What is a context window?

The context window is the maximum number of tokens a model can consider at once, covering both your prompt and the generated completion. If your input plus expected output exceeds the window, the request fails or older content is truncated. Larger windows let you pass more documents, history or examples in a single call.

Are these prices and limits exact?

They are realistic published estimates at the time of the last update, but providers change pricing and limits frequently. Always confirm the current figures on the provider's official pricing and model pages before committing to production budgets.

Why do input and output tokens cost different amounts?

Generating output tokens is more compute-intensive than reading input tokens, so most providers charge several times more for output. This is why prompt engineering that reduces verbose completions can cut your bill significantly even when the input stays the same.

What does modality mean in this table?

Modality lists the input types a model accepts — text only, text plus images (vision), or text plus images and audio. Choosing a multimodal model lets you send screenshots, documents or audio in the same request, but those models are often pricier than text-only equivalents.

Does a bigger context window mean better answers?

Not automatically. A large window lets you supply more context, but models can still lose track of details buried in the middle of very long inputs. Use retrieval to pass only the most relevant chunks rather than dumping everything just because the window allows it.

Model Context Length Reference Table

Picking the right LLM means balancing context window, price and modalities. This reference table puts the headline numbers for the major models in one searchable, filterable place so you can answer “which model fits my prompt and budget?” in seconds.

How it works

Each row lists a model’s provider, maximum context window (in tokens), input and output cost per one million tokens, supported modalities and training-data cutoff. Use the search box to jump to a model, the dropdowns to filter by provider or modality, and the column headers to sort by context size or cost. Everything runs locally in your browser.

Notes and caveats

Treat the figures as realistic estimates — providers revise pricing and limits often, so verify the current numbers on official pages before locking a budget. Remember that output tokens usually cost several times more than input tokens, so trimming verbose completions saves real money. And a huge context window is not a substitute for good retrieval: models can still miss details buried in the middle of very long inputs, so pass the most relevant content rather than everything you have.