GPT-4.1 Mini token & cost calculator

OpenAI GPT-4.1 Mini is the long-context budget tier — the model that lets you keep GPT-4.1's 1,047,576-token window without paying GPT-4.1's per-token rate. At $0.40 input / $1.60 output per million tokens, it sits at roughly 1/5 the cost of full GPT-4.1 and is positioned squarely against Gemini 2.5 Flash for long-context, cost-sensitive workloads.

The unlock here is affordable long context. Workloads that historically needed retrieval pipelines (chunk, embed, retrieve, reconstruct) become tractable as direct full-context calls when the per-million rate sits this low. You lose retrieval's compute overhead but gain whole-document fidelity — a tradeoff that's increasingly favorable as context-window pricing falls across the industry.

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 Mini

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

Flat-priced regardless of input size — no Gemini-style tier surcharge above 200k tokens. The calculator above warns you if your input would exceed the 1M-token context window.

Tier	Input $/M	Output $/M
All input	$0.4	$1.6
Context window	1,047,576 tokens

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

Worked examples

These scenarios cover typical long-context workloads. The "long document Q&A" scenario at 50k input tokens lands well under a cent — feasible for jobs that would have been cost-prohibitive a generation ago.

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	<$0.01
Long document Q&A A long-form input (e.g. transcript) with a structured response.	50,000	1,500	$0.022

A useful pattern: paginate when you can, full-context when you must. GPT-4.1 Mini's 1M window is genuinely usable, but a 200k-token prompt isn't 5× cheaper than a 1M-token prompt — the per-token rate is flat. If your task can be cleanly chunked, smaller prompts on a smarter model often beat one huge prompt on this one. The case for GPT-4.1 Mini is when the chunking introduces ambiguity or correctness regressions you can't validate against.

How is this counted?

GPT-4.1 Mini uses OpenAI's canonical o200k_base tokenizer. We count via gpt-tokenizer (MIT) — same exact vocab, calibration factor 1.0. Inputs over 50,000 characters tokenize in a Web Worker so the page stays responsive even on million-token prompts.

FAQ

When does GPT-4.1 Mini make sense?

When you need 1M-token context AND budget pressure. GPT-4.1 Mini gives you the same 1,047,576-token window as full GPT-4.1 at roughly 1/5th the per-token cost. It's the right pick for batch processing of large documents, retrieval-free Q&A on long inputs, and any workload where context length is non-negotiable but quality requirements are moderate.

How does it compare to GPT-5 Mini?

GPT-5 Mini is cheaper on input ($0.25/M vs $0.40/M) but caps at 400k context. GPT-4.1 Mini extends the window to 1M+ at the cost of slightly higher per-token rates. The deciding factor is almost always context length — pick GPT-5 Mini if your prompt fits, GPT-4.1 Mini otherwise.

Is the token count exact?

Yes. GPT-4.1 Mini shares the o200k_base tokenizer with the rest of the modern OpenAI lineup. We count via the canonical gpt-tokenizer package — exact, no approximation.

Should I use Batch API for this model?

Yes when feasible. OpenAI's Batch API discounts most synchronous models 50% for jobs that can wait up to 24 hours. On a long-document workload running through GPT-4.1 Mini in batch mode, the cost lands squarely in "model bill is no longer a real budget conversation" territory.

Does my prompt leave the browser?

No. Tokenization runs entirely in JavaScript on the page (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. For long-context prompts the comparison view is the fastest way to see whether GPT-4.1 Mini or Gemini 2.5 Flash wins on your specific input.

The most relevant comparisons: GPT-4.1 (when capability matters more than cost), GPT-5 Mini (when context fits in 400k), and Gemini 2.5 Flash (the cross-vendor long-context budget option).