Unverified50% confidenceFactExact time
Mainstream VLM-based OCR models typically have parameter counts exceeding 3B (3 billion).
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
9/7/2026
First Seen
Valid until: 12/6/2026
Sources
Related Entities
Related Claims
VerifiedHy3 adopts a Mixture of Experts (MoE) architecture with a total parameter count of 295 billion (295B), activating only 21 billion (21B) parameters per inference.66% similarUnverifiedSmall VLMs such as LLaVA-7B and Qwen-VL-Chat typically have parameter counts in the billions, while object detection models typically range from tens of millions to hundreds of millions of parameters.65% similarUnverifiedArtificial Analysis gives the Qwen 3.8 27-billion-parameter model a score of 51 on the Agentic index.62% similarUnverifiedGLM-4.7 is a MoE (Mixture of Experts) architecture model with a total parameter count of 358B (358 billion).62% similar
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