Resources · AI Token Router
Context Window Economics: What a Bigger Context Window Actually Costs
A context window and a price are two independent numbers, and cross-provider comparisons publish them as separate columns for a reason [1]. Fourteen of the twenty-four models in this catalogue publish a stated context window -- video and image models don't, since the concept doesn't apply to a single generation call -- and seven of those twenty-four are callable through this API today. None of that changes what sending tokens costs: the input rate applies to every token in a request regardless of how much headroom the window has left, so a document that fits comfortably inside a much larger window is not cheaper for fitting comfortably -- it costs exactly what its token count costs.
Put your own numbers in before you take ours on trust.
See DeepSeek V4 Flash's rate cardContext window and price are different axes
Cross-provider pricing comparisons list context window, rate limits and price per token as three separate columns, not one derived from another [1] -- a model with a much larger window is not automatically cheaper or more expensive per token than one with a smaller window, and the reverse holds too.
The chart below plots every catalogued model that publishes a context window. Video and image models don't -- a single generation call doesn't have a 'context' to size the way a chat or agent turn does -- so it covers fourteen of the twenty-four models catalogued, seven of which are callable through this API today; the rest are priced at their intended rate with no configured upstream serving them yet.
What sending more tokens actually costs
The arithmetic is the same regardless of window size: multiply the tokens sent by the per-token input rate, add the tokens generated times the output rate. Below is that math worked by hand across three document sizes and three callable models -- Kimi K3, GLM-5.2 and DeepSeek V4 Flash -- each followed by a short, roughly 1,000-token response.
The 400,000-token row makes the second point. GLM-5.2's 200K window and DeepSeek V4 Flash's 160K window are both smaller than that document, so neither model can hold it at all -- a constraint no price discount changes. Kimi K3's window is the only one of the three that fits it, and on Kimi K3 it costs proportionally more than the smaller documents, not less for the window having room to spare.
| Document size | Kimi K3 | GLM-5.2 | DeepSeek V4 Flash |
|---|---|---|---|
| ~50,000 tokens in, ~1,000 out (a long report) | $0.102 | $0.044 | $0.005 |
| ~120,000 tokens in, ~1,000 out (a large transcript) | $0.231 | $0.101 | $0.011 |
| ~400,000 tokens in, ~1,000 out (near the top of the catalogue's range) | $0.749 | Exceeds context window | Exceeds context window |
Sources
- [1] LLM API Providers (2026): 12 APIs Compared by Price per 1M Tokens, Rate Limits, and Context — Morph. Cross-provider comparison publishing context window, rate limits and price as independent columns.
Questions this raises
- Does a bigger context window mean a lower rate?
- No -- window size and price are independent in this catalogue, same as the wider market [1]. A wider window buys the ability to send more tokens in one request, not a discount on the tokens you do send.
- Why do only 14 of the 24 catalogued models show a context window here?
- Context window is a property of text and embedding models. Video and image models generate from a single call with no equivalent concept, so they're excluded from this specific comparison rather than scored as zero.
- Does 'exceeds context window' in the table mean the request is rejected?
- It means the document as described wouldn't fit in that model's window at all, so the request would need to be chunked or a larger-window model chosen -- it isn't a pricing limit, it's a hard architectural one.
AI Token Router is an OpenAI-compatible gateway for open-weight models, priced below each publisher’s own rate on every row.
Related
- What a Fixed Monthly AI Budget Actually Buys in 2026
Three realistic budget tiers, worked by hand against this catalogue's own rate table, at one stated request shape -- how many requests and tokens $10, $50 and $250 a month actually buys on three callable models.
- When a Closed Frontier Model Is Still the Right Call
Closed frontier models measurably lead reasoning-heavy benchmarks as of September 2026. Where that lead and a simpler operational model are worth the higher price -- and why our catalogue is not the answer for that reader.
- Self-Hosting vs a Managed Open-Weight API: When Each Wins
Where the self-host breakeven actually sits, what self-hosting really costs once engineering time is priced in, and the honest cases where self-hosting wins -- this is not a blanket argument for a managed API.