Source-tracked model comparison

Gemini 3.5 Flash vs GPT-4o: how do developer-assistant API costs compare?

Compare verified pricing and model limits for developer-assistant API cost across Google and OpenAI. The cost example uses a developer assistant backend workload and does not assume equal model quality.

Direct cost answer: Gemini 3.5 Flash is estimated at $982.50 per month and GPT-4o at $1,312.50 for 5,000 input tokens, 1,500 output tokens, and 50,000 monthly requests. Gemini 3.5 Flash is $330.00 lower under these assumptions. This does not identify a quality winner.
Current provider alternativesDeveloper assistant backendSources 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 3.5 FlashGPT-4o
ProviderGoogleOpenAI
API model IDgemini-3.5-flashgpt-4o
Input / 1M$1.50$2.50
Cached input / 1M$0.15$1.25
Output / 1M$9.00$10.00
Context window1,048,576 tokens128,000 tokens
Maximum output65,536 tokens16,384 tokens
Accepted inputtexttext, image
Example workload

Developer assistant backend cost scenario

5,000 input and 1,500 output tokens per request, 50,000 monthly requests, and 20% cached input.

Google

Gemini 3.5 Flash

$982.50 / month
Input cost
$307.50
Output cost
$675.00
Per 1,000 calls
$19.65
Pricing profile
standard
View model details
OpenAI

GPT-4o

$1,312.50 / month
Input cost
$562.50
Output cost
$750.00
Per 1,000 calls
$26.25
Pricing profile
standard
View model details

Cost result: Gemini 3.5 Flash is $330.00 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 completes the target coding tasks reliably?
  • How many retries are needed for acceptable output?
  • Do existing SDK and cloud choices change switching cost?
Workload caveat

What this estimate leaves out

The calculation does not compare coding quality and excludes indexing, execution sandboxes, and repository storage.

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