Unverified50% confidenceOpinionExact time
The asymmetric input-output architecture of V4.1 Flash is designed to align with the distribution pattern in Agent tasks where input tokens far outnumber output tokens, thereby reducing overall inference costs.
1
Sources
50%
Confidence
Long-term
Relevance
10/10/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedV4.1 Flash adopts a new CED (Causal Encoding) architecture, whose core idea is an asymmetry in computation between input and output.76% similarUnverifiedFlashAttention fuses the QKV computation, softmax, and output projection of the attention mechanism into a single kernel, reducing memory complexity from O(N²) to O(N) and achieving 2–4x end-to-end speedup.75% similarUnverifiedThe reason V4.1 Flash's total cost increased is that it tends to spawn more sub-Agents in complex tasks; in the benchmark, two problems cumulatively spawned 37 sub-Agents and burned through more than one million tokens.75% similarUnverifiedCompared to the old Flash model, V4.1 Flash has more comprehensive capabilities and more accurate recognition, but takes longer and costs more per session.75% similarUnverifiedFlash versions achieve reduced response times and inference costs while maintaining capability through distillation, quantization, or architectural optimization74% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/993491API
curl https://kongchang.com/api/v1/knowledge/claims/993491MCP
get_claim(id=993491)