GPT-4.1 token & cost calculator

OpenAI GPT-4.1 is the long-context member of OpenAI's frontier lineup — the model to reach for when your input is too big for GPT-5's 400k-token window. At 1,047,576 tokens of context, GPT-4.1 is one of three models in this calculator's roster (alongside Gemini 2.5 Pro and Flash) that can handle whole-codebase or whole-transcript workloads in a single call. The pricing — $2 input / $8 output per million — is higher than GPT-5 on input but lower on output, and it stays flat regardless of how much context you push through.

The right way to position GPT-4.1 in your stack: route by input size, not by overall complexity. For any request that fits comfortably under 200k tokens, GPT-5 (or Sonnet, or Pro) is usually the better quality-per-dollar pick. The moment you cross into the territory where retrieval would otherwise be the alternative — and where retrieval risks losing relevant context — GPT-4.1 starts winning, often dramatically.

Expected response (output tokens)

Prompt

Client-side. Never uploaded.

0 / 1,000,000 charactersContext window: 1,047,576 tokens

Or start with an example

Total estimated cost

<$0.01GPT-4.1

Tokensexact

Input cost

$0.00

Output cost (est.)

<$0.01

@ 1,024 response tokens

Context used

of 1,047,576

Verified 2026-05-09 · exact

Saved scenariosnone yet

Saved on this browser only — never uploaded. Up to 10 scenarios.

Tip: save a scenario when you have a prompt + model + response length you might revisit. Useful for sizing features before committing to a vendor.

Verify privacysince this page loaded — updates live

Prompt uploads0Always 0 — by design

Outgoing requests0Analytics + page assets only — no prompt content

Cookies on this origin0Vercel Analytics + Clarity may set first-party cookies

localStorage keys0Theme preference + saved scenarios live here

Server endpoints1/api/og only — accepts title + subtitle, never prompt text

Inspect

Open DevTools → Network. Type into the calculator. No request bodies should contain your prompt text.

Pricing

GPT-4.1 is flat-priced. The calculator above will warn you if your input would exceed the 1.047M-token context window, but at typical usage there's plenty of headroom.

Tier	Input $/M	Output $/M
All input	$2	$8
Context window	1,047,576 tokens

Verified against openai.com on 2026-05-09.

Worked examples

These three scenarios stay well under the context window. To stress-test long-context economics, paste a real long input above and watch the cost scale linearly — there's no tier surcharge here, unlike Gemini 2.5 Pro.

Scenario	Input	Output	Cost
Short chat turn A typical Q&A turn with a small system prompt.	800	400	<$0.01
System prompt + tool spec A larger context window with a tool schema, single response.	5,000	500	$0.014
Long document Q&A A long-form input (e.g. transcript) with a structured response.	50,000	1,500	$0.112

The pattern that pays off on GPT-4.1: prompt caching, aggressively. When a workload involves the same large document being referenced across many requests (agent loop, multi-turn conversation, batch rewrites), caching turns the input bill from a real number into a rounding error. The first request pays full freight; the next thousand pay roughly half. For long-context applications this is the difference between a feature that ships and a feature that gets cut for cost.

How is this counted?

GPT-4.1 uses the same o200k_base tokenizer as GPT-5 and GPT-5 Mini/Nano. We count via gpt-tokenizer (MIT) — the canonical OpenAI vocab, exact match, calibration factor 1.0. Inputs over 50,000 characters tokenize in a Web Worker.

FAQ

When should I pick GPT-4.1 over GPT-5?

When context length is the binding constraint. GPT-4.1 supports 1,047,576-token context — over 2× larger than GPT-5's 400k. For workflows that involve whole codebases, long transcripts, or document collections that don't fit in GPT-5's window, GPT-4.1 is the OpenAI answer. On shorter prompts where context isn't the issue, GPT-5 is meaningfully cheaper and often higher quality.

Is the token count exact?

Yes. GPT-4.1 uses OpenAI's canonical o200k_base tokenizer, which gpt-tokenizer ships verbatim. The count is exact — no approximation, no calibration factor.

How does GPT-4.1 compare to Gemini 2.5 Pro's long context?

Both support million-token windows, but the pricing geometry is different. Gemini 2.5 Pro charges a tier surcharge above 200k tokens (roughly doubles input + output rates); GPT-4.1 stays flat-priced regardless of input size. For sustained large-input workloads, GPT-4.1 is often cheaper despite a higher baseline rate.

What's the right way to think about input cost at this scale?

A single 800,000-token request on GPT-4.1 costs $1.60 just for input — meaningful at low traffic, fast to compound at scale. If your workload involves repeated full-context calls (e.g. agent re-reading the same large doc), prompt caching is essential. OpenAI's cached-input rate is roughly half the standard input rate.

Does my prompt leave the browser?

No. Tokenization runs entirely client-side, in a Web Worker for inputs over 50,000 characters. There is no server endpoint that ever receives prompt content.

Compare against every other model

To see this exact prompt scored against every supported model, sorted by total cost, paste it into the home calculator and toggle Compare across all models. The comparison is most informative on long-context prompts where Gemini 2.5 Pro's tier surcharge kicks in.

The most useful comparisons: GPT-4.1 Mini (the budget version with the same 1M context window), GPT-5 (when context length isn't the bottleneck), and Gemini 2.5 Pro (the cross-vendor long-context option with tiered pricing).