What does Gemini 3.5 Flash-Lite cost?

QuickSilver Pro charges $0.255 input and $2.125 output per 1M tokens, 15% below Google's $0.30/$2.50 list price.

Does it work with the OpenAI SDK?

Yes. Use https://api.quicksilverpro.io/v1 as the base URL and gemini-3.5-flash-lite as the model ID.

1M context
Multimodal
Responses API

Gemini 3.5 Flash-Lite on QuickSilver Pro

Name: Gemini 3.5 Flash-Lite on QuickSilver Pro
Brand: QuickSilver Pro
Price: 0.255 USD
Availability: InStock

gemini-3.5-flash-lite

GoogleLive availability—

Gemini 3.5 Flash-Lite is Google's current low-cost GA model for high-throughput workloads. QuickSilver Pro serves it with a 1M-token context window at $0.255 input / $2.125 output per million tokens, 15% below Google's $0.30/$2.50 list price.

1M context
Vision
Tools
Streaming
JSON Schema output

ByRaullen ChaiUpdated August 14, 2026

You pay · input / output per 1M

$0.255 / $2.125

15% lower

on both legs vs Google list price

Pricing comparison ($/1M tokens)

Bar length = price relative to the list price

Provider	Input	Output	vs QSP
QuickSilver Pro	$0.255	$2.125	lowest-cost
Google list price(google/gemini-3.5-flash-lite)	$0.30	$2.50	15% lower
OpenAI (GPT-4o mini)	$0.15	$0.60	254% more expensive

Google list source: ai.google.dev/gemini-api/docs/pricing

At a glance

Context: 1M tokens
Input / 1M: $0.255
Output / 1M: $2.125
Thinks by default: No
Latency · live: —
AA Intelligence Index: 28

High-volume Gemini workloads with GA stability and predictable cost.

When is Gemini 3.5 Flash-Lite the right call?

Curated from catalog capabilities and published prices, not a benchmark.

When to use

Use Gemini 3.5 Flash-Lite for classification, extraction, routing, summarization, simple chat, and agent subtasks where throughput and price matter most.

Long documents and codebases, up to 1M tokens of context
Agents that call tools, with strict JSON Schema output
Prompts that include images
Responses API clients as well as Chat Completions

When to use something else

For reasoning-heavy coding or multimodal analysis, use Gemini 3.6 Flash. For the hardest long-context reasoning tasks, evaluate Gemini 3.1 Pro Preview.

Compare alternatives in Models

Quickstart (curl)

Get an API key →

curl https://api.quicksilverpro.io/v1/chat/completions \
  -H "Authorization: Bearer $QSP_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-3.5-flash-lite",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.quicksilverpro.io/v1",
    api_key=os.environ["QSP_KEY"],
)
response = client.chat.completions.create(
    model="gemini-3.5-flash-lite",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.quicksilverpro.io/v1",
  apiKey: process.env.QSP_KEY,
});
const response = await client.chat.completions.create({
  model: "gemini-3.5-flash-lite",
  messages: [{ role: "user", content: "Hello!" }],
});
console.log(response.choices[0].message.content);

curl https://api.quicksilverpro.io/v1/responses \
  -H "Authorization: Bearer $QSP_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-3.5-flash-lite",
    "input": "Hello!"
  }'

python (OpenAI SDK)

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.quicksilverpro.io/v1",
    api_key=os.environ["QSP_KEY"],
)
response = client.chat.completions.create(
    model="gemini-3.5-flash-lite",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)

OpenAI-compatible. One-line migration via base_url.

Gateway behavior

Compatibility notes for this model.

prompt: Prompts up to 1M tokens.

/v1/chat/completions: Supported
/v1/responses: Supported
streaming: Supported
tools: Supported
image input: Supported
JSON Schema: Supported
reasoning: Not listed for this model

Where the price comes from

Catalog v68 · no separate long-context tier

Provider reference: $0.30 / $2.50ai.google.dev/gemini-api/docs/pricing
Catalog verified: 2026-07-31

usage.cost = prompt_tokens × $0.255/1M + completion_tokens × $2.125/1M

/pricing.json

FAQ

Common questions

Yes. Existing integrations can continue using the old ID during migration, but new workloads should use gemini-3.5-flash-lite.