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CostPerPrompt

Gemini 3.1 Flash Lite Preview API Pricing

Google · context window 1M · prices updated 2026-09-15

This is a dated/preview alias of Gemini 3.1 Flash Lite and currently carries identical pricing. The main page is kept up to date — this one exists so the exact model string resolves.

Input per 1M tokens
$0.25
Output per 1M tokens
$1.50
Cached input per 1M
$0.025
90% cheaper than fresh input

Where Gemini 3.1 Flash Lite Preview sits on price

At $1.50 per million output tokens, Gemini 3.1 Flash Lite Preview is a mid tier model — 1.4× cheaper than the median output price of $2.08, which puts it cheaper than 60% and pricier than 40% of the models we track. It sits 15th cheapest of the 38 Google models we track. Output costs 6.0× more than input here, so anything that makes the model write less — tighter instructions, structured output, lower max_tokens — moves the bill more than trimming the prompt.

Output tokens per $1
666,667
One full context fill
$0.2621
Cheaper than
60% of tracked models

Cached input is 90% cheaper. On an input-heavy workload you need roughly a 28% cache-hit rate to take 25% off the input line — reachable for chatbots and agents that resend the same system prompt and history.

What real workloads cost on Gemini 3.1 Flash Lite Preview

These three workloads are the ones teams actually run on a mid-priced model — a $1.50/1M model is not bought for the same job as one ten times the price.

Workload Per request Per month
Support chatbot — 500 conversations/day (5K in / 1.4K out) Conversation history re-sent each turn — the classic prompt-caching win. $0.0033 $50.25
Document pipeline — 10K docs/day (3K in / 500 out) Batch-friendly: no user waiting, so a ~50% batch discount usually applies. $0.0015 $450
Long-context analysis — 200 runs/day (250K in / 4K out) Only possible on large-context models; input cost is nearly the whole bill. $0.0685 $411

Model your exact traffic in the API cost calculator — it preloads Gemini 3.1 Flash Lite Preview with caching and batch options.

Gemini 3.1 Flash Lite Preview price history

Gemini 3.1 Flash Lite Preview's price has not moved since we began tracking it on Aug 2, 2026 — 44 days of stability in a market where 78 of the 334 models we track have repriced over the same period, including 6 of Google's own 38 models.

Change-points from our daily price snapshots (tracking since Aug 2, 2026; intraday moves between snapshots are not captured).

Gemini 3.1 Flash Lite Preview vs Mistral Large 3 2512

The closest-priced alternative from another vendor is Mistral Large 3 2512 (Mistral) — priced within a rounding error on output, with $0.25 more per million input tokens. When two models land this close on price, the decision is quality on your own workload, not the price sheet: run 50 real requests through both and compare.

See Mistral Large 3 2512 pricing →

Cheaper alternatives

More Google models

Frequently asked questions

How much does the Gemini 3.1 Flash Lite Preview API cost?

Gemini 3.1 Flash Lite Preview costs $0.25 per million input tokens and $1.50 per million output tokens, with cached input at $0.025 per million (90% cheaper). That works out to roughly 666,667 output tokens per dollar.

What does the support chatbot workload cost on Gemini 3.1 Flash Lite Preview?

Support chatbot — 500 conversations/day (5K in / 1.4K out) costs about $0.0033 per request and $50.25 per month on Gemini 3.1 Flash Lite Preview. Conversation history re-sent each turn — the classic prompt-caching win.

What does it cost to fill Gemini 3.1 Flash Lite Preview's 1M context window?

Sending 1M of input in a single request costs $0.2621 at $0.25 per million tokens — before any output. With prompt caching that same fill drops to about $0.0262 on repeat requests. This is why large context windows are cheap to advertise and expensive to actually use.

Is Gemini 3.1 Flash Lite Preview worth the price?

Gemini 3.1 Flash Lite Preview sits in the middle of the market (15 of 38 by price within Google). The honest test is a routing experiment: send the same 200 real requests to this model and to a tier below, and compare failure rate against the price gap — most teams find a majority of traffic never needed the pricier model.