Meta
Meta has 92 models in Epoch AI's notable models list, 9 of them released since January 2025; the largest training run with a compute estimate is Llama 4 Behemoth (preview), at 5.2e25 FLOP. Its published safety framework is the Superintelligence Scaling Framework, and 3 of its 5 indexed releases since January 2025 had a safety card on the day of release.
- 5.2e25 FLOP largest training run, Llama 4 Behemoth (preview), 2025 Notable AI Models Epoch AI Inclusion is editorial ("notability criteria"), not exhaustive. Compute figures are frequently estimates; check the per-row confidence and notes columns before treating any value as measured. Cite Epoch AI, "Notable AI Models". https://epoch.ai/data/ai-models. Retrieved 28 October 2026 via AI Safety Tracker, https://aisafetytracker.org/developers/meta/.
- 9 notable models released since January 2025
- 3 of 5 releases since January 2025 with a system card on the day Release Safety Disclosure Index This site Our own reading of the system card, model card or safety report each developer published for each release Epoch AI lists from 2025, joined to Epoch's list. Every cell is what a developer says about its own model under its own framework: whether a card came out with the release, whether it reports dangerous-capability results, which government institutes it names as pre-release testers, and whether it says a risk threshold was reached. None of it is verified. A release Epoch adds after the last review is shown as awaiting review. Cite AI Safety Tracker, "Release Safety Disclosure Index". https://aisafetytracker.org/alignment/#releases. Retrieved 18 October 2026. Notable AI Models Epoch AI Inclusion is editorial ("notability criteria"), not exhaustive. Compute figures are frequently estimates; check the per-row confidence and notes columns before treating any value as measured. Cite Epoch AI, "Notable AI Models". https://epoch.ai/data/ai-models. Retrieved 18 October 2026 via AI Safety Tracker, https://aisafetytracker.org/developers/meta/.
- 17 sites it owns in Epoch's data-centre hub
Models
Every model Epoch AI judges notable from Meta, by year, with the largest training run each year where Epoch has a compute estimate.
| Year | Models | Models, relative | Largest run that year |
|---|---|---|---|
| 2016 | 5 | ResNeXt-101 (64×4d), 1.2e19 FLOP | |
| 2017 | 4 | ConvS2S (ensemble of 8 models), 5.6e19 FLOP | |
| 2018 | 3 | ResNeXt-101 32x48d, 8.7e21 FLOP | |
| 2019 | 17 | XLM-RoBERTa, 2.1e22 FLOP | |
| 2020 | 7 | ESM1b, 5.1e21 FLOP | |
| 2021 | 10 | XLMR-XXL, 3.4e22 FLOP | |
| 2022 | 16 | OPT-175B, 4.3e23 FLOP | |
| 2023 | 12 | Llama 2-70B, 8.1e23 FLOP | |
| 2024 | 7 | Llama 3.1-405B, 3.8e25 FLOP | |
| 2025 | 4 | Llama 4 Behemoth (preview), 5.2e25 FLOP | |
| 2026 | 5 | - |
Most recent
| Published | Model | Domain | Released as | Training compute |
|---|---|---|---|---|
| 2026-10-12 | Muse Wonder Full | Language | API only | - |
| 2026-10-12 | Muse Wonder | Language | API only | - |
| 2026-09-02 | Muse Spark 1.3 | Language | API only | - |
| 2026-08-05 | Muse Spark 1.2 | Language | API only | - |
| 2026-04-08 | Muse Spark | Multimodal | API only | - |
| 2025-11-10 | Meta's Generative Ads Model (GEM) | Recommendation | Unreleased | - |
| 2025-04-05 | Llama 4 Scout | Multimodal | Open weights | 4.1e24 |
