Gemini · Mid-tier · 1.0M context window

Gemini 3.1 Pro pricing for product descriptions

$0.0190 per call at 500 input / 1,500 output tokens.

Per call

$0.0190

10K calls/mo

$190

Cache savings

90% off input

Batch savings

50% off all

Calculate your Gemini 3.1 Pro costs

Adjust token counts, volume, and discount toggles to model your product descriptions spend. Prices verified daily against provider pricing pages.

Configure your usage

500
100100K
1,500
5050K
10,000
1001M

Estimated cost

Monthly cost
$190
10,000 calls/month
Cost per call$0.019
Daily cost$6
Input rate$2.00/MTok
Output rate$12.00/MTok
Analyze your real spend

Quick start

Call Gemini 3.1 Pro with the Google Generative AI SDK:

import google.generativeai as genai

model = genai.GenerativeModel("gemini-3.1-pro")
response = model.generate_content("Your prompt here")
# Cost: ~$0.0190 per call at standard rates
# Input:  $2.00/MTok
# Output: $12.00/MTok

What drives product descriptions cost

Catalogue-scale generation: modest output per item, multiplied across thousands of SKUs. Per-call price is small and total spend is set by catalogue size, which makes this a batch API workload almost by definition: descriptions are rarely needed within seconds. Shared brand and formatting context belongs in the cached prefix.

For Gemini 3.1 Pro specifically, that means output tokens cost 6.0x more than input, at $12.00 vs $2.00 per MTok. At this use case’s 500/1,500 token split, output accounts for 95% of the $0.0190 per-call cost.

Complete Gemini 3.1 Pro pricing breakdown

Standard token rates

Direction$/MTokTokens per call
Input$2.00500
Output$12.001,500

Prompt caching: 90% off input tokens

Caching drops input cost from $2.00 to $0.20/MTok. For repeated system prompts, that saves $0.000900 per call.

Batch API: 50% off all tokens

Submit requests asynchronously and receive 50% off all token costs. This works best for offline pipelines where latency requirements are flexible.

Monthly cost projections

Monthly volumeCost per callStandard monthlyCached monthlyYou save with caching
10,000 calls$0.0190$190$181$9
50,000 calls$0.0190$950$905$45
100,000 calls$0.0190$1,900$1,810$90
500,000 calls$0.0190$9,500$9,050$450
1,000,000 calls$0.0190$19,000$18,100$900

Gemini 3.1 Pro quality benchmarks for product descriptions

Gemini 3.1 Pro is a solid pick for product descriptions

Scores 77/100 (rank #7 of 88). Good balance of quality and cost for product descriptions.

Top-ranked: Gemini 3.8 Flash (83/100) at $0.006000/call

Performance across all task types

Gemini 3.1 Pro scores by task type. Product Descriptions highlighted.

Q&A87Classification86Code Review84Reasoning84Summarization80Creative Writing *77Extraction75Code Generation57
Task typeConfidence scoreRank out of 88
Q&A87/100#4
Classification86/100#3
Code Review84/100#4
Reasoning84/100#22
Summarization80/100#2
Creative Writing(current page)77/100#7
Extraction75/100#5
Code Generation57/100#24

Confidence scores are a third-party benchmark snapshot from week 26 of 2026. This snapshot is 17 weeks old, so treat the scores as directional and validate against your own evals.

Alternative models for product descriptions

10 models score 80+ on product descriptions benchmarks, sorted by input rate. Cheapest: Gemini 3.6 Flash (68% less, 76/100 quality).

ModelProviderInput $/MTokOutput $/MTokQualityPer callvs Gemini
Gemini 3.6 FlashGemini$0.75$3.7576/100$0.00600068% cheaper
Gemini 3.7 FlashGemini$0.75$3.7582/100$0.00600068% cheaper
Gemini 3.8 FlashGemini$0.75$3.7583/100$0.00600068% cheaper
Gemini 3.5 FlashGemini$1.50$9.0075/100$0.014325% cheaper
Grok 4.6Grok$2.00$6.0078/100$0.010047% cheaper
Grok 4.7Grok$2.00$6.0075/100$0.010047% cheaper
GPT 5.4GPT$2.50$15.0079/100$0.023825% more
GPT 5.6 SolGPT$4.00$20.0076/100$0.032068% more
Claude Opus 4.8Claude$5.00$25.0081/100$0.0400111% more
Claude Fable 5.1Claude$10.00$50.0078/100$0.0800321% more

