NLP Lineage

11 May 2023 · Efficiency and infrastructure

100K context

100K context windows

Anthropic

Date
11 May 2023
What the date marks
Announcement
Kind
Product
Organisation
Anthropic
Access
API only
Threads
In the guided tour
Chapter 10. Reading more than you remember
Learning phases
4. Embeddings and RAG

Notable: no later milestone in the data is documented as descending from it. How levels are decided

Read the primary source (anthropic.com)

Full summary in progress. Until then, this page shows the milestone's record from the research data.

What it changed

Before it

Production context windows were 4K to 8K tokens, occasionally 32K.

The problem

Anything book-length had to be chunked and retrieved rather than read.

What it introduced

A deployed 100,000-token window, enough for a novel or a large codebase in one prompt.

Why it matters

It opened the long-context era in products and began the running argument about retrieval versus a bigger window.

What modern models inherit

The expectation that a model can read a whole document.

What came next

Gemini 1.5's million tokens, and the long-context race through 2026.

Before and after

The milestones it descends from, and the milestones that descend from it. Work developed alongside it is not counted. Weak links are left out unless you include them.

Before: where it came from

100K context has 26 documented ancestors across 8 chapters, reaching back to Recurrent networks in 1990. Showing its direct parents and the 11 turning points among them.

100K context has 26 documented ancestors across 8 chapters, reaching back to Recurrent networks in 1990. Showing all of them.

100K context has 35 recorded ancestors, including indirect ones, across 8 chapters, reaching back to Recurrent networks in 1990. Showing its direct parents and the 11 turning points among them.

100K context has 35 recorded ancestors, including indirect ones, across 8 chapters, reaching back to Recurrent networks in 1990. Showing all of them.

