Transparency-focused index scoring developers on 100 indicators across upstream resources, the model itself, and downstream use.
Foundation Model Transparency Index
by Stanford CRFM·Latest: v1.2 December 2025·Source ↗
Transparency-focused index scoring developers on 100 indicators across upstream resources, the model itself, and downstream use.
Organizations
13
Dimensions
123
Snapshots
3
Latest wave
v1.2 December 2025
Grades by dimension — v1.2 December 2025
Rows are organizations; columns are the dimensions FMTI scores. Cell text is the published grade (overall first, then per-pillar).
| Organization | Overall | Acceptable use policy | Accountability | Agent Protocols | AI bug bounty | Amount of usage | AUP enforcement frequency | AUP enforcement process | Basic model properties | Benchmarked inference | Benefits Assessment | Capabilities | Capabilities evaluation | Capabilities taxonomy | Carbon emissions for final training run | Change log | Classification of usage data | Code access | Compute | Compute hardware for final training run | Compute provider | Compute usage for final training run | Compute usage including R&D | Consumer/enterprise usage | Crawling | Data Acquisition | Data acquisition methods | Data domain composition | Data laborer practices | Data language composition | Data Processing | Data processing methods | Data processing purpose | Data processing techniques | Data Properties | Data replicability | Data retention and deletion policy | Data size | Deeper model properties | Detection of machine-generated content | Development duration for final training run | Distribution channels with usage data | Documentation for responsible use | Downstream | Downstream mitigations | Energy usage for final training run | Enterprise mitigations | Enterprise users | External data access | External developer mitigations | External products and services | External reproducibility of capabilities evaluation | External reproducibility of mitigations evaluation | External reproducibility of risks evaluation | External risk evaluation | Feedback mechanisms | Foundation model roadmap | Geographic statistics | Government commitments | Government use | Impact | Instructions for data generation | Intermediate tokens | Internal compute allocation | Internal product and service mitigations | Internal products and services | Licensed data compensation | Licensed data sources | Methods | Misuse incident reporting protocol | Mitigations efficacy | Mitigations taxonomy | Mitigations taxonomy mapped to risk taxonomy | Model | Model access | Model Behavior Policy | Model cost | Model dependencies | Model information | Model Mitigations | Model objectives | Model response characteristics | Model stages | Model theft prevention measures | New human-generated data sources | Notice of usage data used in training | Open weights | Organization chart | Other resources | Oversight mechanism | Permitted and prohibited users | Permitted, restricted, and prohibited model behaviors | Permitted, restricted, and prohibited