Google

Google

Gemini 3.5 Flash Lite

Gemini 3.5 Flash Lite is a cost-optimized, high-throughput model built for processing massive context windows. Reach for it when you need fast, inexpensive text generation from multimodal inputs at scale.

Modalities

Text+Image → Text

Below release

Not established

Weights

Not published

Where it sits in the catalogue Kyma measures

Every number against every text model Kyma prices per token — a stated rule, not a chosen line-up.

Price · input#22/69

$0.405

output $3.375 · cached $0.0405

median $1.009 · best $0.0494

Price · output#32/69

$3.375

per 1M tokens generated

median $3.528 · best $0.2164

Availability · 30d#16/67

97.89%

1,377 observations since 2026-07-29

median 93.5% · best 100.0%

Throughput#24/67

53.4 tok/s

probe median, one fixed prompt

median 39.3 tok/s · best 321.8 tok/s

Response time#8/67

1.12 s

probe median, to a complete answer

median 2.54 s · best 0.86 s

Context

1.05M

max output 8,192

published by Google, not measured here

Tick above each rail is this model, below it the other 68. Dashed rule is the field median, solid is its best. Better is left; the axis stops at the 90th percentile, so a few models sit past its right edge.

Usage

How much this model is actually called here.

Rank

#22

of 98 active models

Tokens served

12.0M

all-time

Platform share

0.4%

of all tokens

Tokens · last 15 daysAug 31Sep 14

How it behaves under real clients

The same model answering different prompt shapes — measured, not benchmarked.

ClientRequestsTokensThroughputTo first tokenp95 totalCompleted
Python App13211.4M216 tok/s2.45 s26 s99.2%

Grouped by the client that sent the request. Not a benchmark: the same model on different prompt shapes. Throughput and time-to-first-token are medians; p95 is the slowest response in twenty. 1 client under 100 requests not shown.

Two clocks, and why they disagree

Kyma measures this model twice. Both are real; they answer different questions.

Probe · every 6h · 30 days

1.12s to answer

One fixed prompt, on a schedule, to every model. Comparable, because the model is the only thing that changes.

Observations
1,377
Answered by a substitute
14

Real traffic · last 7 days

2.25s to answer

Your requests, at the lengths clients actually send. Not comparable between models, but it is what running this one feels like.

Requests
48
Completed
93.8%
p95
5.2 s

Caching, as realised · last 7 days

0.0% of input cached

The share of input that actually hit cache, so the effective rate below is what was charged, not a best case.

Cached input
0
Fresh input
20.3K
List input
$0.405 /1M
Effective input
$0.405 /1M

The gap is prompt length, not the model degrading. Use the probe figure to choose between models, the traffic figure to budget for your own.

Pricing

Pay per token. Cached input bills at this model’s own cached rate, listed below.

$0.405 /1M input$3.375 /1M output
Hobby10 req/day · 2K in / 500 out
~$0.75/mo
Production1,000 req/day · 2K in / 500 out
~$74.92/mo
Scale20,000 req/day · 2K in / 500 out
~$1,499/mo
+ Estimate your workload
1,000
2,000
500
30%

Estimated monthly cost

$68.36

$2.28 / day on Gemini 3.5 Flash Lite

Same workload on:

Gemini 3.8 Flash$137+100%
Gemini 3.1 Pro$405+492%

Estimates use list pricing with cached input at this model's own cached rate. Actual bills depend on real token counts, and every response includes its exact cost.

When to use Gemini 3.5 Flash Lite

Updated 2026-07-29

Where this model earns its cost — and where it doesn't.

This model is Google's entry-level tier for 1M-token context windows. It accepts text, images, audio, and video as input, but generates text-only output. The architecture prioritizes throughput and low latency, making it suitable for batch processing and high-volume workloads.

On Kyma, the model runs through an OpenAI-compatible endpoint with automatic request failover. It supports tool calling and prompt caching, which bills repeated prefixes at this model's cached input rate. Every response includes exact usage costs in the usage.cost field and identifies the routed model via the X-Kyma-Model header.

The model caps output at 8,192 tokens and does not support native structured outputs or explicit reasoning traces. Thinking tokens are included in the output pricing, so monitor token counts if your prompts trigger internal processing.

