Research pain points
Academic experiments require control variables to compare model performance, but interface differences across platforms make it difficult to standardize test environments.
UniAPI Application Program
The experimental environment can be set up according to the following procedure:
- Unified Test Interface: Connect all models to be tested (e.g. GPT-4/Claude2/Gemini) to UniAPI
- Creating benchmark test sets: Sent over the /v1/chat/completions interface with the same prompt sequence
- data acquisition: Quality of service metrics (success rate/response latency) in the Records Management panel
- Analysis of results: Export historical route logs in Redis for side-by-side comparisons
special function
Characteristics that are particularly useful in research scenarios:
- Forced assignment of routing function (disable auto-optimization)
- Raw response header passthrough (retaining information such as x-ratelimit for each vendor)
- Detailed request log in development mode
This answer comes from the articleUniAPI: Server-Free Unified Management of Large Model API ForwardingThe
May not be reproduced without permission:AI productivity tools " How to efficiently compare the performance of different large models in educational research scenarios?
