模型路由降低成本并提升效率
问题概述
当前的模型路由方案在降低调用成本、减少冗余切换和处理不同提供商质量波动方面仍有不足，导致开发者在使用多模型编码代理时面临成本和性能的双重挑战。
痛点标签
- 高额的模型调用费用
- 缓存切换导致额外 token 消耗
- 不同模型提供商的质量不稳定
待验证问题
- 您在实际项目中是否遇到过模型调用成本高的情况？
- 您是否担心缓存切换导致的额外 token 消耗会影响性能？
- 您是否使用多家模型提供商，并希望路由自动适配其质量波动？
证据
原话
Have you compared this to using GPT-6.1 Sol instead of GPT 6 Astra + Deepseek? From my test, 6.1 Sol is a lot more token efficient than 6 Sol while being similar to Astra in performance, and I don't really find 6 Astra to be significantly better than 6/6.1 Sol for general coding as I feel 6 Astra is only noticeably better at spatial reasoning/vision compared to 6 Sol, and 6.1 Sol really closed the gap on that front.
原话
That sounds really quite interesting- one question pops up, this sounds like it could produce a lot of non-cache input tokens. How much impact does this have, and are you mitigating it? Interested in adding support to my own 3code- https://3code.capocasa.dev which aims to reduce cost by using more compact system prompts and a clever compaction variant.
原话
How do you handle provider variance on OpenRouter for the opensource models? Or do you use your own hosted version to mitigate this? And for both opensource and closed source, does the router account for provider quality, or catch it when a provider degrades?
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