If Gemini can complete a task for $1 and Qwen completes that same task for $1, then the cost per token is irrelevant in most use-cases. One would think this stuff should correlate well enough that you can use it as a proxy, but I think a lot of people are noticing this is a serious mistake and that these "cheap" models aren't as cheap as they appear when you consider this.
You point is valid for textual LLM, with large CoT, not Omni which will respond quick with a voice. In this case, token price is a good enough proxy.
What matter the most and isn't told by token price is the latency. You expect a voice LLM to respond very quick. If it takes 5s to response to a simple "Hello, what the weather today?", them not much people will use it.
_ache_ · · focus · HN ↗
in/out ($) Gemini : 1.5 / 9.0 | Qwen 3.8: 0.15 / 0.47
That is a massive cost reduction.
Refs: <a href="https://www.alibabacloud.com/help/en/model-studio/model-pricing#china-beijing-h4" rel="nofollow">https://www.alibabacloud.com/help/en/model-studio/model-pric... <a href="https://runware.ai/gemini-omni" rel="nofollow">https://runware.ai/gemini-omni
killingtime74 · · focus · HN ↗
_ache_ · · focus · HN ↗
nater5000 · · focus · HN ↗
If Gemini can complete a task for $1 and Qwen completes that same task for $1, then the cost per token is irrelevant in most use-cases. One would think this stuff should correlate well enough that you can use it as a proxy, but I think a lot of people are noticing this is a serious mistake and that these "cheap" models aren't as cheap as they appear when you consider this.
_ache_ · · focus · HN ↗
What matter the most and isn't told by token price is the latency. You expect a voice LLM to respond very quick. If it takes 5s to response to a simple "Hello, what the weather today?", them not much people will use it.