Artificial Analysis Openness Index ranks K2 Think v2 as most transparent open model
New index reveals which 'open' models truly share training data and methods, not just weights.
Artificial Analysis has released an Openness Index that moves beyond the typical 'open weights' definition to evaluate how much an open model actually enables independent reproduction. The index examines factors like whether training data, training code, and the full training regimen are publicly available. K2 Think v2 achieved the highest rating because it supplies not only its weights but also the training dataset and detailed instructions for recreating the model from scratch. This means any team with sufficient compute resources could theoretically replicate the exact same model, a gold standard for openness.
In contrast, DeepSeek, despite being widely used and often described as open, received a lower score because it does not publish its training data or disclose the specific methods used to train the model. The index highlights a growing distinction in the AI community: truly open models enable third-party verification and derivative works, while 'open weight' models may only offer free inference. For enterprises and researchers prioritizing transparency, auditability, and long-term reproducibility, this index provides a practical benchmark when selecting foundation models.
- K2 Think v2 ranks highest for sharing both training data and training regimen, enabling full model recreation.
- DeepSeek scores lower on the index due to lack of published training data and training methodology.
- The Openness Index evaluates factors beyond free weights, including training code, data, and reproducibility.
Why It Matters
Transparency benchmarks help teams choose truly open models that support auditability and independent replication.