GPAI Ledger › Gemini 3 Pro (Google) › Capture 11 Aug 2026

Gemini 3 Pro — capture 20260811T103456Z

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Public  Summary  of  Training  Content  for  General-Purpose  AI  models
 Version  of  the  Summary:  v1
 Last  update:  2  July  2026

 1.  General  information
 1.1.  Provider  identification
 Provider  name  and
 contact  details:
 Google  Ireland  Limited
 Gordon  House
 Barrow  Street
 Dublin  4
 Ireland
 Authorised
 representative  name
 and  contact  details:
 1.2.  Model  identification
 Versioned  model
 name(s):
 The  Gemini  3  Pro  family  of  models
 Model  dependencies:  Gemini  3  Pro  is  not  a  modification  or  a  fine-tune  of  a  prior  model.  Each  subsequent
 model  in  the  Gemini  3  Pro  family  is  based  on  Gemini  3  Pro  (see  each  model  card  for
 individual  model  details).  The  Gemini  3  Pro  family  includes  models  such  as:  Gemini  3
 Pro  Image,  Gemini  3  Flash,  Gemini  3.1  Pro,  Gemini  3.1  Flash  Image,  Gemini  3.1
 Flash-Lite,  Gemini  3.1  Flash  Live,  and  Gemini  3.5  Flash.
 Date  of  placement  of  the
 model  on  the  Union
 market:
 November  2025
 1.3  Modalities,  overall  training  data  size  and  other  characteristics
 Modality  Training  data  size  Types  of  content
 Text  Less  than  1  billion  tokens
 1  billion  to  10  trillion  tokens
 More  than  10  trillion  tokens
 The  model  trains  on  datasets
 containing  information  that
 can  be  represented  as
 written  language,  including
 software  code  and  publicly
 available  web  documents
 across  educational,
 government,  legal,  and
 research  sectors.
 2

 Image  Less  than  1  million  images
 1  Million  to  1  billion  images
 More  than  1  billion  images
 The  model  trains  on  datasets
 representing  static  visual
 information,  including
 photography,  diagrams,  and
 illustrations.
 Audio  Less  than  10  000  hours
 10  000  to  1  million  hours
 More  than  1  million  hours
 The  model  trains  on  datasets
 representing  sound  that  has
 been  recorded  and  digitized,
 including  speech  recordings
 and  sound  effects.
 Video  Less  than  10  000  hours
 10  000  to  1  million  hours
 More  than  1  million  hours
 The  model  trains  on  datasets
 containing  a  sequence  of
 image  frames  that  may  be
 accompanied  by  audio,
 including  performances,
 video  clips,  and  video  effects.
 Other
 Latest  date  of  data
 acquisition/collection  for
 model  training:
 The  knowledge  cut-off  date  for  Gemini  3  Pro  is  01  /  2025.  We  may  periodically
 update  models  after  this  date.
 Description  of  the
 linguistic  characteristics
 of  the  overall  training
 data:
 Our  models  are  trained  to  work  with  a  breadth  of  languages,  including  EU  official
 languages,  representative  of  the  internet  in  general.
 Other  relevant
 characteristics  of  the
 overall  training  data:
 The  pre-training  dataset  was  a  large-scale,  diverse  collection  of  data  encompassing
 a  wide  range  of  domains  and  modalities,  which  included  publicly-available
 web-documents,  code,  images,  audio  (including  speech  and  other  audio  types),  and
 video.  The  post-training  dataset  included  different  types  of  instruction  tuning  data,
 reinforcement  learning  data,  and  human-preference  data.
 Additional  comments
 (optional):
 2.  List  of  data  sources
 2.1.  Publicly  available  datasets
 Have  you  used  publicly  available  datasets  to  train  the  model?  Yes  No
 3

 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:
 Our  publicly  available  datasets  include  data  across  various  sectors  such  as
 educational,  government,  legal,  and  research  sectors  comprising  a  wide  variety  of
 media  types  and  languages.
 General  description  of
 other  publicly  available
 datasets  not  listed
 above:
 See  the  description  of  linguistic  characteristics  and  other  relevant  characteristics  of
 overall  training  data  in  Section  1.
 Additional  comments
 (optional):
 2.2  Private  non-publicly  available  datasets  obtained  from  third  parties
 2.2.1.  Datasets  commercially  license  d  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  fr  om  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  database  s,  o  r  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:
 None  of  the  private  datasets  subject  to  section  2.2.2  are  publicly  known.
 4

