Expired60% confidenceFactExact time
On-device large model parameter counts are typically between 1B and 13B, and there is a significant gap compared to cloud-based large models in complex reasoning and long-document understanding
2
Sources
60%
Confidence
Medium-term (~90 days)
Relevance
7/4/2026
First Seen
This claim has expired. It is kept for the record and no longer describes the current state.
Valid until: 10/2/2026(expired)
Sources
Tencent Marvis Hands-On: How Multi-Agent AI Takes Over Your Computer
bilibili下班学AI7/2/2026
Related Claims
UnverifiedThe fundamental reason for the response quality gap between on-device and cloud models lies in parameter scale and quantization compression; on-device models undergo aggressive quantization (such as INT4/INT8) and distillation, resulting in degraded instruction-following capability.76% similarUnverifiedOn-device models are constrained by smartphone computing power and still have a significant gap compared to cloud-based 70B+ models in complex scenarios such as multi-step reasoning, context memory, and ambiguity resolution.73% similarVerifiedLimited by the computing power and memory of consumer-grade devices, the scale of models that can be run locally is typically far smaller than cloud-based flagship models, with gaps in complex reasoning, long-context processing, and other tasks.70% similarUnverifiedGiven the same prompt, a local model at the 7B or 13B level may only partially understand complex constraints that Opus can execute accurately.68% similarUnverifiedWhen model scale crosses certain thresholds, emergent capabilities not previously present arise (emergent abilities), such as multi-step logical reasoning and complex mathematical computation.68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/68506API
curl https://kongchang.com/api/v1/knowledge/claims/68506MCP
get_claim(id=68506)