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DeepSeek

Frontier AI Lab
Founded Jul 2023 (2 years old)HQ: Hangzhou, Zhejiang, Chinadeepseek.com
Policy & Governance
Scorecards3

Grades from external scorecards. We mirror published grades only; per-source methodology lives at the link in each panel header. See the scorecards directory for cross-org comparison.

AI Lab Watch

by Zach Stein-PerlmanPublished 2025-09-01Source ↗
Overall:Very Weak
Risk info sharing
Very Weak
Misuse prevention
Very Weak
Planning
Very Weak
Risk assessment
Very Weak
Boosting safety research
Very Weak
Scheming risk prevention
Very Weak
Prep for extreme security
Very Weak

Foundation Model Transparency Index

by Stanford CRFMPublished 2025-12-01Source ↗
Overall:32
Agent Protocols
100
AI bug bounty
0
Amount of usage
0
AUP enforcement frequency
0
AUP enforcement process
100
Basic model properties
100
Benchmarked inference
0
Benefits Assessment
0
Capabilities evaluation
100
Capabilities taxonomy
100
Carbon emissions for final training run
0
Change log
100
Classification of usage data
0
Code access
0
Compute hardware for final training run
100
Compute provider
100
Compute usage for final training run
0
Compute usage including R&D
0
Consumer/enterprise usage
0
Crawling
0
Data acquisition methods
0
Data domain composition
0
Data laborer practices
0
Data language composition
0
Data processing methods
100
Data processing purpose
100
Data processing techniques
0
Data replicability
0
Data retention and deletion policy
0
Data size
100
Deeper model properties
100
Detection of machine-generated content
0
Development duration for final training run
100
Distribution channels with usage data
0
Documentation for responsible use
0
Downstream
19.4
Acceptable use policy
60
Accountability
33.3
Downstream mitigations
0
Impact
0
Model Behavior Policy
75
Post-deployment monitoring
0
Usage data
0
Energy usage for final training run
0
Enterprise mitigations
0
Enterprise users
0
External data access
0
External developer mitigations
0
External products and services
0
External reproducibility of capabilities evaluation
0
External reproducibility of mitigations evaluation
0
External reproducibility of risks evaluation
0
External risk evaluation
0
Feedback mechanisms
0
Foundation model roadmap
100
Geographic statistics
0
Government commitments
100
Government use
0
Instructions for data generation
0
Intermediate tokens
100
Internal compute allocation
100
Internal product and service mitigations
0
Internal products and services
0
Licensed data compensation
0
Licensed data sources
0
Misuse incident reporting protocol
0
Mitigations efficacy
0
Mitigations taxonomy
100
Mitigations taxonomy mapped to risk taxonomy
0
Model
46.7
Capabilities
50
Model cost
0
Model dependencies
100
Model access
50
Model information
75
Model Mitigations
20
Model objectives
100
Release
62.5
Model response characteristics
100
Risks
20
Model stages
100
Model theft prevention measures
0
New human-generated data sources
0
Notice of usage data used in training
0
Open weights
100
Organization chart
0
Oversight mechanism
0
Permitted and prohibited users
100
Permitted, restricted, and prohibited model behaviors
0
Permitted, restricted, and prohibited uses
100
Post-deployment coordination with government
0
Pre-deployment risk evaluation
0
Public datasets
0
Quantization
100
Regional policy variations
0
Release stages
0
Researcher credits
0
Responsible disclosure policy
0
Risks evaluation
0
Risks taxonomy
100
Risk thresholds
0
Safe harbor
0
Security incident reporting protocol
0
Specialized access
0
Synthetic data purpose
100
Synthetic data sources
100
System prompt
100
Terms of use
100
Top distribution channels
100
Train-test overlap
0
Upstream
32.4
Compute
44.4
Data Acquisition
16.7
Data Processing
66.7
Data Properties
20
Methods
66.7
Other resources
0
Usage data used in training
0
Users of internal products and services
0
Versioning protocol
0
Water usage for final training run
0
Whistleblower protection
0