Source-tracked model comparison

GPT-5.4 Nano vs GPT-4o Mini: which has lower API cost for classification?

Compare verified pricing and model limits for replacing GPT-4o Mini with a lower-cost OpenAI routing tier. The cost example uses a classification and routing workload and does not assume equal model quality.

Direct cost answer: GPT-4o mini is estimated at $76.50 per month and gpt-5.4-nano at $120.00 for 700 input tokens, 80 output tokens, and 500,000 monthly requests. GPT-4o mini is $43.50 lower under these assumptions. This does not identify a quality winner.
Generation changeClassification and routingSources checked 2026-07-13 / 2026-07-13
Tracked facts

Pricing and model limits

Prices are USD per 1M tokens under each model's verified default profile.

FieldGPT-4o minigpt-5.4-nano
ProviderOpenAIOpenAI
API model IDgpt-4o-minigpt-5.4-nano
Input / 1M$0.15$0.20
Cached input / 1M$0.075$0.02
Output / 1M$0.60$1.25
Context window128,000 tokens400,000 tokens
Maximum output16,384 tokens128,000 tokens
Accepted inputtext, imagetext, image
Example workload

Classification and routing cost scenario

700 input and 80 output tokens per request, 500,000 monthly requests, and 0% cached input.

OpenAI

GPT-4o mini

$76.50 / month
Input cost
$52.50
Output cost
$24.00
Per 1,000 calls
$0.153
Pricing profile
standard
View model details
OpenAI

gpt-5.4-nano

$120.00 / month
Input cost
$70.00
Output cost
$50.00
Per 1,000 calls
$0.24
Pricing profile
standard / short context
View model details

Cost result: GPT-4o mini is $43.50 lower per month for these assumptions. This is a price comparison, not a model-quality ranking.

StackLens assessment

Questions to answer before choosing

  • Does Nano meet the required classification threshold?
  • How many ambiguous cases still need GPT-4o Mini?
  • Will shorter outputs preserve the expected cost advantage?
Workload caveat

What this estimate leaves out

Quality and latency are not assumed equal across models; validate labels on a representative evaluation set.

Latency, reliability, output quality, retries, regional processing, and provider-specific tool charges can change the practical decision.