| 2025-04-05 | Llama 4 Maverick | Multimodal | Open weights | 2.2e24 |
| 2025-04-05 | Llama 4 Behemoth (preview) | Multimodal | Unreleased | 5.2e25 |
| 2024-12-13 | Apollo 7B | Multimodal | Unreleased | - |
Earlier models 82
| Published | Model | Domain | Released as | Training compute |
|---|---|---|---|---|
| 2024-12-06 | Llama 3.3 70B | Language | Open weights | 6.9e24 |
| 2024-10-12 | Llama-3.1-Nemotron-70B-Instruct | Language | Open weights | 7.9e24 |
| 2024-10-04 | Movie Gen Video | Video | Unreleased | 1.6e24 |
| 2024-09-24 | Llama 3.2 11B | Multimodal | Open weights | 5.8e23 |
| 2024-07-23 | Llama 3.1-405B | Language | Open weights | 3.8e25 |
| 2024-04-18 | Llama 3-70B | Language | Open weights | 7.9e24 |
| 2023-12-08 | SeamlessM4T | Speech | Open weights | - |
| 2023-12-07 | Llama Guard | Language | Open weights | 1.6e23 |
| 2023-11-08 | MultiBand Diffusion | Audio | Open weights | 2.6e19 |
| 2023-07-18 | Llama 2-70B | Language | Open weights | 8.1e23 |
| 2023-07-18 | Llama 2-7B | Language | Open weights | 8.4e22 |
| 2023-06-08 | MusicGen | Audio | Open weights | - |
| 2023-05-09 | ImageBind | Multimodal | Open weights | - |
| 2023-04-14 | DINOv2 | Vision | Open weights | 7.4e21 |
| 2023-04-09 | Incoder-6.7B | Language | Open weights | 3.0e21 |
| 2023-04-05 | Segment Anything Model | Vision | Open weights | 7.8e21 |
| 2023-03-05 | AudioGen | Audio | Open weights | 9.5e21 |
| 2023-02-24 | LLaMA-65B | Language | Open weights | 5.5e23 |
| 2022-11-22 | CICERO | Games | Open weights | - |
| 2022-11-16 | Galactica | Language | Open weights | 3.2e23 |
| 2022-10-24 | EnCodec | Audio | Open weights | - |
| 2022-10-11 | Diplodocus | Games | Open weights | - |
| 2022-09-29 | Make-A-Video | Video | API only | - |
| 2022-08-10 | BlenderBot 3 | Language | Open weights | 4.3e23 |
| 2022-07-21 | ESM2-15B | Biology | Open weights | 7.4e22 |
| 2022-07-06 | NLLB | Language | Open weights | 1.8e22 |
| 2022-05-02 | OPT-175B | Language | Open weights | 4.3e23 |
| 2022-04-14 | Sparse all-MLP | Language | Unreleased | 5.3e20 |
| 2022-03-24 | Make-A-Scene | Image generation | Unreleased | 6.4e21 |
| 2022-03-10 | ViT-G (model soup) | Vision | Open weights | 3.4e21 |
| 2022-01-20 | data2vec (vision) | Vision | Open weights | - |
| 2022-01-20 | data2vec (speech) | Speech | Open weights | - |
| 2022-01-20 | data2vec (language) | Language | Open weights | - |
| 2022-01-07 | Detic | Vision | Open weights | 2.3e19 |
| 2021-12-20 | XGLM-7.5B | Language | Open weights | 2.3e22 |
| 2021-12-16 | Contriever | Language | Open weights | 1.6e20 |
| 2021-11-11 | Masked Autoencoders ViT-H | Vision | Open weights | 4.6e20 |
| 2021-08-17 | XLMR-XXL | Language | Open weights | 3.4e22 |
| 2021-08-03 | 6-Act Tether | Robotics | Open weights | - |
| 2021-07-29 | SEER | Vision | Open weights | 1.8e22 |
| 2021-07-27 | HuBERT | Speech | Open weights | 5.5e21 |
| 2021-03-05 | Generative BST | Language | Open weights | 1.4e22 |
| 2021-02-13 | MSA Transformer | Biology | Open weights | 5.5e21 |
| 2021-01-15 | DeiT-B | Vision | Open weights | 7.9e19 |
| 2020-12-15 | ESM1b | Biology | Open weights | 5.1e21 |
| 2020-10-22 | wave2vec 2.0 LARGE | Speech | Open weights | 3.9e21 |
| 2020-08-03 | DeLighT | Language | Unreleased | 3.8e18 |
