File size: 2,632 Bytes
5f1be45 3820a60 5f1be45 3820a60 1610c06 5f1be45 1610c06 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | ---
dataset_info:
features:
- name: model
dtype: string
- name: cost
dtype: string
splits:
- name: EmbedLLM
num_bytes: 4043
num_examples: 108
- name: RouterBench
num_bytes: 511
num_examples: 11
- name: Sprout
num_bytes: 883
num_examples: 14
- name: FusionBench
num_bytes: 1388
num_examples: 40
- name: R2Bench
num_bytes: 596
num_examples: 10
download_size: 10904
dataset_size: 7421
configs:
- config_name: default
data_files:
- split: EmbedLLM
path: data/EmbedLLM-*
- split: RouterBench
path: data/RouterBench-*
- split: Sprout
path: data/Sprout-*
- split: FusionBench
path: data/FusionBench-*
- split: R2Bench
path: data/R2Bench-*
tags:
- llm-routing
- model-selection
pretty_name: RoutingCompendium (Cost)
---
# RoutingCompendium — Cost
Inference price of every candidate LLM appearing in [`Wikit/RoutingCompendium-perf`](https://huggingface.co/datasets/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.
| Field | Type | Description |
|---|---|---|
| `model` | `string` | Model name, matching an entry of `models_name` in the corresponding `-perf` split. |
| `cost` | `string` | Price, stored as a string and parsed with `ast.literal_eval`. |
### 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`.
```python
{'input': 0.09, 'output': 0.55}
**2. A number** — the model's parameter count, in billions. Used by `Sprout` and `EmbedLLM`.
```python
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.
|