README.md

---
configs:
- config_name: models
  data_files:
  - split: models
    path: models.jsonl
- config_name: benchmarks
  data_files:
  - split: benchmarks
    path: benchmarks.jsonl
tags:
- benchmarks
- evaluation
- llm
- leaderboard
---

# LLM Benchmark Usage (2023–2026)

Which evaluation benchmarks 39 AI labs use to evaluate their models, and how that's
changed over time — hand-built from 62 papers, technical reports, system cards, model
cards, and blog posts, covering 128 models from 2023-07 to 2026-07.

```python
from datasets import load_dataset

models = load_dataset("SaylorTwift/llm-benchmark-usage", "models")["models"]
```

## `models`

One row per model, fully self-contained. 128 rows.

| column | type | description |
|---|---|---|
| `model_id` | string | Hugging Face repo id for open-weight models (e.g. `Qwen/Qwen3.5-397B-A17B`), or a plain slug for closed API models (e.g. `claude-opus-4-8`, `gpt-5.5`, `gemini-2.5-pro`, `grok-4`) |
| `lab` | string | Organization/lab that released the model |
| `release_date` | timestamp | Release date. HF repo creation date for open-weight models (a proxy, not always the exact announcement date); hand-researched announcement/system-card date for closed models |
| `source` | string | Link to the paper/report/card/blog this model's evaluation suite was extracted from |
| `benchmarks` | list[string] | The evaluation suite: benchmark names, canonicalized (e.g. always `GPQA-Diamond`, never `GPQA Diamond`/`GPQA-diamond`). Values act as foreign keys into `benchmarks.name` where a metadata entry exists — the `benchmarks` split currently covers only a subset of all referenced benchmarks, so a name without a matching row there just means no metadata has been collected yet, not that the benchmark wasn't used. Per-source free-text categories were dropped when this field was flattened to plain names |

Richer per-source metadata (paper type, title, notes, confidence flags) previously
lived in a `sources` table alongside this one; it may move to a separate dataset later.

## `benchmarks`

One row per benchmark, 125 rows: `name`, `categories` (list, e.g. `agentic`, `math`),
`modality` (list), `language` (list), `description`, `paper_url`, `implementation_url`.
`models.benchmarks` values reference this table by `name`; coverage is partial (125 of
~540 distinct referenced benchmarks) and will grow over time.

## Benchmark name canonicalization

Benchmark names are deduplicated across ~250 raw name variants collected from primary
sources (casing, hyphenation, and cross-lab transliteration differences — e.g.
`τ²-Bench` / `TAU2-Bench` / `TauBench V2` were all the same benchmark and are unified
to `TAU2-Bench`). Genuinely distinct benchmark variants are kept separate on purpose
(e.g. `GPQA` vs. `GPQA-Diamond` vs. `GPQA Hard`, or `HumanEval` vs. `HumanEval+`).

## Known limitations

- Not a random or complete sample of all models ever released — built by snowballing
  outward from four 2026 HF leaderboards (GPQA, HLE, SWE-bench Pro, Terminal-Bench 2.0),
  a hand-picked set of closed models, and a curated flagship pull from ~18 additional
  labs added for historical depth back to 2023. Skews toward H1 2026.
- The Grok line (xAI) is sourced partly from screenshots of the original announcement
  pages (which block automated fetching) and partly from secondary aggregators where no
  screenshot was available.
- `lab` attribution is by HF namespace; community requantizations of another lab's
  checkpoint (e.g. exolabs/RedHat AI repacks of NVIDIA models) are attributed to the
  original lab, since requantizing isn't an independent benchmarking choice.
- Two models currently resolve to an empty `benchmarks` list due to pre-existing gaps
  in the source data: `HelpingAI/Dhanishtha-2.0-0126` (its source's benchmark entries
  are all scoped to its 3 other models) and `MiniMaxAI/MiniMax-H3` (its source has no
  benchmark entries recorded yet).