| 2020-07-02 | SemExp | Robotics | Open weights | - |
| 2020-05-26 | DETR | Vision | Open weights | 4.0e20 |
| 2020-05-22 | Retrieval-Augmented Generator | Language | Open weights | - |
| 2020-02-21 | Feedback Transformer | Language | Unreleased | 7.7e18 |
| 2019-12-19 | DD-PPO | Robotics | Unreleased | 7.8e20 |
| 2019-11-19 | Transformer - LibriVox + Decoding/Rescoring | Speech | Open weights | - |
| 2019-11-13 | MoCo | Vision | Open weights | - |
| 2019-11-10 | Sandwich Transformer | Language | Unreleased | 2.4e19 |
| 2019-11-10 | CamemBERT | Language | Open weights | 8.3e20 |
| 2019-11-05 | XLM-RoBERTa | Language | Open weights | 2.1e22 |
| 2019-11-01 | Base LM + kNN LM + Continuous Cache | Language | Unreleased | 3.1e19 |
| 2019-10-29 | BART-large | Language | Open weights | - |
| 2019-10-02 | AlphaX-1 | Vision | Unreleased | 8.9e17 |
| 2019-09-25 | Adaptive Inputs + LayerDrop | Language | Open weights | - |
| 2019-07-11 | Pluribus | Games | Unreleased | 6.6e16 |
| 2019-07-01 | RoBERTa Large | Language | Open weights | 8.5e21 |
| 2019-06-17 | LaNet-L (CIFAR-10) | Vision | Open weights | - |
| 2019-06-14 | FixRes ResNeXt-101 WSL | Vision | Open weights | - |
| 2019-06-01 | XLM | Language | Open weights | - |
| 2019-05-31 | DLRM-2020 | Recommendation | Unreleased | 4.0e18 |
| 2019-05-02 | ResNeXt-101 Billion-scale | Vision | Open weights | - |
| 2018-09-28 | Transformer (Adaptive Input Embeddings) WT103 | Language | Open weights | 4.5e19 |
| 2018-08-28 | Big Transformer for Back-Translation | Language | Open weights | 4.8e20 |
| 2018-05-02 | ResNeXt-101 32x48d | Vision | Open weights | 8.7e21 |
| 2017-08-07 | RetinaNet-R101 | Vision | Not recorded | 2.1e18 |
| 2017-08-07 | RetinaNet-R50 | Vision | Not recorded | - |
| 2017-07-25 | ConvS2S (ensemble of 8 models) | Language | Not recorded | 5.6e19 |
| 2017-03-30 | Mask R-CNN | Vision | Not recorded | - |
| 2016-11-16 | ResNeXt-101 (64×4d) | Vision | Open weights | 1.2e19 |
| 2016-11-16 | ResNeXt-50 | Vision | Open weights | - |
| 2016-11-07 | DTN (Domain Transfer Network) | Vision | Unreleased | - |
| 2016-08-25 | DenseNet-264 | Vision | Open weights | - |
| 2016-07-15 | Character-enriched word2vec | Language | Not recorded | - |
| 2015-06-08 | YOLO | Vision | Not recorded | - |
| 2014-06-23 | DeepFace | Vision | Not recorded | - |
Notability is Epoch's editorial judgement, and compute figures are its estimates, most of them not disclosed by the developer. Includes models Epoch credits to Facebook and Facebook AI Research. Notable AI Models Epoch AI Inclusion is editorial ("notability criteria"), not exhaustive. Compute figures are frequently estimates; check the per-row confidence and notes columns before treating any value as measured. Cite Epoch AI, "Notable AI Models". https://epoch.ai/data/ai-models. Retrieved 28 October 2026 via AI Safety Tracker, https://aisafetytracker.org/developers/meta/. Notable AI Models Epoch AI Inclusion is editorial ("notability criteria"), not exhaustive. Compute figures are frequently estimates; check the per-row confidence and notes columns before treating any value as measured. Cite Epoch AI, "Notable AI Models". https://epoch.ai/data/ai-models. Retrieved 28 October 2026 via AI Safety Tracker, https://aisafetytracker.org/developers/meta/.