uses | Post-deployment coordination with government | Post-deployment monitoring | Pre-deployment risk evaluation | Public datasets | Quantization | Regional policy variations | Release | Release stages | Researcher credits | Responsible disclosure policy | Risk thresholds | Risks | Risks evaluation | Risks taxonomy | Safe harbor | Security incident reporting protocol | Specialized access | Synthetic data purpose | Synthetic data sources | System prompt | Terms of use | Top distribution channels | Train-test overlap | Upstream | Usage data | Usage data used in training | Users of internal products and services | Versioning protocol | Water usage for final training run | Whistleblower protection |
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| AI21 Labs | 66 | 80 | 100 | 100 | 100 | 0 | 0 | 100 | 100 | 0 | 100 | 75 | 100 | 100 | 0 | 100 | 0 | 0 | 22.2 | 100 | 100 | 0 | 0 | 100 | 100 | 91.7 | 100 | 0 | 100 | 0 | 66.7 | 100 | 100 | 0 | 0 | 0 | 100 | 0 | 100 | 100 | 0 | 0 | 100 | 75 | 100 | 0 | 100 | 100 | 0 | 100 | 0 | 100 | 0 | 0 | 100 | 0 | 100 | 0 | 100 | 100 | 71.4 | 100 | 100 | 0 | 100 | 100 | 100 | 0 | 66.7 | 100 | 0 | 100 | 100 | 70 | 50 | 100 | 0 | 100 | 75 | 60 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 50 | 100 | 100 | 100 | 100 | 0 | 71.4 | 0 | 100 | 100 | 100 | 87.5 | 100 | 0 | 100 | 100 | 60 | 100 | 100 | 100 | 100 | 0 | 100 | 100 | 100 | 100 | 0 | 0 | 52.9 | 20 | 100 | 0 | 100 | 0 | 100 |
| Alibaba | 26 | 60 | 33.3 | 100 | 0 | 0 | 0 | 100 | 100 | 0 | 0 | 50 | 100 | 100 | 0 | 100 | 0 | 0 | 11.1 | 0 | 100 | 0 | 0 | 0 | 0 | 16.7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20 | 0 | 0 | 100 | 100 | 0 | 0 | 0 | 0 | 22.2 | 40 | 0 | 100 | 0 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 0 | 100 | 0 | 100 | 0 | 0 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 66.7 | 0 | 0 | 0 | 0 | 40 | 50 | 50 | 0 | 100 | 75 | 0 | 100 | 100 | 100 | 0 | 0 | 0 | 100 | 0 | 0 | 0 | 100 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 0 | 62.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 100 | 100 | 0 | 100 | 100 | 0 | 17.6 | 0 | 0 | 0 | 100 | 0 | 0 |
| Amazon | 39 | 80 | 33.3 | 100 | 100 | 0 | 0 | 100 | 0 | 0 | 0 | 50 | 100 | 100 | 0 | 100 | 0 | 0 | 11.1 | 0 | 100 | 0 | 0 | 0 | 0 | 16.7 | 100 | 0 | 0 | 0 | 33.3 | 100 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 100 | 0 | 100 | 100 | 50 | 100 | 0 | 100 | 0 | 0 | 100 | 0 | 0 | 0 | 0 | 100 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 100 | 0 | 100 | 0 | 0 | 0 | 66.7 | 0 | 0 | 100 | 100 | 50 | 50 | 75 | 0 | 0 | 0 | 60 | 100 | 100 | 100 | 100 | 0 | 0 | 0 | 0 | 0 | 100 | 100 | 100 | 100 | 100 | 57.1 | 0 | 0 | 0 | 100 | 75 | 100 | 0 | 100 | 100 | 40 | 0 | 100 | 0 | 100 | 100 | 100 | 0 | 0 | 100 | 100 | 0 | 17.6 | 20 | 0 | 0 | 0 | 0 | 0 |
