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.
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.
{'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 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.