Safety framework and release disclosures
The frontier safety framework Meta has published, and what it published at each release since January 2025.
- Framework
- Superintelligence Scaling Framework since 2025
- Its own scale
- Risk thresholds: moderate or lower, high, critical
- Risk domains
- CBRN, cyber, loss of control
- External evaluation
- not stated
- Commits to stop
- yes: Commits to not deploying, or not continuing, until mitigations are in place.
Version 2.1, 2 October 2026, renamed from the Advanced AI Scaling Framework of April 2026, itself renamed from the Frontier AI Framework. Since version 2 the critical tier reads Develop with Mitigations, formerly Stop, so every tier permits development and deployment once mitigations are validated to bring risk down to moderate. Version 2.1 adds a third loss-of-control outcome, containment failure in training and evaluation. Covers any model trained with 1e26 FLOP or more.
- 3 of 5 with a system card on the day
- 2 that no card names
- 0 naming a government institute that tested before release
- 1 stated to have reached a threshold in its own framework
| Released | Model | Card | Results | Government tested | Threshold |
|---|---|---|---|---|---|
| 2026-09-02 | Muse Spark 1.3 The launch post links a capability evaluation methodology, not a safety report. | none | - | - | - |
| 2026-08-05 | Muse Spark 1.2 The launch post links a capability evaluation methodology, not a safety report. | none | - | - | - |
| 2026-04-08 | Muse Spark | at release | results | none named | reached Chemical and biological capability likely in the high risk category before safeguards |
| 2025-04-05 | Llama 4 Scout | at release | summary only | none named | not stated Says Llama 4 does not enable catastrophic cyber outcomes; names no threshold |
| 2025-04-05 | Llama 4 Maverick | at release | summary only | none named | not stated Says Llama 4 does not enable catastrophic cyber outcomes; names no threshold |
Awaiting review: Muse Wonder Full, Muse Wonder, listed by Epoch since the index was last read.
Every entry is Meta's own statement about its own models under its own framework. The rules behind each column are on the alignment page. Frontier Safety Framework Index This site Our own reading of each developer's published safety framework and model cards. Every field is what the developer says about itself: these are self-assessments under self-written frameworks with no common scale and no external audit, and they are indexed here for exactly that reason. Not a ranking, and not a verification. Cite AI Safety Tracker, "Frontier Safety Framework Index". https://aisafetytracker.org/alignment/#frameworks. Retrieved 18 October 2026. Release Safety Disclosure Index This site Our own reading of the system card, model card or safety report each developer published for each release Epoch AI lists from 2025, joined to Epoch's list. Every cell is what a developer says about its own model under its own framework: whether a card came out with the release, whether it reports dangerous-capability results, which government institutes it names as pre-release testers, and whether it says a risk threshold was reached. None of it is verified. A release Epoch adds after the last review is shown as awaiting review. Cite AI Safety Tracker, "Release Safety Disclosure Index". https://aisafetytracker.org/alignment/#releases. Retrieved 18 October 2026. Notable AI Models Epoch AI Inclusion is editorial ("notability criteria"), not exhaustive. Compute figures are frequently estimates; check the per-row confidence and notes columns before treating any value as measured. Cite Epoch AI, "Notable AI Models". https://epoch.ai/data/ai-models. Retrieved 18 October 2026 via AI Safety Tracker, https://aisafetytracker.org/developers/meta/.
