Unverified50% confidenceEventExact time
The authors tested a variety of vision architectures ranging from D=7,850 to D=4,203,522 (about 4.2 million parameters) on the full MNIST benchmark.
1
Sources
50%
Confidence
Long-term
Relevance
10/16/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedArtificial Analysis noted that Haiku 5.5 used 162,000 output tokens in the intelligence index test in Max mode, approximately three times that of GPT-6 Luna.62% similarVerifiedHy3 adopts a Mixture of Experts (MoE) architecture with a total parameter count of 295 billion (295B), activating only 21 billion (21B) parameters per inference.60% similarPartially VerifiedMistral Large 4 adopts a sparse Mixture of Experts (MoE) architecture, with 49 billion (49B) parameters actually activated during each inference.60% similarUnverifiedThe same parameter name reasoning_effort behaves differently across models from different vendors: in gpt-5.4-mini the median output tokens rose from 18 at none to 254 at high (×14.1); gpt-oss-120b rose from refusal to 1,340 (×4.6); claude-haiku-5.5 rose from 150 to 298 (×2.0); gemini-3.7-flash rose from 453 to 1,013 (×2.2).59% similarUnverifiedMistral Large 4's vision encoder has been reduced from 2.5 billion parameters in the previous generation to 1.6 billion parameters.59% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1046146API
curl https://kongchang.com/api/v1/knowledge/claims/1046146MCP
get_claim(id=1046146)