LLM Context Window Comparison
How much can each model actually hold? Toggle real-world reference points — books, codebases, contracts — to see what fits in each context window.
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<iframe src="https://benchlm.ai/embed/context-window" width="100%" height="640" frameborder="0" loading="lazy" title="LLM Context Window Comparison · BenchLM"></iframe>What is a context window?
A context window is the maximum amount of text — measured in tokens, not words — that a language model can read and reason about in a single request. The window holds both your prompt and the model's response. Once you exceed it, the model cannot see the earlier parts of the conversation or document.
Why context size matters
Bigger windows unlock different workflows. With 8K tokens you can hold a short essay; at 200K you can fit a full novel; at 2M you can drop in an entire mid-size codebase. The reference points on the chart above make those trade-offs concrete: instead of “200K tokens,” think “one War & Peace.”
Tokens vs. words
English prose tokenizes at roughly 1.3 tokens per word — the word “tokenization” alone is two or three tokens depending on the model. Code is denser (closer to 1 token per 3.5 characters), and non-Latin scripts can be much denser still. The chart and reference points use these averages; your specific input will vary. Use the token counter to measure your exact input.
When does context size actually limit you?
Two limits matter in practice. First, the hard ceiling: anything beyond the window gets truncated. Second, the effectivewindow: most models start losing track of details well before their advertised limit — the “lost in the middle” problem. For real long-context work, check long-context benchmarks like RULER or Needle-in-a-Haystack, not just the advertised window size on this chart.
Frequently asked questions
What is a context window?
A context window is the maximum amount of text (measured in tokens) that a language model can process in a single request. This includes both your prompt and the model's response. Larger context windows let models read longer documents, hold longer conversations, and work with bigger codebases at once.
Does a bigger context window mean a better model?
Not necessarily. Window size only tells you the maximum input length the model accepts. Many models advertise 1M+ token windows but degrade in accuracy well before reaching that limit on real tasks (this is called the "lost in the middle" problem). Use this chart to compare advertised sizes; check long-context benchmarks for actual quality at length.
Why is the chart on a logarithmic scale?
Context windows span four orders of magnitude — from a few thousand tokens to tens of millions. On a linear axis, smaller models would be invisible next to Gemini's 2M window. Log scale keeps every model legible.
How do you estimate the token counts for the reference points?
English prose tokenizes at roughly 1.3 tokens per word — the word "tokenization" alone is two or three tokens depending on the model. Code is denser (closer to 1 token per 3.5 characters), and non-Latin scripts can be much denser still. The reference points use these averages; your specific input will vary.
Why don't I see model X?
The default view shows a curated list of frontier and popular models. Toggle "Show all models" to see every model in our catalog with a published context window.
Can I embed this chart on my own site?
Yes — copy the iframe snippet from the "Embed this chart" section. The embedded view shows the chart and a small attribution link back to BenchLM.
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