GPAI Ledger › Grok 4.5 (xAI) › Capture 11 Aug 2026

Grok 4.5 — capture 20260811T103721Z

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Public  Summary  of  Training  Content  for  Grok  4.5   Version  of  the  Summary:   V1  Last  update:   8  July  2026  General  information
1.  General  information  1.1.  Provider  identification   Provider  name  and  contact  details:
xAI  LLC,  1450  Page  Mill  Rd,  Palo  Alto,  CA  94304
Authorised  representative  name  and  contact  details:
EDSR,  Valukoja  8/2,  2nd  floor,  Tallinn,  Estonia,  EE-11415
1.2.  Model  identification   Versioned  model  name(s):  Grok  4.5  Model  dependencies:  Not  applicable Date  of  placement  of  the  model  on  the  Union  market:  14  July  2026
1.3  Modalities,  overall  training  data  size  and  other  characteristics   Modality
Training  data  size
Types  of  content
 ☒  Text
☐  Less  than  1  billion  tokens  ☐  1billion  to  10  trillions  tokens  ☒  More  than  10  trillions  tokens

Grok  4.5  was  pre-trained  using  a  carefully  designated  data  recipe  that  incorporates  a  diverse  corpus  of  publicly  available  information,  including,  for  example,  certain  scientific  text,  legal  and  official  documents,  social  media  posts,  and  source  code.  The  training  data  also  encompasses  content  produced  by  third  parties,  data  from  users  and  contractors,  as  well  as  internally  generated  data.  Training  data  was  curated  and  filtered  for  quality  and  safety.
☒  Image
☐  Less  than  1  million  images  ☐  1Million  to1  billion  images  ☒  More  than  1  billion  images
Grok  4.5  was  trained  on  a  large  and  diverse  set  of  images  from  publicly  available  sources,  including,  for  example,  certain  photographs,  visual  art  works,  infographics,  and  social  media  images.  The  training  data  may  also  encompass  images  produced  by  third  parties,  from  users  and  contractors,  as  well  as  internally  generated  content.  Training  data  was  curated  and  filtered  for  quality  and  safety.
☒  Audio
 1

☐  Less  than  10  000  hours  ☐  10  000  to1  million  hours
Grok  4.5  was  trained  on  a  diverse  set  of  publicly  available  audio  data,  including,  for  example,  certain  clips  and  audio
1
 Excluding  audio  that  is  part  of  video,  as  this  should  be  reported  under  the  “video”  modality  instead.  Furthermore,  the  Commission
understands

the

modality

of

‘audio’

to

include

‘speech’.

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☒  More  than  1  million  hours  from  social  media  videos.  The  training  data  may  also  encompass  audio  content  produced  by  third  parties,  from  users  and  contractors,  as  well  as  internally  generated  content.  Training  data  was  curated  and  filtered  for  quality  and  safety.
☒  Video
☐  Less  than  10  000  hours  ☐  10  000  to1  million  hours   ☒  More  than  1  million  hours
Grok  4.5  was  trained  on  publicly  available  audiovisual  content,  including,  for  example,  certain  clips  and  social  media  videos.   The  training  data  may  also  encompass  audiovisual  content  produced  by  third  parties,  from  users  and  contractors,  as  well  as  internally  generated  content.  Training  data  was  curated  and  filtered  for  quality  and  safety.
☐  Other
Not  applicable  Not  applicable
 Latest  date  of  data  acquisition/collection  for  model  training:
The  data  used  to  train  Grok  4.5  includes  different  datasets  from  varying  time  periods,  with  data  collected  no  later  than  June  2026.  The  model  is  also  continuously  refined  on  new  data  after  this  date. Description  of  the  linguistic  characteristics  of  the  overall  training  data:
Multilingual,  with  strong  English  coverage  and  substantial  representation  across  EU  official  languages  and  other  languages  from  around  the  world.
Other  relevant  characteristics  of  the  overall  training  data:
Grok’s  training  corpus  is  designed  for  a  highly  capable  text-output  model  with  strong  multimodal  understanding.  It  draws  from  a  wide  range  of  written  materials,  text-bearing  images,  and  audiovisual  content.  The  corpus  is  intentionally  broad,  aiming  for  extensive  coverage  across  topics,  geographies,  languages,  and  formats.
Additional  comments  (optional):
2.  List  of  data  sources
2.  List  of  data  sources   2.1.  Publicly  available  datasets      Have  you  used  publicly  available  datasets  to  train  the  model?    ☒  Yes      ☐  No  If  yes,  specify  the  modality(ies)  of  the  content  covered  by  the  datasets  concerned:
☒  Text    ☒  Image    ☒  Video   ☒  Audio  ☐  Other
List  of  large  publicly  available  datasets:
The  primary  source  of  Grok  4.5’s  training  is  data  consisting  of   text,  images,  audio  content  and  audiovisual  content  from  publicly  available  sources  from  the  Internet.  General  description  of  other  publicly  available  datasets  not  listed  above:
   Additional  comments  (optional):