  1. 2023100K contextThis milestone.
  2. 2023Claude100K context built directly on thisChapter 7. The assistant goes public
  3. 2022Constitutional AIClaude built directly on thisChapter 6. Teaching models what people want
  4. 2022HH-RLHFClaude was influenced by thisChapter 6. Teaching models what people want
  5. 2022Chain of thought Turning pointConstitutional AI was influenced by thisChapter 12. Thinking longer
  6. 2021HHH assistantHH-RLHF built directly on thisChapter 6. Teaching models what people want
  7. 2020RLHF for summarisation Turning pointHH-RLHF built directly on thisChapter 6. Teaching models what people want
  8. 2020GPT-3 Turning pointChain of thought was influenced by thisChapter 5. Scale becomes a science
  9. 2020Scaling laws Turning pointGPT-3 was influenced by thisChapter 5. Scale becomes a science
  10. 2019RLHF for LMsRLHF for summarisation built directly on thisChapter 6. Teaching models what people want
  11. 2019Sparse Transformer Turning pointGPT-3 built directly on thisChapter 4. Pretrain once, use everywhere
  12. 2019GPT-2 Turning pointGPT-3 built directly on this
    RLHF for LMs built directly on this    Chapter 4. Pretrain once, use everywhere
  13. 2019WebTextGPT-2 was influenced by thisChapter 4. Pretrain once, use everywhere
  14. 2018GPT-1GPT-2 built directly on thisChapter 4. Pretrain once, use everywhere
  15. 2017RLHF originConstitutional AI was influenced by thisChapter 6. Teaching models what people want
  16. 2017Transformer Turning pointGPT-2 built directly on this
    Sparse Transformer built directly on this
    Scaling laws built directly on this    Chapter 3. Attention, then only attention
  17. 2016Layer normGPT-2 built directly on thisChapter 3. Attention, then only attention
  18. 2015Residual connectionsTransformer was influenced by thisChapter 3. Attention, then only attention
  19. 2015LM pretraining Turning pointGPT-1 was influenced by thisChapter 2. Words become vectors
  20. 2015BPE tokenizationGPT-2 built directly on thisChapter 2. Words become vectors
  21. 2015Luong attentionTransformer was influenced by thisChapter 3. Attention, then only attention
  22. 2014AdamTransformer was influenced by thisChapter 2. Words become vectors
  23. 2014Seq2seq Turning pointTransformer built directly on thisChapter 2. Words become vectors
  24. 2014Attention Turning pointTransformer built directly on thisChapter 3. Attention, then only attention
  25. 2014GRU / encoder-decoderAttention built directly on thisChapter 2. Words become vectors
  26. 1997LSTM Turning pointSeq2seq built directly on thisChapter 1. Language as probability
  27. 1990Recurrent networksLSTM was influenced by thisChapter 1. Language as probability
  1. 2023100K contextThis milestone.
  2. 2023Claude100K context built directly on thisChapter 7. The assistant goes public
  3. 2022Constitutional AIClaude built directly on thisChapter 6. Teaching models what people want
  4. 2022HH-RLHFClaude was influenced by thisChapter 6. Teaching models what people want
  5. 2022Chain of thought Turning pointConstitutional AI was influenced by thisChapter 12. Thinking longer
  6. 2021HHH assistantHH-RLHF built directly on thisChapter 6. Teaching models what people want
  7. 2020RLHF for summarisation Turning pointHH-RLHF built directly on thisChapter 6. Teaching models what people want
  8. 2020GPT-3 Turning pointChain of thought was influenced by thisChapter 5. Scale becomes a science
  9. 2020Scaling laws Turning pointGPT-3 was influenced by thisChapter 5. Scale becomes a science
  10. 2019RLHF for LMsRLHF for summarisation built directly on thisChapter 6. Teaching models what people want
  11. 2019Sparse Transformer Turning pointGPT-3 built directly on thisChapter 4. Pretrain once, use everywhere
  12. 2019GPT-2 Turning pointGPT-3 built directly on this
    RLHF for LMs built directly on this    Chapter 4. Pretrain once, use everywhere
  13. 2019WebTextGPT-2 was influenced by thisChapter 4. Pretrain once, use everywhere
  14. 2019Transformer-XLSparse Transformer was influenced by this (indirect influence)Chapter 4. Pretrain once, use everywhere
  15. 2018GPT-1GPT-2 built directly on thisChapter 4. Pretrain once, use everywhere
  16. 2018ELMoGPT-1 was influenced by this (indirect influence)Chapter 4. Pretrain once, use everywhere
  17. 2018ULMFiTGPT-1 was influenced by this (indirect influence)Chapter 4. Pretrain once, use everywhere
  18. 2017CoVeELMo was influenced by thisChapter 3. Attention, then only attention
  19. 2017RLHF originConstitutional AI was influenced by thisChapter 6. Teaching models what people want
  20. 2017Transformer Turning pointGPT-2 built directly on this
    Sparse Transformer built directly on this
    Scaling laws built directly on this    Chapter 3. Attention, then only attention
  21. 2017ConvS2STransformer was influenced by this (indirect influence)Chapter 3. Attention, then only attention
  22. 2016Layer normGPT-2 built directly on thisChapter 3. Attention, then only attention
  23. 2015Residual connectionsTransformer was influenced by thisChapter 3. Attention, then only attention
  24. 2015LM pretraining Turning pointGPT-1 was influenced by thisChapter 2. Words become vectors
  25. 2015BPE tokenizationGPT-2 built directly on thisChapter 2. Words become vectors
  26. 2015Luong attentionTransformer was influenced by thisChapter 3. Attention, then only attention
  27. 2014AdamTransformer was influenced by thisChapter 2. Words become vectors
  28. 2014GloVeELMo built directly on thisChapter 2. Words become vectors
  29. 2014Seq2seq Turning pointTransformer built directly on thisChapter 2. Words become vectors
  30. 2014Attention Turning pointTransformer built directly on thisChapter 3. Attention, then only attention
  31. 2014GRU / encoder-decoderAttention built directly on thisChapter 2. Words become vectors
  32. 2013word2vecELMo was influenced by thisChapter 2. Words become vectors
  33. 2011NLP from scratchELMo was influenced by thisChapter 1. Language as probability
  34. 2003Neural LMNLP from scratch was influenced by this
    word2vec was influenced by this    Chapter 1. Language as probability
  35. 1997LSTM Turning pointSeq2seq built directly on this
    ELMo built directly on this    Chapter 1. Language as probability
  36. 1990Recurrent networksLSTM was influenced by thisChapter 1. Language as probability

After: what it led to

No documented descendants are recorded for 100K context without weak links.

No documented descendants are recorded for 100K context without weak links.

100K context has 9 recorded descendants, including indirect ones, across 3 chapters, reaching forward to Gemini 3.1 in 2026. Showing its direct descendants.

100K context has 9 recorded descendants, including indirect ones, across 3 chapters, reaching forward to Gemini 3.1 in 2026. Showing all of them.

  1. 2023100K contextThis milestone.
  2. 2023Lost in the middlewas influenced by 100K context (indirect influence)Chapter 10. Reading more than you remember
  3. 2024Gemini 1.5was influenced by Lost in the middle (indirect influence)Chapter 10. Reading more than you remember
  4. 2024Gemini 2.0built directly on Gemini 1.5Chapter 14. Where the threads meet now
  5. 2025Gemini 2.5was influenced by Gemini 1.5Chapter 12. Thinking longer
  6. 2025IMO goldbuilt directly on Gemini 2.5Chapter 12. Thinking longer
  7. 2025IMO-ProofBenchwas influenced by IMO goldChapter 12. Thinking longer
  8. 2025Gemini 3was influenced by Gemini 1.5Chapter 14. Where the threads meet now
  9. 2026Gemini 3 Deep Thinkbuilt directly on Gemini 3Chapter 12. Thinking longer
  10. 2026Gemini 3.1built directly on Gemini 3Chapter 14. Where the threads meet now

Direct links and their sources

Where it came from

Built directly on

What it led to

Influenced

Developed alongside