The cheapest model on my plan loses every benchmark. It still beats models charging 14x more.
The cheapest model I can run on my plan costs $0.14 per million tokens. The one I actually reach for costs $0.44 . I put them side by side expecting the cheap one to be close . It lost all four benchmarks. Coding, reasoning, math, real-world bug-fixing — four for four. I'm still recommending it. Not because it competes with the good models. Because of what it does to the models in the middle. The…
The cheapest language model on the author's plan, MiMo-V2.5, performs poorly in benchmark tests against pricier options like Deepseek v4. The $0.14 per million token MiMo-V2.5 model scored significantly lower in coding ability, reasoning, math, and bug-fixing compared to pricier models. Despite losing all four benchmarks, the author still recommends MiMo-V2.5 due to its superior performance in handling a large number of requests.
The author highlights that the decision between cheaper models and pricier ones should be based on resource constraints rather than benchmark scores. The article emphasizes the importance of considering request limits, input prices, and output costs when selecting a model, rather than solely relying on benchmark results.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