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2.2  Private  non-publicly  available  datasets  obtained  from  third  parties   2.2.1.  Datasets  commercially  licensed  by  rightsholders  or  their  representatives  Have  you  concluded  transactional  commercial  licensing  agreement(s)  with  rightsholder(s)  or  with  their  representatives?
☒ Yes      ☐  No
If  yes,  specify  the  modality(ies)  of  the  content  covered  by  the  datasets  concerned:   ☒ Text    ☒ Image    ☐  Video   ☐  Audio  ☐  Other
2.2.2.  Private  datasets  obtained  from  other  third  parties  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?
☒ Yes      ☐  No
If  yes,  specify  the  modality(ies)  of  the  content  covered  by  the  datasets  concerned:   ☒ Text    ☒ Image    ☒ Video  ☒ Audio  ☒ Other If  publicly  known,  list  private  datasets  obtained  from  other  third  parties:   Not  applicable
General  description  of  non-publicly  known  private  datasets  obtained  from  third  parties
xAI  partnered  with  third  parties  to  access  data  spanning  a  diverse  set  of  domains  and  contexts  to  improve  Grok  4.5,  consisting  of  text,  images,  audio  and/or  video.   Additional  comments  (optional):    2.3  Data  crawled  and  scraped  from  online  sources   Were  crawlers  used  by  the  provider  or  on  behalf  of?
☒  Yes      ☐  No
If  yes,  specify  crawler  name(s)/identifier(s):  xAI  Web  Crawler
Purposes  of  the  crawler(s):
 xAI  uses  a  web  crawler  to  crawl  publicly  available  internet  pages  to  help  improve  language  understanding,  reasoning,  and  overall  capabilities.
General  description  of  crawler  behaviour:
 xAI  uses  a  web  crawler  to  discover  and  scan  publicly  available  internet  pages  and  rescan  for  updates  and  analysis.
Period  of  data  collection:  From  01/2024  to  06/2026
Comprehensive  description  of  the  type  of  content  and  online  sources  crawled:
Content  encompasses  a  wide  variety  of  publicly  available  internet  data  including  educational,  scientific,  technical,  government,  institutional,  and  general-interest  sources.  These  materials  may   consist  of  text,  images,  audio,  video,  and  associated  metadata.  Type  of  modality  covered:   ☒  Text    ☒  Image    ☒  Video   ☒  Audio   ☐  Other
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Summary  of  the  most  relevant  domain  names  crawled:
The  most  relevant  crawled  domains  include  academic/research  repositories,  patent  and  technical  databases,  legal/government  resources,  document-sharing  platforms,  community  sites,  and  region-specific  portals. Additional  comments  (optional):  Not  applicable
2.4  User  data   Was  data  from  user  interactions  with  the  AI  model  (e.g.  user  input  and  prompts)  used  to  train  the  model?
 ☒ Yes     ☐  No
Was  data  collected  from  user  interactions  with  the  provider’s  other  services  or  products  used  to  train  the  model?
 ☒ Yes      ☐  No
If  yes,  provide  a  general  description  of  the  provider’s  services  or  products  that  were  used  to  collect  the  user  data:
Subject  to  privacy  settings,  controls,  user  requests,  opt-outs,  and  our  policies,  xAI  may  use  interactions  with  Grok  to  help  train  and  improve  our  models,  including  Grok  4.5.  For  more  information  on  how  we  use  data  to  improve  model  performance,  please  visit: https://x.ai/privacy Type  of  modality  covered:  ☒  Text    ☒  Image    ☒  Video   ☒  Audio  ☐  Other
Additional  comments  (optional):
 xAI  uses  advanced  data  filtering  processes  to  reduce  personal  information  from  training  data.  We  also  use  advanced  data  processes  to  reduce  the  amount  of  personal  data  in  our  training  data.
2.5  Synthetic  data   Was  synthetic  AI-generated  data  created  by  the  provider  or  on  their  behalf  to  train  the  model?
 ☒  Yes      ☐  No
If  yes,  modality  of  the  synthetic  data:   ☒  Text    ☒  Image   ☒  Video   ☒  Audio   ☐  Other
If  yes,  specify  the  general-purpose  AI  model(s)  used  to  generate  the  synthetic  data  if  available  on  the  market:
xAI  generates  synthetic  data  using  its  own  general-purpose  AI  models,  including  Grok.   Information  about  other  AI  models,  including  provider’s  own  AI  model(s)  not  available  on  the  market,  used  to  generate  synthetic  data  to  train  the  model  to  which  this  Summary  applies:
 xAI  uses  a  combination  of  internal  models  to  generate  synthetic  data  for  targeted  training  objectives.
Additional  comments  (optional):  Not  applicable
  2.6  Other  sources  of  data   Have  data  sources  other  than  those  described  in  Sections  2.1  to  2.5  been  used  to  train  the  model?   ☒  Yes      ☐  No
If  yes,  provide  a  narrative  description  of  these  data  sources  and  the  data:
 xAI  and  third  parties  create  data  spanning  a  diverse  set  of  domains  and  contexts  to  help  our  models
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improve  on  a  wide  variety  of  tasks.   Additional  comments  (optional):
1.  Data  processing  aspects  3.  Data  processing  aspects  3.1.  Respect  of  reservation  of  rights  from  text  and  data  mining  exception  or
limitation

  Are  you  a  Signatory  to  the  Code  of  Practice  for  general-purpose  AI  models  that  includes  commitments  to  respect  reservations  of  rights  from  the  TDM  exception  or  limitation?
 ☐  Yes      ☒ No
Describe  the  measures  implemented  before  model  training  to  respect  reservations  of  rights  from  the  TDM  exception  or  limitation  before  and  during  data  collection,  including  the  opt-out  protocols  and  solutions  honoured  by  the  provider  or,  as  applicable,  by  third  parties  from  which  datasets  have  been  obtained:
 xAI  respects  opt-out  signals.  For  example,  xAI  does  not  train  on  X  data  from  X  users  who  have  opted  out  of  training.
Additional  comments  (optional):
 3.2  Removal  of  illegal  content
General  description  of  measures  taken:
xAI  applies  preprocessing  and  screening  measures  designed  to  avoid  or  remove  content  that  may  be  illegal  from  our  training  data.  These  measures  include  filtering,  keyword-based  rules  and  model-based  classifiers.   3.3.  Other  information  (optional)  Other  relevant  information  about  data  processing  (optional):

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