Wikit/RoutingCompendium-cost
RoutingCompendium — Cost Inference price of every candidate LLM appearing in Wikit/RoutingCompendium-perf. The two datasets are meant to be loaded together: -perf gives what each candidate scores on a query, -cost gives what calling it costs. Splits One split per benchmark, with the same names as RoutingCompendium-perf (RouterBench, Sprout, EmbedLLM, FusionBench, R2Bench). Each split lists the candidates of that benchmark's pool — a few dozen rows at most.… See the full description on the dataset page: https://huggingface.co/datasets/Wikit/RoutingCompendium-cost.
RoutingCompendium — Cost
Inference price of every candidate LLM appearing in `Wikit/RoutingCompendium-perf`. The two datasets are meant to be loaded together: -perf gives what each candidate scores on a query, -cost gives what calling it costs.
Splits
One split per benchmark, with the same names as RoutingCompendium-perf (RouterBench, Sprout, EmbedLLM, FusionBench, R2Bench). Each split lists the candidates of that benchmark's pool — a few dozen rows at most.
Schema
One row per candidate model.
The two shapes of cost
cost is a string so that one column can hold both a scalar and a dict. After ast.literal_eval you get either:
1. A dict of per-token prices — {"input": float, "output": float}, in USD per 1M tokens, as published by the provider. Used by RouterBench, FusionBench and R2Bench.
{'input': 0.09, 'output': 0.55}
**2. A number** — the model's parameter count, in billions. Used by `Sprout` and `EmbedLLM`.
7.0 # a 7B model
### Parameter count -> USD per 1M tokens
Open-weight models are priced by size, using the [Together AI pricing](https://www.together.ai/pricing) as accessed on 2025-06-05:
| Parameters (B) | USD / 1M tokens |
|---|---|
| ≤ 4 | 0.10 |
| ≤ 8 | 0.20 |
| ≤ 21 | 0.30 |
| ≤ 41 | 0.80 |
| ≤ 80 | 0.90 |
| ≤ 110 | 1.80 |
| > 110 | 1.80 + 0.03 × (params − 110) |
Beyond 110B the last interval's slope (\$0.03 per additional billion parameters) is extrapolated linearly.