Source-tracked model comparison

Gemini 3.1 Pro Preview vs GPT-4o: how do long-context research costs compare?

Compare verified pricing and model limits for long-context research cost across Gemini Pro and GPT-4o. The cost example uses a long-context research workload and does not assume equal model quality.

Direct cost answer: Gemini 3.1 Pro Preview is estimated at $666.00 per month and GPT-4o at $812.50 for 150,000 input tokens, 5,000 output tokens, and 2,000 monthly requests. Gemini 3.1 Pro Preview is $146.50 lower under these assumptions. This does not identify a quality winner.
Current provider alternativesLong-context researchSources 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 Pro PreviewGPT-4o
ProviderGoogleOpenAI
API model IDgemini-3.1-pro-previewgpt-4o
Input / 1M$2.00$2.50
Cached input / 1M$0.20$1.25
Output / 1M$12.00$10.00
Context window1,048,576 tokens128,000 tokens
Maximum output65,536 tokens16,384 tokens
Accepted inputtexttext, image
Example workload

Long-context research cost scenario

150,000 input and 5,000 output tokens per request, 2,000 monthly requests, and 10% cached input.

Google

Gemini 3.1 Pro Preview

$666.00 / month
Input cost
$546.00
Output cost
$120.00
Per 1,000 calls
$333.00
Pricing profile
standard
View model details
OpenAI

GPT-4o

$812.50 / month
Input cost
$712.50
Output cost
$100.00
Per 1,000 calls
$406.25
Pricing profile
standard
View model details

Cost result: Gemini 3.1 Pro Preview is $146.50 lower per month for these assumptions. This is a price comparison, not a model-quality ranking.

StackLens assessment

Questions to answer before choosing

  • Does the workload fit each tracked context window?
  • How are citations and unsupported claims evaluated?
  • Does preview status meet the deployment requirement?
Workload caveat

What this estimate leaves out

Models whose tracked context window is below the scenario input are excluded from the compatible-model table.

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