New Open Source AI Model Rivals DeepSeek's Performance—With Far Less Training Data - Decrypt
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OpenThinker-32B achieved benchmark-beating results using just 14% of the data its Chinese competitor needed, marking a win for open source AI

cross-posted from: https://lemmy.sdf.org/post/29607342

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Here is the data at Hugging Face.

A team of international researchers from leading academic institutions and tech companies upended the AI reasoning landscape on Wednesday with a new model that matched—and occasionally surpassed—one of China’s most sophisticated AI systems: DeepSeek.

OpenThinker-32B, developed by the Open Thoughts consortium, achieved a 90.6% accuracy score on the MATH500 benchmark, edging past DeepSeek’s 89.4%.

The model also outperformed DeepSeek on general problem-solving tasks, scoring 61.6 on the GPQA-Diamond benchmark compared to DeepSeek’s 57.6. On the LCBv2 benchmark, it hit a solid 68.9, showing strong performance across diverse testing scenarios.

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