AI Safety Tracker

Meta

92 notable models 56 with a compute estimate Superintelligence Scaling Framework

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 Licence CC-BY-4.0 Updated weekly Retrieved , today 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 Licence MIT Updated monthly Retrieved , 10 days ago Data JSON CSV 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 Licence CC-BY-4.0 Updated weekly Retrieved , 10 days ago 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.

Notable models by year, with the largest training run each year.
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

Notable models, newest first.
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
Notable models, newest first.
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 Licence CC-BY-4.0 Updated weekly Retrieved , today 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 Licence CC-BY-4.0 Updated weekly Retrieved , today 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
Meta releases since January 2025, with the card published for each.
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 Licence MIT Updated quarterly Retrieved , 10 days ago Data JSON CSV 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 Licence MIT Updated monthly Retrieved , 10 days ago Data JSON CSV 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 Licence CC-BY-4.0 Updated weekly Retrieved , 10 days ago 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.

Training-compute thresholds and this developer's models above each.
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 Licence MIT Updated quarterly Retrieved , 5 days ago Data JSON CSV 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 Licence CC-BY-4.0 Updated weekly Retrieved , 5 days ago 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.

Datacentre sites this developer owns or uses, with power now and planned.
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 Licence CC-BY-4.0 Updated several times a week Retrieved , today 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 Licence MIT Updated quarterly Retrieved , today Data JSON 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 Licence MIT Updated quarterly Retrieved , 5 days ago Data 7 datasets 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 Licence MIT Updated quarterly Retrieved , 5 days ago Data 7 datasets 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 Licence MIT Updated quarterly Retrieved , 5 days ago Data 3 datasets 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 Licence MIT Updated quarterly Retrieved , 5 days ago Data 3 datasets 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.