Source-tracked model comparison

Gemini 3.1 Flash-Lite vs GPT-4o Mini: which has lower classification API cost?

Compare verified pricing and model limits for high-volume classification across compact Google and OpenAI models. The cost example uses a classification and routing workload and does not assume equal model quality.

Direct cost answer: Gemini 3.1 Flash-Lite is estimated at $147.50 per month and GPT-4o mini at $76.50 for 700 input tokens, 80 output tokens, and 500,000 monthly requests. GPT-4o mini is $71.00 lower under these assumptions. This does not identify a quality winner.
Current provider alternativesClassification and routingSources checked 2026-07-14 / 2026-07-13
Tracked facts

Pricing and model limits

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

FieldGemini 3.1 Flash-LiteGPT-4o mini
ProviderGoogleOpenAI
API model IDgemini-3.1-flash-litegpt-4o-mini
Input / 1M$0.25$0.15
Cached input / 1M$0.025$0.075
Output / 1M$1.50$0.60
Context window1,048,576 tokens128,000 tokens
Maximum output65,536 tokens16,384 tokens
Accepted inputtext, image, audiotext, image
Example workload

Classification and routing cost scenario

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

Google

Gemini 3.1 Flash-Lite

$147.50 / month
Input cost
$87.50
Output cost
$60.00
Per 1,000 calls
$0.295
Pricing profile
standard
View model details
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

Cost result: GPT-4o mini is $71.00 lower per month for these assumptions. This is a price comparison, not a model-quality ranking.

StackLens assessment

Questions to answer before choosing

  • Which model reaches the target classification accuracy?
  • How many cases require escalation?
  • Do provider controls or regions constrain the choice?
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.