tokevenGemini · Mid-tier · 1,000K context window
Gemini 3.1 Pro pricing for math reasoning
$0.0390 per call at 1,500 input / 3,000 output tokens.
Per call
$0.0390
10K calls/mo
$390
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 math reasoning spend. Prices verified daily against provider pricing pages.
Configure your usage
Estimated cost
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: ~$0.0390 per call at standard rates
# Input: $2.00/MTok
# Output: $12.00/MTokHow Gemini 3.1 Pro compares on cost
Per-call cost for all models scoring 50+ on math reasoning benchmarks. Hover for details.
Cheapest viable option: Claude Sonnet 5 at $0.0330/call (15% less, 90/100 quality).
What drives math reasoning cost
Short problems, long solutions. Input is trivial and output is large because the derivation is the answer, so output rate is effectively the only price that matters. Models differ sharply in how much they show their working, which means two models at the same output rate can differ substantially in real cost per problem.
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 1,500/3,000 token split, output accounts for 92% of the $0.0390 per-call cost.
Complete Gemini 3.1 Pro pricing breakdown
Standard token rates
| Direction | $/MTok | Tokens per call |
|---|---|---|
| Input | $2.00 | 1,500 |
| Output | $12.00 | 3,000 |
Prompt caching: 90% off input tokens
Caching drops input cost from $2.00 to $0.20/MTok. For repeated system prompts, that saves $0.002700 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 volume | Cost per call | Standard monthly | Cached monthly | You save with caching |
|---|---|---|---|---|
| 10,000 calls | $0.0390 | $390 | $363 | $27 |
| 50,000 calls | $0.0390 | $1,950 | $1,815 | $135 |
| 100,000 calls | $0.0390 | $3,900 | $3,630 | $270 |
| 500,000 calls | $0.0390 | $19,500 | $18,150 | $1,350 |
| 1,000,000 calls | $0.0390 | $39,000 | $36,300 | $2,700 |
Gemini 3.1 Pro quality benchmarks for math reasoning
Use Gemini 3.1 Pro for math reasoning
Scores 90/100 (rank #4 of 20). Strong pick for production math reasoning workloads.
Top-ranked: Claude Fable 5 (98/100) at $0.1650/call
Performance across all task types
Gemini 3.1 Pro scores by task type. Math Reasoning highlighted.
| Task type | Confidence score | Rank out of 20 |
|---|---|---|
| Reasoning(current page) | 90/100 | #4 |
| Summarization | 87/100 | #4 |
| Q&A | 87/100 | #4 |
| Code Generation | 86/100 | #5 |
| Classification | 86/100 | #3 |
| Extraction | 85/100 | #4 |
| Code Review | 84/100 | #4 |
| Creative Writing | 84/100 | #4 |
Confidence scores are a third-party benchmark snapshot from week 29 of 2026. This snapshot is 8 weeks old, so treat the scores as directional and validate against your own evals.
Alternative models for math reasoning
8 models score 80+ on math reasoning benchmarks, sorted by input rate. Cheapest: Claude Sonnet 5 (15% less, 90/100 quality).
