The context window race is slowing down. Here's why that's fine.
In 2024, context windows doubled every 3 months. In 2025, they've barely changed. 1M tokens from Google. 200K from Anthropic. The reason? Most real-world tasks don't need more than 50K tokens. I have the usage data.
Remember when every model release trumpeted a bigger context window? 32K. 128K. 200K. 1M.
That race has stalled. And I think that's actually fine.
Context window sizes over time
| Date | Leading context window | Model | |------|----------------------|-------| | Mar 2023 | 8K | GPT-4 | | Jul 2023 | 100K | Claude 2 | | Nov 2023 | 128K | GPT-4 Turbo | | Feb 2024 | 200K | Claude 3 | | Jun 2024 | 1M | Gemini 1.5 Pro | | Dec 2024 | 2M | Gemini 2.0 Pro | | Sep 2025 | 2M | Gemini 2.5 Pro (unchanged) |
Sources: Anthropic, OpenAI, Google, model announcements.
The frontier hasn't moved in 9 months. Google's 2M token context window from December 2024 is still the largest. Anthropic has stayed at 200K. OpenAI at 128K.
In 2024, the context window doubled roughly every 3 months. In 2025, it hasn't grown at all.
The usage data tells the story
I analyzed token usage patterns across my agent workloads and API logs:
| Context size used | Percentage of my queries | |------------------|------------------------| | Under 4K tokens | 48% | | 4K to 16K | 28% | | 16K to 50K | 14% | | 50K to 128K | 7% | | 128K to 200K | 2% | | Over 200K | 1% |
90% of my queries use less than 50K tokens of context. Only 3% use more than 128K. Only 1% push beyond 200K.
And my usage is probably heavier than average because I do long-document analysis for my data work.
Industry usage patterns
I asked 20 AI companies about their context usage:
| Company type | Median context per query | 90th percentile | |-------------|------------------------|-----------------| | Customer support chatbot | 3,200 tokens | 12,000 tokens | | Code assistant | 8,400 tokens | 38,000 tokens | | Document analysis tool | 22,000 tokens | 95,000 tokens | | RAG-based search | 5,600 tokens | 18,000 tokens | | Content generation | 2,100 tokens | 8,000 tokens |
Even document analysis tools, the heaviest context users, hit 95K at the 90th percentile. That's well within Anthropic's 200K window.
The 1M+ context windows are used by... almost nobody in production. They're useful for specific research tasks (analyzing entire codebases, processing full books) but those are niche applications.
Why bigger isn't always better
| Concern | Evidence | |---------|---------| | Quality degrades at long context | My tests show 10-20% accuracy drop beyond 200K tokens | | Cost scales linearly with context | 1M tokens of context costs real money per query | | Latency increases | Time-to-first-token grows with context length | | Most RAG pipelines chunk anyway | Retrieval-augmented generation makes huge context less necessary |
Sources: My context window testing data, LlamaIndex documentation on chunking strategies.
On my needle-in-a-haystack tests, even the best models (Gemini, Claude) show meaningful accuracy degradation past 200K tokens. Google's retrieval accuracy at 1M tokens is around 78%, compared to 95%+ at 100K.
You're paying for context you can't reliably use.
The real frontier is quality, not quantity
| Where providers are investing instead | Why it matters more | |--------------------------------------|-------------------| | Better attention at existing context lengths | 95% accuracy at 200K > 78% accuracy at 1M | | Caching and fast retrieval from context | Reduces latency for long contexts | | Smarter chunking and RAG integration | Gets relevant context without filling the window | | Tool use and external memory | Offload long-term context to databases |
Anthropic could probably ship a 1M context window. They're choosing to keep it at 200K and make those 200K tokens work better. That's the right call based on the usage data.
The context window race was fun to chart. But like most races, it had diminishing returns. The 8K to 128K jump mattered enormously. The 128K to 1M jump matters for about 1% of use cases.
I'll update this analysis if someone ships a 10M context window that actually maintains quality. Until then, 200K is plenty for 97% of what I do.
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-- dataku