 General  description  of
 non-publicly  known
 private  datasets
 obtained  from  third
 parties
 Datasets  cover  subject  areas  including  educational  materials,  scientific  reasoning,
 and  mathematics.
 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):
 See  list  of  crawlers  here  .
 Purposes  of  the
 crawler(s):
 Google  uses  crawlers  to  discover  and  scan  websites,  find  information  for  building
 Google's  search  indexes,  perform  other  product  specific  crawls,  and  for  analysis.
 General  description  of
 crawler  behaviour:
 Google's  crawlers  are  used  to  to  discover  and  scan  websites,  find  information  for
 building  Google's  search  indexes,  perform  other  product  specific  crawls,  and  for
 analysis.  For  more  information  on  crawler  behaviour,  see  here  .
 Period  of  data
 collection:
 The  knowledge  cut-off  date  for  Gemini  3  Pro  is  01  /  2025.  We  may  periodically
 update  models  after  this  date.
 Comprehensive
 description  of  the  type
 of  content  and  online
 sources  crawled:
 Crawled  data  includes  a  broad  range  of  publicly  available  online  material,  such  as
 publicly  available  websites  across  educational,  government,  legal,  and  research
 sectors.  This  includes  a  wide  variety  of  media  types  and  languages.
 Type  of  modality
 covered:
 Text  Image  Video  Audio
 Other:
 Summary  of  the  most
 relevant  domain  names
 crawled:
 The  most  relevant  domains  crawled  include  publicly  available  websites  across
 educational,  government,  legal,  and  research  sectors  comprising  a  wide  variety  of
 media  types  and  languages.  Google  uses  crawlers  to  discover  and  scan  websites,
 find  information  for  building  Google's  search  indexes,  perform  other  product
 specific  crawls,  and  for  analysis.  See  list  of  our  common  crawlers  here  .  Google’s
 common  crawlers  obey  robots.txt  rules  when  crawling  automatically.  To  learn  more
 about  Google’s  crawlers,  visit  this  site  .
 Additional  comments
 (optional):
 2.4  User  data
 5

 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:
 In  accordance  with  Google’s  relevant  terms  of  service,  privacy  policy,  and
 service-specific  policies,  and  pursuant  to  user  consent  where  legally  required,  we
 use  data  collected  from  users  of  Google  products  and  services—such  as  user  chats
 with  Gemini  Apps—to  train  AI  models.
 Type  of  modality
 covered:
 Text  Image  Video  Audio
 Other:
 Additional  comments
 (optional):
 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:
 We  have  relied  on  the  externally  available  Gemini  3  family  of  models  to  generate
 synthetic  data.
 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:
 We  may  use  fine-tuned  versions  of  internal  models  and  non-GPAI  models  to
 generate  synthetic  data  for  training.
 6

 Additional  comments
 (optional):
 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:
 This  category  reflects  types  of  data  that  do  not  fit  within  the  categories  described
 above.  It  includes  datasets  that  Google  acquires  or  generates  in  the  course  of  its
 business  operations,  or  directly  from  its  workforce.
 Additional  comments
 (optional):
 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:
 Data  filtering  and  preprocessing  included  techniques  such  as  deduplication,
 honoring  robots.txt,  safety  filtering  in-line  with  Google's  commitment  to  advancing
 AI  safely  and  responsibly  ,  and  quality  filtering  to  mitigate  risks  and  improve  training
 data  reliability.  Specifically,  our  Google-Extended  control  lets  web  publishers
 manage  whether  content  Google  crawls  from  their  sites  may  be  used  for  training
 Gemini  models  that  power  Gemini  Apps  and  Gemini  Enterprise  Agent  Platform  ,  for
 grounding  in  Gemini  Apps  ,  and  for  the  Grounding  with  Google  Search  feature  on
 Gemini  Enterprise  Agent  Platform  .  The  data  filtering  and  preprocessing  process  may
 also  involve  filtering  irrelevant  or  harmful  content,  text,  and  other  modalities,
 including  filtering  content  that  is  pornographic,  violent,  or  violative  of  child  sexual
 abuse  material  (CSAM)  laws.
 Additional  comments
 (optional):
 3.2  Removal  of  illegal  content
 7

 General  description  of
 measures  taken:
 As  described  above,  data  filtering  and  preprocessing  included  techniques  such  as
 safety  filtering  in-line  with  Google's  commitment  to  advancing  AI  safely  and
 responsibly  .  This  process  may  involve  filtering  irrelevant  or  harmful  content,  text,  and
 other  modalities,  including  filtering  content  that  is  pornographic,  violent,  or  violative
 of  child  sexual  abuse  material  (CSAM)  laws.
 3.3.  Other  information  (optional)
 Other  relevant
 information  about  data
 processing  (optional):
 8