Datasets:
License:
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: ValueError
Message: Expected object or value
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 281, in _generate_tables
examples = [ujson_loads(line) for line in batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or valueNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
🤖 benchmarks-llm
Comparaisons de modèles LLM établies à partir de sources publiques (Artificial Analysis, BenchLM, Vals.ai, NVIDIA, OpenRouter, docs officielles) et des rapports internes titoune33 (11-12/08/2026).
Fichiers
| Fichier | Contenu |
|---|---|
models.json |
Fiches structurées : 6 modèles (DeepSeek V4 Flash 0731, Mistral Large 3, Mistral Medium 3.5, Mistral Small 4, Nemotron 3.5 Lightning, MiMo V2.5) |
comparisons.jsonl |
14 comparaisons chiffrées pairwise (1 ligne = 1 métrique × 2 modèles) |
Schéma models.json
{
"id": "slug-unique",
"vendor": "éditeur",
"release_date": "ISO-8601 ou null",
"architecture": "MoE | null",
"params_total_b": 284, // milliards, total
"params_active_b": 13, // milliards, actifs
"context_tokens": 1000000,
"input_image": true|false, // + input_video, input_audio, reasoning
"open_weights": true|false,
"license": "MIT | proprietary | modified-open",
"price_input_usd_per_mtok": 0.14, // USD par M tokens
"price_output_usd_per_mtok": 0.28,
"price_cache_hit_usd_per_mtok": 0.0028,
"intelligence_aa_index": 52, // Artificial Analysis Intelligence Index v4.1.1
"speed_output_tok_per_s": 131,
"time_to_first_token_s": 1.44,
"agent_benchmarks": { "terminal_bench_2_1": 82.7, ... },
"benchlm_public_score": null,
"notes": "..."
}
Schéma comparisons.jsonl
{
"comparison": "slug-a_vs_slug-b",
"date": "2026-08-11",
"metric": "intelligence_aa_index", // clé du modèle ou nom libre
"values": { "slug-a": 52, "slug-b": 30 },
"winner": "slug-a" | null, // null si égalité/non comparable
"note": "optionnel",
"source": "Artificial Analysis"
}
Licence & sources
- Données factuelles : CC BY 4.0 — sources citées dans
models.json(urls) et dans les rapports d'origine. - Rapports internes titoune33 (benchmark DeepSeek vs Mistral, 11/08 ; MiMo vs DeepSeek, 12/08) : © titoune33.
Usage
- Routage de modèles : entrée du
llm-gateway(choisir le modèle par tâche, coût et vitesse). - Décisions d'achat : DeepSeek-V4-Flash-0731 = meilleur rapport intelligence/prix pour texte/agent ; MiMo V2.5 = multimodal ; Nemotron = exécution locale.
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