About Gemini 3.1 Pro

Provider

Gemini

Tier

Mid-tier

Context

1.0M tokens

Released

Apr 2026

Other models from Gemini

ModelTierInput $/MTokOutput $/MTokContext
Gemini 3.5 FlashFast / Budget$1.50$9.001.0M
Gemini 3 FlashFast / Budget$0.50$3.001.0M
Gemini 3.5 Flash-LiteFast / Budget$0.30$2.501.0M
Gemini 2.5 Computer UseFast / Budget$1.25$10.00128K
Gemini 2.5 FlashFast / Budget$0.30$2.501.0M
Gemini 2.5 Flash-LiteFast / Budget$0.10$0.401.0M
Gemini 2.5 ProMid-tier$1.25$10.001.0M
Gemini 3.1 Flash-LiteFast / Budget$0.25$1.501.0M
Gemini 3.1 Pro Custom ToolsMid-tier$2.00$12.001.0M
Gemini 3.6 FlashFast / Budget$0.75$3.751.0M
Gemini 3.7 FlashFast / Budget$0.75$3.751.0M
Gemini 3.8 FlashFast / Budget$0.75$3.751.0M
Gemini Omni 1.1 FlashFast / Budget$1.50$9.001.0M
Gemini Omni FlashFast / Budget$1.50$9.001.0M
Gemini Robotics ER 2Fast / Budget$1.00$5.00131K

You can compare Gemini 3.1 Pro head-to-head against any other model on our model comparison pages, or track how pricing has changed over time on the pricing history tracker.

Frequently asked questions

How much does Gemini 3.1 Pro cost for product descriptions per API call?

A typical product descriptions call using Gemini 3.1 Pro costs about $0.0190 per call. This estimate uses 500 input tokens and 1,500 output tokens at standard rates. Standard rates are $2.00 per million input tokens and $12.00 per million output tokens.

Is Gemini 3.1 Pro the best model for product descriptions?

Gemini 3.1 Pro scores 77 out of 100 on product descriptions benchmarks overall. It ranks #7 across all 88 models in our cross-provider registry. The highest-ranked model for this task is Gemini 3.8 Flash from Gemini. That model scores 83/100 on this task type. Confidence scores are a third-party benchmark snapshot from week 26 of 2026. This snapshot is 17 weeks old, so treat the scores as directional and validate against your own evals.

What is the cheapest model for product descriptions with good quality?

The most affordable model for product descriptions above 80/100 quality is Gemini 3.6 Flash. It comes from Gemini at $0.75 per million input tokens. Output costs $3.75 per million tokens with a quality score of 76/100. Switching from Gemini 3.1 Pro to Gemini 3.6 Flash saves about 68% per API call.

How can I reduce my Gemini 3.1 Pro costs for product descriptions?

Three strategies lower Gemini 3.1 Pro costs for this task type effectively. Prompt caching gives a 90% discount on input tokens when reusing system prompts. The batch API gives 50% off all tokens for asynchronous processing workloads. Prompt optimization reduces token counts through more concise instructions and context windows. Combining caching with batching brings your cost from $0.0190 to about $0.000950 per call.

How does Gemini 3.1 Pro pricing compare to other Gemini models?

Gemini has 15 other models alongside Gemini 3.1 Pro today: Gemini 3.5 Flash at $1.50/$9.00 per million tokens (fast / budget), Gemini 3 Flash at $0.50/$3.00 per million tokens (fast / budget), Gemini 3.5 Flash-Lite at $0.30/$2.50 per million tokens (fast / budget), Gemini 2.5 Computer Use at $1.25/$10.00 per million tokens (fast / budget), Gemini 2.5 Flash at $0.30/$2.50 per million tokens (fast / budget), Gemini 2.5 Flash-Lite at $0.10/$0.40 per million tokens (fast / budget), Gemini 2.5 Pro at $1.25/$10.00 per million tokens (mid-tier), Gemini 3.1 Flash-Lite at $0.25/$1.50 per million tokens (fast / budget), Gemini 3.1 Pro Custom Tools at $2.00/$12.00 per million tokens (mid-tier), Gemini 3.6 Flash at $0.75/$3.75 per million tokens (fast / budget), Gemini 3.7 Flash at $0.75/$3.75 per million tokens (fast / budget), Gemini 3.8 Flash at $0.75/$3.75 per million tokens (fast / budget), Gemini Omni 1.1 Flash at $1.50/$9.00 per million tokens (fast / budget), Gemini Omni Flash at $1.50/$9.00 per million tokens (fast / budget), Gemini Robotics ER 2 at $1.00/$5.00 per million tokens (fast / budget). The right choice depends on whether your product descriptions workload prioritizes quality, cost, or speed.

What context window does Gemini 3.1 Pro support?

Gemini 3.1 Pro supports a context window of 1.0M tokens per API call. This sets the maximum combined length of your input prompt and generated response. For product descriptions, typical calls use 500 input and 1,500 output tokens. That is well within this limit, leaving room for longer prompts or multi-turn conversations.

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