README.md

---
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.