| Anthropic | 46 | 100 | 100 | 100 | 0 | 0 | 100 | 100 | 0 | 0 | 100 | 25 | 100 | 0 | 0 | 100 | 100 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 100 | 25 | 100 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 100 | 0 | 100 | 100 | 72.2 | 100 | 0 | 100 | 0 | 0 | 100 | 0 | 0 | 0 | 0 | 100 | 0 | 0 | 100 | 100 | 0 | 28.6 | 0 | 100 | 0 | 100 | 100 | 0 | 0 | 0 | 0 | 100 | 100 | 100 | 56.7 | 50 | 100 | 0 | 100 | 25 | 80 | 0 | 100 | 0 | 100 | 0 | 100 | 0 | 0 | 0 | 100 | 100 | 100 | 100 | 100 | 57.1 | 0 | 0 | 0 | 100 | 75 | 100 | 100 | 100 | 100 | 60 | 100 | 100 | 100 | 100 | 0 | 0 | 0 | 100 | 100 | 100 | 0 | 8.8 | 60 | 0 | 0 | 100 | 0 | 100 |
| DeepSeek | 32 | 60 | 33.3 | 100 | 0 | 0 | 0 | 100 | 100 | 0 | 0 | 50 | 100 | 100 | 0 | 100 | 0 | 0 | 44.4 | 100 | 100 | 0 | 0 | 0 | 0 | 16.7 | 0 | 0 | 0 | 0 | 66.7 | 100 | 100 | 0 | 20 | 0 | 0 | 100 | 100 | 0 | 100 | 0 | 0 | 19.4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 100 | 0 | 100 | 0 | 0 | 0 | 100 | 100 | 0 | 0 | 0 | 0 | 66.7 | 0 | 0 | 100 | 0 | 46.7 | 50 | 75 | 0 | 100 | 75 | 20 | 100 | 100 | 100 | 0 | 0 | 0 | 100 | 0 | 0 | 0 | 100 | 0 | 100 | 0 | 0 | 0 | 0 | 100 | 0 | 62.5 | 0 | 0 | 0 | 0 | 20 | 0 | 100 | 0 | 0 | 0 | 100 | 100 | 100 | 100 | 100 | 0 | 32.4 | 0 | 0 | 0 | 0 | 0 | 0 |
| 41 | 80 | 100 | 100 | 100 | 0 | 0 | 100 | 0 | 0 | 100 | 25 | 100 | 0 | 0 | 100 | 0 | 0 | 11.1 | 0 | 100 | 0 | 0 | 0 | 100 | 33.3 | 0 | 0 | 0 | 0 | 33.3 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 0 | 100 | 0 | 0 | 100 | 55.6 | 100 | 0 | 100 | 0 | 0 | 100 | 100 | 0 | 0 | 0 | 0 | 0 | 100 | 0 | 100 | 0 | 28.6 | 0 | 100 | 0 | 100 | 0 | 0 | 0 | 66.7 | 0 | 0 | 100 | 0 | 43.3 | 50 | 75 | 0 | 0 | 0 | 40 | 100 | 100 | 100 | 100 | 0 | 100 | 0 | 0 | 0 | 100 | 100 | 100 | 100 | 0 | 42.9 | 0 | 0 | 0 | 100 | 87.5 | 100 | 100 | 100 | 100 | 20 | 0 | 100 | 0 | 100 | 0 | 100 | 100 | 0 | 100 | 100 | 0 | 23.5 | 0 | 0 | 0 | 100 | 0 | 100 | |
| IBM | 95 | 80 | 100 | 100 | 100 | 100 | 0 | 100 | 100 | 100 | 100 | 75 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 91.7 | 100 | 100 | 100 | 0 | 100 | 100 | 100 | 100 | 0 | 100 | 100 | 100 | 100 | 0 | 100 | 100 | 85.7 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 93.3 | 100 | 100 | 100 | 100 | 100 | 80 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 0 | 100 | 80 | 100 | 100 | 100 | 100 | 100 |
| Meta | 31 | 40 | 0 | 0 | 100 | 0 | 0 | 0 | 100 | 0 | 100 | 50 | 100 | 100 | 100 | 100 | 0 | 0 | 22.2 | 0 | 100 | 0 | 0 | 0 | 100 | 33.3 | 100 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20 | 0 | 0 | 100 | 100 | 100 | 0 | 0 | 100 | 33.3 | 80 | 0 | 100 | 0 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 14.3 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 40 | 50 | 75 | 0 | 100 | 75 | 0 | 0 | 100 | 0 | 0 | 0 | 100 | 100 | 0 | 0 | 0 | 100 | 0 | 100 | 0 | 28.6 | 0 | 0 | 100 | 0 | 50 | 0 | 100 | 100 | 100 | 20 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 100 | 100 | 0 | 0 | 20.6 | 0 | 100 | 0 | 0 | 0 | 0 |
| Midjourney | 14 | 60 | 0 | 0 | 0 | 0 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 100 | 100 | 25 | 40 | 0 | 0 | 0 | 0 | 100 | 100 | 0 | 0 | 0 | 0 | 0 | 100 | 0 | 0 | 0 | 28.6 | 0 | 0 | 0 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 16.7 | 0 | 25 | 0 | 0 | 0 | 0 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 100 | 0 | 100 | 0 | 0 | 0 | 0 | 100 | 0 | 62.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 100 | 100 | 0 | 0 | 20 | 0 | 0 | 0 | 0 | 0 |