High Volume Document Analysis

Process long reports or transcripts to extract key data points without hitting context limits.

Automated Content Categorization

Route large batches of user messages or logs into predefined labels using tool integration.

Audio And Video Summarization

Condense multimedia recordings and image sets into concise text summaries.

Fast Interactive Chat Routing

Handle lightweight conversational turns where response speed matters more than complex reasoning.

Not ideal for: Do not use this model for tasks requiring explicit reasoning chains, strict JSON schema enforcement, or outputs longer than 8,192 tokens.

How it compares

Against the peers people actually weigh it against.

SpecGemini 3.5 Flash LiteGemini 3.8 FlashGemini 3.1 Pro
Input /1M$0.405$1.013$2.70
Output /1M$3.375$5.063$16.20
Context1M1M1M
ToolsYesYesYes
ReasoningYesYesYes
Throughput53.4 tok/s35 tok/s95.3 tok/s

Quick start

Up and running in under two minutes.

  1. 1

    Create an API key

    Sign up and grab a key from the dashboard. Gemini 3.5 Flash Lite needs a top-up — the signup credit covers the free tier.

    Get API key →
  2. 2

    Make your first request

    Drop in your key and send a chat completion — fully OpenAI-compatible.

    curl https://kymaapi.com/v1/chat/completions \
      -H "Authorization: Bearer YOUR_API_KEY" \
      -H "Content-Type: application/json" \
      -d '{
        "model": "gemini-3.5-flash-lite",
        "messages": [
          {"role": "user", "content": "Explain prompt caching in one paragraph."}
        ]
      }'
  3. 3

    Stream responses

    Add "stream": true to receive tokens as they arrive.

    curl https://kymaapi.com/v1/chat/completions \
      -H "Authorization: Bearer YOUR_API_KEY" \
      -H "Content-Type: application/json" \
      -d '{
        "model": "gemini-3.5-flash-lite",
        "stream": true,
        "messages": [{"role": "user", "content": "Hello!"}]
      }'

FAQ

Common questions about this model.

What is the context window of Gemini 3.5 Flash Lite?

Gemini 3.5 Flash Lite has a 1M-token context window — roughly 1542 pages of text in a single request.

How much does the Gemini 3.5 Flash Lite API cost?

$0.405 per 1M input tokens and $3.375 per 1M output tokens, with cached input at $0.0405/1M — 90% off the input rate on repeated prompt prefixes. No subscription; you pay only for what you use.

Does Gemini 3.5 Flash Lite support function calling?

Yes — Gemini 3.5 Flash Lite supports tool/function calling and structured outputs (JSON mode), so it works with agent frameworks out of the box.

Are the weights for Gemini 3.5 Flash Lite publicly available?

No. Google does not publish Gemini 3.5 Flash Lite's weights — it is reachable through an API rather than as a file you can download and run (https://deepmind.google/models/model-cards/gemini-3-5-flash-lite/, read 2026-08-14). That is Google's decision about their own product, not a limitation of Kyma.

Is Gemini 3.5 Flash Lite ever served below the precision its creator released it at?

Kyma cannot say, and for this model nobody can. Google does not publish Gemini 3.5 Flash Lite's weights, so there is no released checkpoint whose precision a route could be measured against. We publish "not established" rather than reading a low format as a downgrade: on several models in this catalogue a low format is exactly how the creator ships it.

How do I use Gemini 3.5 Flash Lite?

Kyma is OpenAI-compatible: point your SDK's base URL at https://kymaapi.com/v1, use your Kyma API key, and set the model to gemini-3.5-flash-lite. Signing up is free, but this model needs a top-up: the $0.50 signup credit covers the free tier only.

Does this model support structured JSON outputs natively?

No, it does not support structured outputs. You will need to parse the generated text manually or route the request to a model with native schema enforcement.

How does prompt caching affect my input costs?

Kyma bills cached prompt prefixes at this model's cached input rate, which is published beside the input rate rather than assumed.

Can I send audio and video files directly to the API?

Yes, the model accepts text, image, audio, and video inputs, though it only generates text responses.

Needs a top-up — the signup credit covers the free tier.Create account →

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