI models, finding that open-weight models have been trailing the state of the art by approximately four months since the start of the year.

The new benchmarks like DeepSWE now show a very big gap in proprietary models and open source

Reddit r/singularity

New benchmarks like DeepSWE reveal a significant performance gap between proprietary and open-source AI models, causing disappointment in the open-source community.

Open vs Closed AI Models: How the Gap Collapsed in 2025-2026 and Where It's Heading

Reddit r/artificial

The article examines how the performance gap between open and closed AI models has narrowed dramatically from early 2025 to mid-2026, highlighted by DeepSeek's open model release and the subsequent market impact. It discusses the roles of Chinese labs in driving the open frontier and the implications for the industry.

The state of open source AI (15 minute read)

TLDR AI

The state of open source AI report by Mozilla highlights that open-weight models have reached parity with closed models on many tasks, while inference costs have dropped 50× in 36 months. The majority of production tokens now route through open models, and the competitive landscape has shifted to the agentic layer above.

Open Source models may finally catch up