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CostPerPrompt

GPT-4.1 Nano API Pricing

OpenAI · context window 1M · prices updated 2026-09-15

Input per 1M tokens
$0.1
Output per 1M tokens
$0.4
Cached input per 1M
$0.025
75% cheaper than fresh input

Where GPT-4.1 Nano sits on price

At $0.4 per million output tokens, GPT-4.1 Nano is a budget tier model — 5.2× cheaper than the median output price of $2.08, which puts it cheaper than 84% and pricier than 16% of the models we track. It sits 8th cheapest of the 92 OpenAI models we track. Output costs 4.0× input, the usual spread — trim both, starting with the answer length.

Output tokens per $1
2,500,000
One full context fill
$0.1048
Cheaper than
84% of tracked models

Cached input is 75% cheaper. On an input-heavy workload you need roughly a 33% cache-hit rate to take 25% off the input line — reachable for chatbots and agents that resend the same system prompt and history.

What real workloads cost on GPT-4.1 Nano

These three workloads are the ones teams actually run on a budget-priced model — a $0.4/1M model is not bought for the same job as one ten times the price.

Workload Per request Per month
High-volume classification — 200K requests/day (400 in / 20 out) Short output, huge volume: the input price dominates the bill. $0 $288
Small app — 1K requests/day (500 in / 150 out) The baseline most side projects actually run at. $0.0001 $3.30
Long-context analysis — 200 runs/day (250K in / 4K out) Only possible on large-context models; input cost is nearly the whole bill. $0.0266 $160

Model your exact traffic in the API cost calculator — it preloads GPT-4.1 Nano with caching and batch options.

GPT-4.1 Nano price history

GPT-4.1 Nano's price has not moved since we began tracking it on Aug 2, 2026 — 44 days of stability in a market where 78 of the 334 models we track have repriced over the same period, including 9 of OpenAI's own 92 models.

Change-points from our daily price snapshots (tracking since Aug 2, 2026; intraday moves between snapshots are not captured).

GPT-4.1 Nano vs Qwen2.5 72B Instruct

The closest-priced alternative from another vendor is Qwen2.5 72B Instruct (Alibaba (Qwen)) — priced within a rounding error on output, with $0.26 more per million input tokens. When two models land this close on price, the decision is quality on your own workload, not the price sheet: run 50 real requests through both and compare.

See Qwen2.5 72B Instruct pricing →

Cheaper alternatives

More OpenAI models

Frequently asked questions

How much does the GPT-4.1 Nano API cost?

GPT-4.1 Nano costs $0.1 per million input tokens and $0.4 per million output tokens, with cached input at $0.025 per million (75% cheaper). That works out to roughly 2,500,000 output tokens per dollar.

What does the high-volume classification workload cost on GPT-4.1 Nano?

High-volume classification — 200K requests/day (400 in / 20 out) costs about $0 per request and $288 per month on GPT-4.1 Nano. Short output, huge volume: the input price dominates the bill.

What does it cost to fill GPT-4.1 Nano's 1M context window?

Sending 1M of input in a single request costs $0.1048 at $0.1 per million tokens — before any output. With prompt caching that same fill drops to about $0.0262 on repeat requests. This is why large context windows are cheap to advertise and expensive to actually use.

Is GPT-4.1 Nano worth the price?

GPT-4.1 Nano is in the cheapest end of the market — cheaper than 84% of the models we track. At this price the question is not cost but capability: it is a good fit for classification, extraction, routing and bulk summarisation, and a poor fit for multi-step reasoning where a wrong answer costs more than the tokens saved.