Its models against the compute thresholds
How many of Meta's models with a compute estimate sit at or above each training-compute threshold written into an instrument, and the first to cross it.
| Instrument | Threshold | Status | Its models above | First of them to cross |
|---|---|---|---|---|
| Regulation (EU) 2024/1689, Article 51(2) European Union | 1e25 | in force | 2 | Llama 3.1-405B, 2024-07-23 |
| SB 53, Transparency in Frontier Artificial Intelligence Act United States (California) | 1e26 | in force | 0 | - |
| Enforcement Decree of the AI Basic Act, Article 23 South Korea | 1e26 | in force | 0 | - |
| Executive Order 14110, section 4.2 United States (federal) | 1e26 | revoked | 0 | - |
| SB 1047, Safe and Secure Innovation for Frontier AI Models Act United States (California) | 1e26 | vetoed | 0 | - |
| RAISE Act, Responsible AI Safety and Education Act United States (New York) | 1e26 | enacted | 0 | - |
Arithmetic on Epoch's compute estimates against the number in each instrument, not a legal finding about any model. The full timeline is on the capability page. Compute Threshold Index This site Training-compute thresholds written into law, executive action or published policy, with the text that sets each one. Whether a given model crosses a threshold here is arithmetic on Epoch's compute estimates, most of which are estimates rather than disclosures, and is not a legal finding about any model or developer. Cite AI Safety Tracker, "Compute Threshold Index". https://aisafetytracker.org/capability/#thresholds. Retrieved 23 October 2026. Notable AI Models Epoch AI Inclusion is editorial ("notability criteria"), not exhaustive. Compute figures are frequently estimates; check the per-row confidence and notes columns before treating any value as measured. Cite Epoch AI, "Notable AI Models". https://epoch.ai/data/ai-models. Retrieved 23 October 2026 via AI Safety Tracker, https://aisafetytracker.org/developers/meta/.
Datacentres
Sites in Epoch AI's data-centre hub that Meta owns or is named as using, largest planned IT power first. Those it owns plan 5.6 GW of IT power between them.
| Site | Role | Country | Stage | IT power now | Planned |
|---|---|---|---|---|---|
| Meta Hyperion | owner, confident | United States | Under construction | - | 1.6 GW |
| Meta Prometheus | owner, confident | United States | Expanding | 471 MW | 1.0 GW |
| Meta Bowling Green | owner, confident | United States | Under construction | - | 422 MW |
| Meta Montgomery | owner, confident | United States | Expanding | 153 MW | 358 MW |
| QTS Eagle Mountain | owner, speculative | United States | Under construction | - | 304 MW |
| Meta Los Lunas | owner, confident | United States | Expanding | 118 MW | 218 MW |
| Meta Cheyenne | owner, confident | United States | Expanding | 152 MW | 207 MW |
| Meta-QTS Hillsboro 2 | owner, likely | United States | Operating | 180 MW | 180 MW |
| Meta Jeffersonville | owner, confident | United States | Operating | 178 MW | 178 MW |
| Meta Rosemount | owner, confident | United States | Operating | 178 MW | 178 MW |
| Meta Temple | owner, confident | United States | Operating | 178 MW | 178 MW |
| Meta Kuna | owner, confident | United States | Operating | 152 MW | 152 MW |
| Meta Huntsville | owner, confident | United States | Operating | 146 MW | 146 MW |
| Meta Eagle Mountain | owner, confident | United States | Operating | 133 MW | 133 MW |
| Meta Aiken | owner, confident | United States | Expanding | 69 MW | 129 MW |
| Meta Gallatin | owner, confident | United States | Operating | 87 MW | 87 MW |
| Meta Sarpy | owner, confident | United States | Operating | 80 MW | 80 MW |
Power is Epoch's estimate from imagery and permits, IT power where it states one. Confidence is Epoch's own label for each owner or user. AI Data Centers Epoch AI The largest AI sites Epoch has found, mostly in the United States. Epoch estimated in September 2026 that they hold 44% of the world's deployed AI compute, with a 90% interval of 23% to 81%. Power, compute and cost are Epoch's estimates from satellite imagery, permits and company statements; four in five IT power estimates fall within a factor of 1.4 of the true value. The owner is the owner of the chips, not necessarily of the building. Capacity after the date of retrieval is Epoch's projection. Coordinates are read from the hub's map page, which publishes them; the downloads do not. Cite Epoch AI, "AI Data Centers". https://epoch.ai/data/ai-data-centers. Retrieved 28 October 2026 via AI Safety Tracker, https://aisafetytracker.org/developers/meta/. AI Datacentre Index This site AI datacentre sites that Epoch AI's hub does not hold, recorded from the statements of developers, named tenants and planning authorities. Australia only so far. A site is included only where a primary source says it is built for AI compute or names the AI deployment inside it. Capacity is as the developer states it, IT or facility power as labelled, and is not an estimate; most of it is proposed and not yet approved. Coordinates are this site's placement of each stated address or area. Cite AI Safety Tracker, "AI Datacentre Index". https://aisafetytracker.org/datacentres/#added. Retrieved 28 October 2026.
