From GPT-4 Turbo’s 128k Context to GPT-5: How Far We’ve Come

When OpenAI launched GPT-4 Turbo with a 128k context window in late 2023, it felt like a seismic moment. For the first time, developers and researchers could pass the equivalent of 300+ pages of text into a single query. Entire books, codebases, or legal archives could fit inside one prompt.

Fast forward to today — mid-2025 — and that milestone already feels like a historical marker, a signpost on a much faster road of progress than anyone expected. Let’s take a step back to remember what the 128k leap meant, and then look forward at how far we’ve come.

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The 128k Revolution: A Brief Look Back

In context:

• Before GPT-4 Turbo: Context windows maxed out around 32k tokens, which was enough for chapters or small datasets, but still required heavy chunking and retrieval pipelines.

• With GPT-4 Turbo: The 128k context window represented a 4x leap. Suddenly, workflows shifted:

• Developers tried end-to-end ingestion of entire repositories.

• Researchers experimented with multi-document analysis.

• Knowledge workers pasted entire contracts or case studies for one-shot reasoning.

It was not perfect:

• Latency rose significantly with very long prompts.
• The “lost in the middle” effect made recall uneven across the sequence.
• The cost per call ballooned as input token counts grew.

Still, the 128k context window was a psychological breakthrough. It proved that scaling context length was possible, and it forced the ecosystem to rethink how much retrieval-augmented generation (RAG) was necessary.

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The Technical Challenge It Solved

To support 128k tokens, OpenAI had to tackle three big problems:

1. Quadratic Attention Scaling

   Naïve self-attention grows as O(n^2) with respect to sequence length. At 128k, that’s billions of operations per layer.

   • The solution: FlashAttention, streaming attention, and kernel-level optimizations.

2. KV Cache Memory Explosion

   Each token must store keys and values across all layers. Read more here

   • For 128k sequences, this added tens of gigabytes of overhead in FP16 precision.
   • Techniques like quantization and memory-efficient caching became essential.

3. Latency vs. Usability

   Reading 128k tokens before generation began could take seconds. Developers had to balance completeness vs. responsiveness.

These lessons directly paved the way for even larger contexts — 512k, 1M, and beyond.

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Looking Forward: The State of Context Windows in 2025

Today, GPT-4 Turbo’s 128k feels modest. Here’s how the field has moved:

• GPT-4o (2024):

  Introduced 1M token context, streaming multimodality, and lower latency. Suddenly, entire research corpora or days of conversation history became searchable in-session.

• Frontier Labs (2024–2025):

  Academic and open-source efforts (Anthropic’s Claude 3.5, Google’s Gemini 1.5 Pro, Mistral’s LongNet variants) showed that long-context isn’t just about scale — it’s about information retention and selective focus.

• GPT-5 (2025):

  Pushed further with smarter context compression, hierarchical memory structures, and seamless multimodal grounding. Models can now reason across text, images, and structured data inside context windows that dwarf 128k.

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What Changed in Practice

• RAG Is Evolving

  Retrieval is no longer just “chunk and embed.” With massive contexts, models can take in broader swaths of raw data, while RAG pipelines focus on precision curation and source attribution.

• User Expectations Shifted

  128k felt magical in 2023; today, people expect models to handle entire workflows without babysitting. The bar for “usable memory” has risen dramatically.

• Efficiency Became Critical

  Just because you can throw a million tokens at a model doesn’t mean you should. Smart developers optimize:

  • Compression
  • Summarization layers
  • Selective attention mechanisms

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Why 128k Still Matters

Even if 128k is no longer the cutting edge, it was a proof point that unlocked:

• The viability of large-context attention in production.
• A shift in AI research priorities toward scalable memory.
• The foundation for the massive contexts we now take for granted.

Think of it like the first iPhone: by today’s standards, it looks limited, but without it, the ecosystem we now live in would not exist.

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Conclusion: A Look Back, A Look Ahead

GPT-4 Turbo’s 128k context window was more than a spec bump. It changed how we thought about working with AI systems, and it laid the groundwork for the context-rich, multimodal, memory-augmented LLMs