Gemini 3.1 Flash-Lite
$152.50 / month- Input cost
- $77.50
- Output cost
- $75.00
- Per 1,000 calls
- $1.525
- Pricing profile
- standard
Source-tracked model comparison
GPT-5.4 Mini vs Gemini 3.1 Flash-Lite: which costs less for structured extraction?
Compare verified pricing and model limits for compact current-generation models for structured extraction. The cost example uses a structured data extraction workload and does not assume equal model quality.
Direct cost answer: Gemini 3.1 Flash-Lite is estimated at $152.50 per month and gpt-5.4-mini at $457.50 for 4,000 input tokens, 500 output tokens, and 100,000 monthly requests. Gemini 3.1 Flash-Lite is $305.00 lower under these assumptions. This does not identify a quality winner.
Tracked facts
Pricing and model limits
Prices are USD per 1M tokens under each model's verified default profile.
| Field | Gemini 3.1 Flash-Lite | gpt-5.4-mini |
|---|---|---|
| Provider | OpenAI | |
| API model ID | gemini-3.1-flash-lite | gpt-5.4-mini |
| Input / 1M | $0.25 | $0.75 |
| Cached input / 1M | $0.025 | $0.075 |
| Output / 1M | $1.50 | $4.50 |
| Context window | 1,048,576 tokens | 400,000 tokens |
| Maximum output | 65,536 tokens | 128,000 tokens |
| Accepted input | text, image, audio | text, image |
Example workload
Structured data extraction cost scenario
4,000 input and 500 output tokens per request, 100,000 monthly requests, and 25% cached input.
gpt-5.4-mini
$457.50 / month- Input cost
- $232.50
- Output cost
- $225.00
- Per 1,000 calls
- $4.575
- Pricing profile
- standard / short context
Cost result: Gemini 3.1 Flash-Lite is $305.00 lower per month for these assumptions. This is a price comparison, not a model-quality ranking.
Questions to answer before choosing
- Which model produces fewer schema repair calls?
- Do the required input formats fit both models?
- How does retry volume change the effective monthly cost?
What this estimate leaves out
Document parsing, OCR, validation infrastructure, and failed-record handling are outside this token estimate.
Latency, reliability, output quality, retries, regional processing, and provider-specific tool charges can change the practical decision.