研究 question
Can 智能路由 plus detached 系统 reduce token usage for 中小企业 financial statement automation when compared with a heavier AI-first workflow?
运行中 Benchmark
NeuralOps 令牌效率 Benchmark
An AINNA operational benchmark recorded approximately 87% reduction in token usage under the tested workflow. The result is tied to 智能路由, 分离式处理 and a clearly stated workload, not to a universal claim about all AI workloads.
approximately 87% reduction in token usageheadline result
~8,500 令牌 per statementbaseline per statement
~1,100 令牌 per statementoptimized per statement
87%内部基准 value
环境 / scope
100 中小企业, 12 statements per 中小企业, study date July 2026. The benchmark assumes local inference API fee RM 0/token and separates external API cost from infrastructure amortization.
Methodology
| Item | 价值 | Why it matters |
|---|---|---|
| 基线 workflow | AI-heavy processing with far more 令牌 per statement. | Represents the reference path. |
| 已优化 workflow | 智能路由 plus detached 系统 before LLM escalation. | Reduces work sent to the model. |
| Counted | Statement processing 令牌, API cost and derived energy estimate. | Keeps the benchmark explicit. |
| Not counted | Universal savings, all hardware variants, all workload types. | Avoids overclaiming. |
Results
- Approximately 87% 令牌降耗 in the tested workflow.
- 关于 6.5M 令牌 saved across the benchmark model.
- 关于 RM5,200 in API savings under the stated assumption set.
- 关于 47.7 kWh energy and about 85% faster processing in the published study page.
Interpretation
The result supports AINNA's claim that a routed and detached architecture can cut unnecessary model usage. It does not prove the same percentage for other 领域, models or infrastructure.
局限性
- The benchmark is internal and operational, not peer reviewed.
- 硬件 and pricing assumptions affect the absolute savings.
- The percentage should not be reused as a universal claim.
- Further human methodology confirmation is still valuable for external publication.
引用信息
Suggested citation: AINNA. "NeuralOps 令牌效率 Benchmark." AINNA 研究, 2026. Canonical URL: https://masli.bond/research/neuralops-token-efficiency/
