Verified 2026-10-07 · sourced from Google

Gemini 3.5 Flash-Lite Token Calculator, Pricing & 100K/1M Cost

Check Google Gemini 3.5 Flash-Lite pricing, estimate 100K and 1M token cost, and size a real API budget before you send a single request. Standard pricing is $0.30 per million input tokens and $2.50 per million output tokens with a 1M token context window.

Quick answer: Gemini 3.5 Flash-Lite pricing per 1M tokens is $0.30 input and $2.50 output. Context window: 1,048,576 tokens · Cached input: $0.030 / 1M.

Best for searches like Gemini 3.5 Flash-Lite token calculator, Gemini 3.5 Flash-Lite pricing, Gemini 3.5 Flash-Lite 100K tokens cost, Gemini 3.5 Flash-Lite 1M token cost.

Reference rates: Developer API · paid tier

USD per million text tokens. Verified 2026-10-07 · Official source

Mode / prompt sizeInputCache readCache writeOutput
Standard$0.3$0.03Not estimated$2.5
Batch$0.15$0.02Not estimated$1.25

Cache storage: $1 per million tokens per hour, charged separately and excluded from the per-call estimate.

1,048,576 context tokens · max output 65,536. Local token counts are estimates; use the provider’s usage counts for an invoice estimate.

Text-only estimate. Local token counts are approximate; use provider-reported usage for billing. Tool, image, audio and video charges are excluded.

Gemini Developer API paid-tier text rates (not Vertex AI). Output includes thinking tokens. Cache storage is billed separately by token-hours; grounding and tools cost extra.

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Some visitors want a fast Gemini 3.5 Flash-Lite API cost estimate, others want a direct 100K or 1M token budget, and some are already comparing alternatives. These shortcuts remove the extra click.

Context window

1,048,576 tokens

Input price

$0.30 / 1M

Output price

$2.50 / 1M

Cached input

$0.030 / 1M

Pricing modes and thresholds

Standard
$0.3000 input · $0.0300 cached · $2.5000 output
Batch
$0.1500 input · $0.0200 cached · $1.2500 output

Usage scenarios

Compare standard and cached pricing (where available) across common workloads.

ScenarioTokens inTokens outTotal tokensStandard costCached cost
Quick chat reply
Single user question with a short assistant answer
650220870$0.0007$0.0006
Coding assistant session
Multi-turn pair programming exchange (≈6 turns)
2,6001,4004,000$0.0043$0.0036
Knowledge base response
Retrieval-augmented answer referencing multiple passages
12,0003,00015,000$0.0111$0.0079
Near-max context run
Large document processing approaching the 1M token limit
983,04065,5361,048,576$0.459$0.193

Daily & monthly budgeting

Translate usage into predictable operating expenses across popular deployment sizes.

ProfileRequests/dayTokens/dayDaily costMonthly costCached dailyCached monthly
Team pilot2575,000$0.0775$2.33$0.0640$1.92
Product launch100500,000$0.480$14.40$0.386$11.56
Enterprise scale5003,000,000$3.10$93.00$2.56$76.80

Pricing notes

  • Text-only estimate. Local token counts are approximate; use provider-reported usage for billing. Tool, image, audio and video charges are excluded.
  • Gemini Developer API paid-tier text rates (not Vertex AI). Output includes thinking tokens. Cache storage is billed separately by token-hours; grounding and tools cost extra.

Frequently asked questions

How much does Gemini 3.5 Flash-Lite cost per 1,000 tokens?

At the published rates of $0.30 per million input tokens and $2.50 per million output tokens, a typical 1,000 token request (≈70% input, 30% output) costs about $0.0010.

Does Gemini 3.5 Flash-Lite offer cached input discounts?

Gemini 3.5 Flash-Lite drops input costs to $0.030 per million cached tokens. Using cached contexts, that same 1,000 token call totals $0.0008, a significant saving for chatbots and RAG systems.

What is the context window for Gemini 3.5 Flash-Lite?

Gemini 3.5 Flash-Lite supports up to 1,048,576 tokens (1M), allowing large prompts and retrieval-augmented payloads in a single call.

How fresh is the Gemini 3.5 Flash-Lite pricing data?

Pricing is sourced from https://ai.google.dev/gemini-api/docs/pricing and was last verified on 2026-10-07. The calculator updates automatically when models.json is refreshed.

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