Update README.md

README.md

@@ -36,4 +36,58 @@ configs:
     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.