How much does it cost to use a full 200K context window in GPT-4?

Using GPT-4 Turbo's full 128K context window costs approximately $1.28 for input only. If you also generate a 4K response, the total reaches about $1.43 per request. For applications making hundreds of requests daily, this can quickly exceed $400-500 monthly just for context processing.

Is paying for a larger context window worth it compared to splitting requests?

Splitting requests into smaller chunks costs less in tokens but loses cross-document reasoning and requires embedding search infrastructure. A single large context request maintains full coherence and is simpler to implement, making it worthwhile when you need under 100 requests daily or require deep multi-document analysis.

Do context window prices differ between input and output tokens?

Yes, output tokens cost 3-5x more than input tokens across all major providers. For example, Claude 3.5 Sonnet charges $3 per million input tokens but $15 per million output tokens. This means reading a large document is cheap, but generating lengthy summaries or responses becomes the primary cost driver.

What's the cheapest way to process large documents with AI models?

Use Gemini 1.5 Flash or Claude 3.5 Haiku for initial document processing and filtering, which cost under $0.10 per million input tokens. Reserve expensive models like GPT-4 or Claude 3.5 Opus only for the final reasoning step on pre-filtered content, reducing total processing costs by 80-90%.

ANALYSIS · VERIFIED 30 JUL 2026

Which AI model offers the most context window per dollar?

Claude 3.5 Haiku delivers the most context window per dollar at approximately 10 million tokens per dollar for input, followed by Gemini 1.5 Flash at around 4 million tokens per dollar. However, the best value depends on whether you're primarily reading or writing tokens, since output costs typically run 3-5x higher than input across all models.

Context window size gets quoted like a spec sheet number — bigger is better, full stop. But a 2M-token window is only useful if feeding it 2M tokens of input doesn't blow your budget on a single call. This page answers a different question than the rest of this site: not "what's the cheapest model," but "what does it actually cost to use the context window you're being sold."

What it costs to fill each model's window once

Context window size × input price per token, for every model this site tracks. Lower is better — it means more room for less money.

Model	Context	Input price	Cost to fill window
GPT-4o mini	128K	$0.15/M	$0.019
Grok 4.1	128K	$0.20/M	$0.026
DeepSeek V3	128K	$0.27/M	$0.035
Gemini 2.5 Flash	1M	$0.15/M	$0.15
GPT-4o	128K	$2.50/M	$0.32
Gemini 3 Flash	1M	$0.50/M	$0.50
Claude Haiku 4.5	200K	$1.00/M	$0.20
Claude Sonnet 5	1M	$2.00/M	$2.00
GPT-5.5	400K	$5.00/M	$2.00
Gemini 3.1 Pro	2M	$2.00/M	$4.00
Claude Opus 4.8	1M	$5.00/M	$5.00
Claude Fable 5	1M	$10.00/M	$10.00

The model this actually flatters

Gemini 2.5 Flash is the standout: a 1M-token window — 8x GPT-4o mini's — for a fill cost of $0.15, still cheaper than fully using GPT-4o's much smaller 128K window ($0.32). If your workload genuinely benefits from stuffing in large documents or many retrieved chunks, Gemini 2.5 Flash gives you room to do that without the cost scaling into a different tier entirely.

The model this doesn't flatter

Gemini 3.1 Pro has the largest window this site tracks — 2M tokens — but at $2.00/M input, using all of it costs $4.00 per call. That's not necessarily wrong for a workload that needs the accuracy and headroom, but it's worth knowing the number before assuming "biggest window" means "most affordable way to work with a lot of context." Claude Opus 4.8 and Claude Fable 5 show the same pattern at the very top of the price range — large windows priced for calls where context depth matters more than cost per call.

How we'd actually decide

Need real headroom for large documents or many retrieved chunks, cost still matters: Gemini 2.5 Flash — the best ratio on this list by a wide margin.
Context needs are modest (single documents, short conversations): the fill-cost number barely matters — pick on price per token instead.
Need the largest window available regardless of cost: Gemini 3.1 Pro at 2M, with the understanding that using all of it is a real line item, not a rounding error.

"Cost to fill window" = context window size × input price per token, a one-time input cost with no output tokens included. Prices verified 30 July 2026. Use the calculator for your own exact mix of input and output.