GPAI Ledger › Phi-4 (Microsoft) › Capture 11 Aug 2026

Phi-4 — capture 20260811T105752Z

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Data Summary for microsoft_phi-4
1. General information
1.0.1 Version of the Summary:1.0
1.0.2 Last update:24-Nov-2025
1.1 Model Developer Identification
1.1.1 Model Developer name and contact details:Microsoft Corporation
at One Microsoft Way, Redmond, WA 98052. Tel: 425-882-8080
1.2 Model Identification
1.2.1 Versioned model name(s):phi-4
1.2.2 Model release date:12-Dec-2024
1.3 Overall training data size and characteristics
1.3.1 Size of dataset and characteristics
1.3.1.A Text training data size:1 billion to 10 trillion tokens
1.3.1.B Text training data content:Training data is an extension of the
data used for Phi-3 and includes a wide variety of sources from:
1. Publicly available documents filtered rigorously for quality, selected high-
quality educational data, and code.
2. Newly created synthetic, “textbook-like” data for the purpose of teaching
math, coding, common sense reasoning, general knowledge of the world
(science, daily activities, theory of mind, etc.).
3. Acquired academic books and Q&A datasets.
4. High quality chat format supervised data covering various topics to re-
flect human preferences on different aspects such as instruct-following,
truthfulness, honesty and helpfulness.
5. Multilingual data constitutes about 8% of our overall data.
1.3.1.C Image training data size:Not applicable. Images are not part of
the training
1.3.1.D Image training data content:Not applicable
1.3.1.E Audio training data size:Not applicable. Audio data is not part of
the training data
1.3.1.F Audio training data content:Not applicable
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1.3.1.G Video training data size:Not applicable. Video data is not part of
the training data
1.3.1.H Video training data content:Not applicable
1.3.1.I Other training data size:Not applicable
1.3.1.J Other training data content:Not applicable
1.3.2 Latest date of data acquisition/collection for model training:
30-Jun-2024
1.3.3 Is data collection ongoing to update the model with new data
collection after deployment?No
1.3.4 Date the training dataset was first used to train the model:
10/01/2024
1.3.5 Rationale or purpose of data selection:Datasets were selected to
maximize high-quality reasoning and problem-solving capabilities. The mixture
emphasizes synthetic, curriculum-structured data and rigorously filtered organic
sources such as academic papers, licensed books, code, and Q&A to improve
STEM reasoning, coding, and general knowledge while reducing noise and
contamination. Targeted acquisitions and multilingual content complement
synthetic data to balance reasoning strength with factual coverage
2. List of data sources
2.1 Publicly available datasets
2.1.1 Have you used publicly available datasets to train the model?
Yes
2.2 Private non-publicly available datasets obtained from
third parties
2.2.1 Datasets commercially licensed by rights holders or their repre-
sentatives
2.2.1.A Have you concluded transactional commercial licensing agree-
ment(s) with rights holder(s) or with their representatives?Yes
2.2.2 Private datasets obtained from other third-parties
2.2.2.A Have you obtained private datasets from third parties that
are not licensed as described in Section 2.2.1, such as data obtained
from providers of private databases, or data intermediaries?This
information cannot be provided due to unavailability of the underlying data
(e.g., loss, corruption, or other access limitations)
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2.3 Personal Information
2.3.1 Was personal data used to train the model?Microsoft follows all
relevant laws and regulations pertaining to personal information
2.4 Synthetic data
2.4.1 Was any synthetic AI-generated data used to train the model?
Yes
3. Data processing aspects
3.1 Respect of reservation of rights from text and data mining excep-
tion or limitation
3.1.1 Does this dataset include any data protected by copyright,
trademark, or patent?Microsoft follows all required regulations and laws for
processing data protected by copyright, trademark, or patent
3.2 Other information
3.2.1 Does the dataset include information about consumer groups
without revealing individual consumer identities?Microsoft follows all
required regulations and laws for protecting consumer identities
3.2.2 Was the dataset cleaned or modified before model training?Yes
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