| Model | Provider | Input $/MTok | Output $/MTok | Quality | Per call | vs Gemini |
|---|---|---|---|---|---|---|
| Claude Sonnet 5 | Claude | $2.00 | $10.00 | 90/100 | $0.0330 | 15% cheaper |
| GPT 5.6 Terra | GPT | $2.00 | $12.00 | 82/100 | $0.0390 | same cost |
| GPT 5.4 | GPT | $2.50 | $15.00 | 80/100 | $0.0488 | 25% more |
| Claude Sonnet 4.6 | Claude | $3.00 | $15.00 | 88/100 | $0.0495 | 27% more |
| GPT 5.6 Sol | GPT | $4.00 | $20.00 | 96/100 | $0.0660 | 69% more |
| Claude Opus 4.8 | Claude | $5.00 | $25.00 | 97/100 | $0.0825 | 112% more |
| GPT 5.5 | GPT | $5.00 | $30.00 | 95/100 | $0.0975 | 150% more |
| Claude Fable 5 | Claude | $10.00 | $50.00 | 98/100 | $0.1650 | 323% more |
About Gemini 3.1 Pro
Provider
Gemini
Tier
Mid-tier
Context
1,000K tokens
Released
Apr 2026
Other models from Gemini
| Model | Tier | Input $/MTok | Output $/MTok | Context |
|---|---|---|---|---|
| Gemini 3.5 Flash | Fast / Budget | $1.50 | $9.00 | 1,000K |
| Gemini 3 Flash | Fast / Budget | $0.50 | $3.00 | 1,000K |
| Gemini 3.5 Flash-Lite | Fast / Budget | $0.30 | $2.50 | 1,000K |
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.
Gemini 3.1 Pro pricing for other use cases
See how Gemini 3.1 Pro costs change across different workload types.
Code Generation
$0.0520/call
Unit Test Generation
$0.0660/call
SQL Generation
$0.0150/call
API Generation
$0.0780/call
Code Refactoring
$0.0560/call
Code Review
$0.0340/call
PR Review
$0.0520/call
Security Review
$0.0420/call
Bug Detection
$0.0340/call
Document Summarization
$0.0320/call
PDF Summarization
$0.0480/call
Meeting Notes
$0.0400/call
Email Summarization
$0.0120/call
Chatbot
$0.0160/call
RAG Pipeline
$0.0300/call
Customer Support Bot
$0.0180/call
FAQ Bot
$0.0126/call
Data Extraction
$0.0200/call
JSON Extraction
$0.0156/call
Invoice Parsing
$0.0112/call
Resume Parsing
$0.0146/call
Chain of Thought
$0.0660/call
Legal Analysis
$0.0680/call
Financial Analysis
$0.0640/call
Content Moderation
$0.002200/call
Sentiment Analysis
$0.002200/call
Categorization
$0.003400/call
Intent Detection
$0.001800/call
Copywriting
$0.0250/call
Blog Writing
$0.0980/call
Marketing Copy
$0.0376/call
Product Descriptions
$0.0190/call
Related pages
Frequently asked questions
How much does Gemini 3.1 Pro cost for math reasoning per API call?
A typical math reasoning call using Gemini 3.1 Pro costs about $0.0390 per call. This estimate uses 1,500 input tokens and 3,000 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 math reasoning?
Gemini 3.1 Pro scores 90 out of 100 on math reasoning benchmarks overall. It ranks #4 across all 20 models in our cross-provider registry. The highest-ranked model for this task is Claude Fable 5 from Claude. That model scores 98/100 on this task type. Confidence scores are a third-party benchmark snapshot from week 29 of 2026. This snapshot is 8 weeks old, so treat the scores as directional and validate against your own evals.
What is the cheapest model for math reasoning with good quality?
The most affordable model for math reasoning above 80/100 quality is Claude Sonnet 5. It comes from Claude at $2.00 per million input tokens. Output costs $10.00 per million tokens with a quality score of 90/100. Switching from Gemini 3.1 Pro to Claude Sonnet 5 saves about 15% per API call.
How can I reduce my Gemini 3.1 Pro costs for math reasoning?
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 $0.0390 to about $0.001950 per call.
How does Gemini 3.1 Pro pricing compare to other Gemini models?
Gemini has 3 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). The right choice depends on whether your math reasoning workload prioritizes quality, cost, or speed.
What context window does Gemini 3.1 Pro support?
Gemini 3.1 Pro supports a context window of 1,000K tokens per API call. This sets the maximum combined length of your input prompt and generated response. For math reasoning, typical calls use 1,500 input and 3,000 output tokens. That is well within this limit, leaving room for longer prompts or multi-turn conversations.
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