Accessing Benchmark Leaderboard Data

Benchmark datasets on the Hub contain leaderboards ranking models by their evaluation scores. You can access this data programmatically to analyse, build dashboards or tools on top of it.

Discovering official benchmarks

Use huggingface_hub to find all official benchmark datasets:

from huggingface_hub import HfApi

api = HfApi()
for ds in api.list_datasets(benchmark=True):
    print(ds.id)

Getting leaderboard rankings

The leaderboard API returns ranked model scores for a benchmark dataset:

GET https://huggingface.co/api/datasets/{dataset_id}/leaderboard

Each entry includes the model’s rank, score, model ID, and the submitting organization’s avatar:

import requests

resp = requests.get(
    "https://huggingface.co/api/datasets/SWE-bench/SWE-bench_Verified/leaderboard"
)
entries = resp.json()

for entry in entries[:5]:
    print(f"#{entry['rank']} {entry['modelId']}: {entry['value']}")

#1 Qwen/Qwen3.5-397B-A17B: 76.4
#2 MiniMaxAI/MiniMax-M2.5: 75.8
#3 stepfun-ai/Step-3.5-Flash: 74.4
...

For gated benchmark datasets, pass an authorization header:

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headers = {"Authorization": "Bearer hf_..."}
resp = requests.get(url, headers=headers)

Curl one-liner for quick access (useful for scripting):

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curl https://huggingface.co/api/datasets/cais/hle/leaderboard \
  --header "Authorization: Bearer $(cat ~/.cache/huggingface/token)" | jq .

Response fields

Each leaderboard entry contains:

Pre-aggregated multi-benchmark dataset

If you want scores from multiple benchmarks in a single file, the OpenEvals/leaderboard-data dataset aggregates scores across official benchmarks into one Parquet file:

You can load it directly with pandas using the hf:// path:

import pandas as pd

df = pd.read_parquet(
    "hf://datasets/OpenEvals/leaderboard-data/data/train-00000-of-00001.parquet"
)
print(df[["model_name", "provider", "aime2026_score", "mmluPro_score"]].head())

This is the fastest way to get a cross-benchmark view without calling multiple API endpoints.

Enriching with model metadata

Use huggingface_hub to enrich leaderboard data with release dates, parameter counts, and other metadata:

from huggingface_hub import HfApi

api = HfApi()
info = api.model_info("Qwen/Qwen3.5-397B-A17B")

print(f"Released: {info.created_at}")
print(f"Parameters: {info.safetensors.total / 1e9:.1f}B" if info.safetensors else "")

Example: building on leaderboard data

The Benchmark Leaderboard Race Space combines these data sources to create an animated visualization of how model rankings evolve over time. You can build your own analyses and visualizations on top of this data — see the source code for a complete example.

Related

• Eval Results — how to submit evaluation results and register benchmarks
• Official Benchmark Datasets — browse all official benchmarks