About
Gemini 3.1 Pro Preview is Google’s frontier reasoning model, delivering enhanced software engineering performance, improved agentic reliability, and more efficient token usage across complex workflows.
Pricing
| Input | $1.40 | per 1M prompt tokens |
|---|---|---|
| Output | $8.40 | per 1M generated tokens, reasoning included |
| Input over 200K | $2.80 | the whole prompt is billed at this rate once it passes the threshold |
| Output over 200K | $12.60 | applies to replies to those prompts |
| Web search | $0.020 | per request that uses the provider’s built-in search |
What a request costs
| Typical request | Each | Per 1,000 |
|---|---|---|
| Short chat reply1,500 in · 400 out | $0.0055 | $5.46 |
| Question over a long document40,000 in · 800 out | $0.063 | $62.72 |
| Agent tool step20,000 in · 1,200 out | $0.038 | $38.08 |
| Very long prompt (over 200K)250,000 in · 1,500 out | $0.719 | $718.90 |
At the developer-API rate, before any provider promotion or prompt-cache saving. In the app, the model picker shows what a message costs on your plan before you send it.
Benchmarks
Head-to-head rankings
- #4SlidesAgent arenaElo 122656% wins
- #8Godot gamesAgent arenaElo 123660% wins
- #11ASCII artModel arenaElo 127264% wins
- #11SVGModel arenaElo 128568% wins
- #22Game devAgent arenaElo 111444% wins
- #26UI componentsModel arenaElo 127969% wins
- #31Web appsAgent arenaElo 111945% wins
- #32Full stackAgent arenaElo 106841% wins
- #34Android appsAgent arenaElo 106441% wins
- #37Mobile appsAgent arenaElo 109143% wins
Scores from Artificial Analysis; bars are scaled to the best model on our list. Rankings from Design Arena head-to-head votes. Published by OpenRouter alongside its catalogue.
Call it from code
OpenAI-compatible: point any OpenAI SDK at https://api.privateer.pro/v1 and set
model. Keys are created in the app under Settings.
cURL
curl https://api.privateer.pro/v1/chat/completions \
-H "Authorization: Bearer $PRIVATEER_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "google/gemini-3.1-pro-preview:batch",
"messages": [{ "role": "user", "content": "Hello" }]
}'Python
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.privateer.pro/v1",
api_key=os.environ["PRIVATEER_API_KEY"],
)
reply = client.chat.completions.create(
model="google/gemini-3.1-pro-preview:batch",
messages=[{"role": "user", "content": "Hello"}],
)
print(reply.choices[0].message.content)JavaScript
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api.privateer.pro/v1",
apiKey: process.env.PRIVATEER_API_KEY,
});
const reply = await client.chat.completions.create({
model: "google/gemini-3.1-pro-preview:batch",
messages: [{ role: "user", content: "Hello" }],
});
console.log(reply.choices[0].message.content);Parameters this model accepts
include_reasoningmax_tokensreasoningreasoning_effortresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_p