Energy and emissions
What Meta has published about the energy behind its models, and what permits and press reporting add.
- Training energy or emissions Published
- GPU hours and emissions in every Llama model card since Llama 2. Llama 3.1 405B: 8,930 tCO2e location-based. Llama 3.1 model card
- Where it trains and serves Partly published
- Meta's own GPU clusters. Which site trained a given model is not stated. Llama 3.1 model card
- What powers those sites Published
- 100% of electricity matched with renewable energy since 2020, so market-based training emissions are reported as zero. Llama 3.1 model card
- Energy per prompt Not disclosed
- No per-prompt figure.
- 688 MWh to train Llama 2 70B (2023), developer model card, energy derived from published hardware hours
- 4,480 MWh to train Llama 3 70B (2024), developer model card, energy derived from published hardware hours
- 21,588 MWh to train Llama 3.1 405B (2024), developer model card, energy derived from published hardware hours
- 1,666 MWh to train Llama 4 Maverick (2025), developer model card, energy derived from published hardware hours
- 3,500 MWh to train Llama 4 Scout (2025), developer model card, energy derived from published hardware hours
- 100% of electricity matched with renewable purchases over since 2020 , as the company reports it
"Published" means the developer's own statement; "reported by others" means a permit, a regulator or the press. A company's clean-energy claims cover its whole operation, never one model. Environmental Disclosure Index This site Our own reading of what frontier developers and the operators of their data centres have published about training energy, emissions, power sources and energy per prompt, with permits and press reporting marked separately from company statements. Most cells record an absence. Company figures are self-reported and mostly unaudited, and market-based emissions reflect contracts rather than the grid at the hour of use. Cite AI Safety Tracker, "Environmental Disclosure Index". https://aisafetytracker.org/environment/. Retrieved 23 October 2026. Environmental Disclosure Index This site Our own reading of what frontier developers and the operators of their data centres have published about training energy, emissions, power sources and energy per prompt, with permits and press reporting marked separately from company statements. Most cells record an absence. Company figures are self-reported and mostly unaudited, and market-based emissions reflect contracts rather than the grid at the hour of use. Cite AI Safety Tracker, "Environmental Disclosure Index". https://aisafetytracker.org/environment/. Retrieved 23 October 2026.
Usage
The token volumes and user counts Meta has stated, each in its own scope.
- 1 billion monthly users, Meta AI, as of May 2025 source
Volumes are normalised to a month and are comparable only within one company's own scope. Every series with its scope is on the usage page. AI Usage Disclosure Index This site Our own compilation of the token volumes, user counts and shares of use that companies and studies have put on the record, each linked to where it was stated. Scopes, units and token definitions differ between companies, so no two figures are comparable without the scope printed beside each. Figures known only through press reporting are marked as such. Not a market share estimate, and not a measure of any company's actual volume beyond what it chose to state. Cite AI Safety Tracker, "AI Usage Disclosure Index". https://aisafetytracker.org/usage/. Retrieved 23 October 2026. AI Usage Disclosure Index This site Our own compilation of the token volumes, user counts and shares of use that companies and studies have put on the record, each linked to where it was stated. Scopes, units and token definitions differ between companies, so no two figures are comparable without the scope printed beside each. Figures known only through press reporting are marked as such. Not a market share estimate, and not a measure of any company's actual volume beyond what it chose to state. Cite AI Safety Tracker, "AI Usage Disclosure Index". https://aisafetytracker.org/usage/. Retrieved 23 October 2026.
Not recorded for Meta: a stated token volume.