| Mistral | 18 | 60 | 33.3 | 100 | 100 | 0 | 0 | 100 | 0 | 0 | 0 | 25 | 100 | 0 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 100 | 0 | 0 | 100 | 33.3 | 80 | 0 | 100 | 0 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 0 | 100 | 0 | 100 | 0 | 14.3 | 0 | 0 | 0 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20 | 25 | 0 | 0 | 0 | 0 | 20 | 0 | 0 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 0 | 100 | 0 | 100 | 0 | 42.9 | 0 | 0 | 0 | 0 | 37.5 | 0 | 0 | 100 | 0 | 0 | 0 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| OpenAI | 35 | 60 | 100 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 0 | 25 | 100 | 0 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 100 | 8.3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 100 | 0 | 0 | 100 | 0 | 0 | 100 | 58.3 | 100 | 0 | 100 | 0 | 0 | 100 | 0 | 0 | 0 | 0 | 100 | 0 | 0 | 0 | 100 | 0 | 14.3 | 0 | 100 | 0 | 100 | 100 | 0 | 0 | 0 | 0 | 100 | 100 | 100 | 43.3 | 0 | 75 | 0 | 0 | 0 | 80 | 0 | 100 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 100 | 100 | 100 | 100 | 100 | 71.4 | 0 | 0 | 0 | 100 | 62.5 | 100 | 0 | 100 | 100 | 60 | 100 | 100 | 100 | 100 | 0 | 0 | 0 | 0 | 100 | 0 | 0 | 2.9 | 20 | 0 | 0 | 100 | 0 | 100 |
| Writer | 72 | 80 | 66.7 | 100 | 100 | 100 | 0 | 100 | 100 | 100 | 100 | 50 | 100 | 100 | 100 | 100 | 100 | 0 | 100 | 100 | 100 | 100 | 100 | 100 | 0 | 58.3 | 100 | 0 | 100 | 100 | 100 | 100 | 100 | 100 | 40 | 0 | 100 | 100 | 0 | 100 | 100 | 100 | 100 | 83.3 | 100 | 100 | 100 | 100 | 0 | 100 | 100 | 0 | 0 | 0 | 0 | 100 | 0 | 100 | 100 | 0 | 85.7 | 100 | 100 | 100 | 100 | 100 | 0 | 0 | 33.3 | 100 | 0 | 100 | 0 | 60 | 50 | 50 | 100 | 100 | 75 | 40 | 0 | 100 | 100 | 100 | 0 | 100 | 0 | 100 | 100 | 100 | 100 | 0 | 100 | 100 | 85.7 | 100 | 0 | 100 | 100 | 87.5 | 100 | 100 | 100 | 100 | 40 | 0 | 100 | 100 | 0 | 0 | 100 | 100 | 0 | 100 | 100 | 0 | 70.6 | 100 | 100 | 100 | 100 | 100 | 0 |
| xAI | 14 | 60 | 0 | 0 | 0 | 0 | 0 | 100 | 0 | 0 | 0 | 25 | 100 | 0 | 0 | 100 | 0 | 0 | 11.1 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 22.2 | 40 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 100 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 16.7 | 0 | 75 | 0 | 0 | 0 | 0 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 100 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 0 | 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 100 | 100 | 100 | 0 | 2.9 | 0 | 0 | 0 | 0 | 0 | 0 |
Snapshot history
| Wave | Published | Orgs | License | Links |
|---|---|---|---|---|
| v1.2 December 2025latest | 2025-12-01 | 13 | CC-BY-4.0 | |
| v1.1 May 2024 | 2024-05-21 | 14 | CC-BY-4.0 | |
| v1.0 October 2023 | 2023-10-18 | 10 | CC-BY-4.0 |
Methodology
Grades are mirrored from upstream sources. We do not score organizations ourselves — see the source link for full methodology and per-indicator detail. Citation of published grades is consistent with fair-use attribution.