Contamination MapText / LLM / Model
OpenAI o1
- Organisation
- OpenAI
- Family
- o-series
- Released
- 2024-12-05 (Date of the o1 System Card ('December 5, 2024'); o1-preview was released earlier (snapshot o1-preview-2024-09-12))
- Training cutoff
- 2023-10-01
Evidence
“Oct 01, 2023 knowledge cutoff”
developers.openai.com · website · Model page header · retrieved 2026-10-15 - Checkpoints
- o1-2024-12-17, o1-preview-2024-09-12
- Weights
- Closed · proprietary
- Links
- PaperModel card
Training data disclosure: Vague
Level determined for 13% of benchmark cells.
- TestTest data seen0
- SeenSeen, split unclear0
- TrainTraining data seen0
- Related data seen0
- NoNot seen3
- ?Unknown19
“The two models were pre-trained on diverse datasets, including a mix of publicly available data, proprietary data accessed through partnerships, and custom datasets developed in-house”
Other declared training data
-
Select Public Data (publicly available datasets, including web data and open-source datasets) (broad web data: may contain benchmark test sets) · split: unknown
Evidence
“Select Public Data: Both models were trained on a variety of publicly available datasets, including web data and open-source datasets.”
arxiv.org · model card · Section 2 · retrieved 2026-10-15 “The two models were pre-trained on diverse datasets, including a mix of publicly available data, proprietary data accessed through partnerships, and custom datasets developed in-house”
arxiv.org · model card · Section 2 · retrieved 2026-10-15 -
Proprietary Data from Data Partnerships (not yet matched to a dataset) · split: unknown
Evidence
“we formed partnerships to access high-value non-public datasets. These proprietary data sources include paywalled content, specialized archives, and other domain-specific datasets”
arxiv.org · model card · Section 2 · retrieved 2026-10-15 -
custom datasets developed in-house (not yet matched to a dataset) · split: unknown
Evidence
“The two models were pre-trained on diverse datasets, including a mix of publicly available data, proprietary data accessed through partnerships, and custom datasets developed in-house”
arxiv.org · model card · Section 2 · retrieved 2026-10-15
Benchmark cells
Classic LLM benchmarks
Unknown MMLU (Measuring Massive Multitask Language Understanding) · training data not disclosed in enough detail
Unknown GSM8K (Grade School Math 8K) · training data not disclosed in enough detail
Unknown MATH (Mathematics Aptitude Test of Heuristics) · training data not disclosed in enough detail
Unknown HumanEval · training data not disclosed in enough detail
Unknown ARC-Challenge (AI2 Reasoning Challenge, Challenge Set) · training data not disclosed in enough detail
Unknown HellaSwag · training data not disclosed in enough detail
Unknown TruthfulQA · training data not disclosed in enough detail
Unknown WinoGrande · training data not disclosed in enough detail
Multimodal LLM benchmarks
Unknown MMMU (Massive Multi-discipline Multimodal Understanding and Reasoning) · training data not disclosed in enough detail
Unknown MMMU-Pro · training data not disclosed in enough detail
Unknown MathVista · training data not disclosed in enough detail
Unknown ChartQA · training data not disclosed in enough detail
Unknown DocVQA · training data not disclosed in enough detail
Unknown AI2D (AI2 Diagrams) · training data not disclosed in enough detail
Not seen CharXiv · its test items were published from 2024-06-26, after the model's stated training-data cutoff (2023-10-01)
Evidence (3)
“Oct 01, 2023 knowledge cutoff”
“arXiv:2406.18521v1 [cs.CL] 26 Jun 2024”
“we release all QA pairs for the validation set and keep the answers to the test set private to prevent data leakage.”
Recent LLM benchmarks
Unknown GPQA Diamond · training data not disclosed in enough detail
Unknown MMLU-Pro · training data not disclosed in enough detail
Unknown LiveCodeBench (code generation, release_v6) · training data not disclosed in enough detail
Unknown SWE-bench Verified · training data not disclosed in enough detail
Not seen AIME 2024 · its test items were published from 2024-01-31, after the model's stated training-data cutoff (2023-10-01)
Evidence (4)
“Oct 01, 2023 knowledge cutoff”
“2024 AIME I problems and solutions. The test was held on Wednesday, January 31 – Thursday, February 1, 2024.”
“AIME I (main AIME competition date): Thursday, Feb. 1, 2024 between 1:30 pm ET and 5:30 pm ET.”
“AIME II (alternate AIME competition date): Wednesday, Feb. 7, 2024 between 1:30 pm ET and 5:30 pm ET.”
Not seen AIME 2025 · its test items were published from 2025-02-06, after the model's stated training-data cutoff (2023-10-01)
Evidence (3)
“Oct 01, 2023 knowledge cutoff”
“2025 AIME I problems and solutions. The test was held on Thursday, February 6, 2025.”
“This year the AIME I will take place on February 6, 2025, while the alternate AIME II will take place on February 12, 2025.”
Unknown MATH-500 · training data not disclosed in enough detail
Notes
System card covers o1 and o1-mini. No contamination or decontamination statement found. The card notes an improved o1 launched on December 17 (snapshot o1-2024-12-17) that postdates the card. Accepts image input: “Modalities Text Input and output Image Input only”
Sources
- OpenAI o1 System Card (arXiv 2412.16720v2) · model card · retrieved 2026-10-15
- OpenAI API docs: o1 model page · website · retrieved 2026-10-15