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Gemini 3.5 Flash Lite

Up to 30%

Gemini 3.5 Flash Lite is available on LLM.API as an OpenAI-compatible chat endpoint—route Gemini Flash Lite quality through one key with transparent token pricing.

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What is Gemini 3.5 Flash Lite?

On LLM.API, Gemini 3.5 Flash Lite (gemini-3.5-flash-lite) serves as a Gemini Flash Lite conversational model for products that need 1M tokens context windows and predictable token pricing. Fast Gemini model balancing multimodal reasoning, tool use, and cost.

Providers

LLM.API routes Gemini 3.5 Flash Lite to the providers below, with discounted effective rates versus list price.

List price by provider ($ / 1M tokens)

InputOutput
google-ai-studio$0.3 in
$2.5 out
google-vertex$0.3 in
$2.5 out

Provider list prices; the LLM.API discount applies on top.

ProviderPricingContextCapabilities
google-ai-studio30% offin $300; out $2500 per 1M tokens1M tokensvision, tools, streaming, reasoning, web search, JSON, structured
google-vertex30% offin $300; out $2500 per 1M tokens1M tokensvision, tools, streaming, reasoning, web search, JSON, structured

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 21 Sept 2026.

Try this model

Test Gemini 3.5 Flash Lite right here — free to start.

Gemini 3.5 Flash Lite
Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call Gemini 3.5 Flash Lite through the OpenAI-compatible API — POST /v1/chat/completions.

python
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_LLMAP_KEY",
    base_url="https://api.llmapi.ai/v1",
)

resp = client.chat.completions.create(
    model="gemini-3.5-flash-lite",
    messages=[
        {"role": "system", "content": "You are a precise product assistant."},
        {"role": "user", "content": "Give me three crisp launch checklist items."},
    ],
)
print(resp.choices[0].message.content)
{
  "model": "gemini-3.5-flash-lite",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ]
}

5 Core Capabilities

  • Multilingual drafting

    Drafts and translates professional content across major business languages. Tuned to how teams typically call Gemini 3.5 Flash Lite.

  • Tool-ready dialogue

    Works well in agent loops that call functions, browsers, or retrieval APIs. Tuned to how teams typically call Gemini 3.5 Flash Lite.

  • Instruction following

    Follows detailed system and user instructions with strong adherence to format and tone. Grounded in the model's chat role rather than generic chat claims.

  • Safety-aware replies

    Supports product policies with refusals and cautious handling of sensitive topics. Reflects Gemini Flash Lite positioning for this endpoint.

  • Structured outputs

    Can produce JSON-friendly or schema-oriented responses when prompted carefully. Grounded in the model's chat role rather than generic chat claims.

6 Most Valuable Use Cases

  • Internal knowledge assistants grounded with your retrieval layer with Gemini 3.5 Flash Lite
  • Meeting-note cleanup and action-item generation
  • Sales and success email drafting with CRM context with Gemini 3.5 Flash Lite
  • Policy Q&A bots with careful refusal behavior
  • Multilingual localization drafts for UX copy with Gemini 3.5 Flash Lite
  • Customer support copilots that draft accurate, on-brand replies

Why Build on LLM.API?

One unified API. Every major model. Built-in reliability, cost control, and observability.

  • Intelligent AI Routing

    Automatically route each request to the best model across providers based on latency, cost, and quality—without changing your integration or redeploying code.

    One endpoint, every model.

  • Cost-Aware Execution

    Control spend with per-request cost estimation, smart model selection, and centralized quotas so teams can experiment fast without runaway bills or manual tracking.

    More performance, less spend.

  • Resilient Fallback Flows

    Define automatic, provider-agnostic fallbacks to keep your app up during outages, rate limits, or timeouts—no brittle failover logic scattered through your codebase.

    Never go dark on users.

  • Deep LLM Observability

    Trace every call across providers with logs, metrics, and request replay so you can debug, tune prompts, and optimize model choices from one unified dashboard.

    See every token, everywhere.

  • Task-Level Orchestration

    Describe tasks, not models. LLM.API maps them to the right tools, models, and prompts so you ship complex AI workflows with minimal glue code.

    Think tasks, not models.

  • High-Throughput Batch APIs

    Process millions of inferences efficiently with optimized batch pipelines, concurrency controls, and retry logic—all behind the same simple interface you use for single calls.

    Scale from 1 to millions.

Why run Gemini 3.5 Flash Lite on LLM.API?

  • Unified AI Routing

    Practical: Reach Gemini 3.5 Flash Lite and sibling models through one OpenAI-compatible endpoint.

  • Cost Control

    Compare provider price points and keep spend visible as you scale Gemini 3.5 Flash Lite.

  • Reliability Layer

    Practical: Retry and route across configured providers when a single upstream blips.

  • Observability

    Trace prompts, tokens, and errors for Gemini 3.5 Flash Lite alongside the rest of your stack.

  • Drop-in SDKs

    Production: Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.

  • Model Breadth

    Swap Gemini 3.5 Flash Lite for chat, media, or embedding alternatives without rewriting auth.

When to Use — When NOT to Use

Use it if...

  • You need provider failover options exposed for this model id (Gemini 3.5 Flash Lite)
  • You need a general-purpose text model for assistants, agents, or content workflows (Gemini 3.5 Flash Lite)
  • You want OpenAI-compatible chat completions through a single LLM.API key (Gemini 3.5 Flash Lite)
  • Your prompts benefit from the model's family strengths (reasoning, speed, or cost) (Gemini 3.5 Flash Lite)

Avoid if...

  • You need guaranteed real-time hard latency SLAs without benchmarking the provider
  • You need pure embedding, OCR, or media generation instead of chat
  • You require on-prem only deployment with no cloud inference
  • Your use case depends on unpublished proprietary benchmarks not listed here

BENCHMARKS

Gemini 3.5 Flash Lite benchmark scores

Intelligence index

This model23.
Tracked median12.

Scale: 0-100 index points

Output speed

This model348 t/s

Scale: 0-400 tokens per second

Reference price per 1M tokens

Input$0.30
Output$2.50

Bars compare input and output list prices for this model.

Artificial Analysis Intelligence Index23
Median index across all tracked models12
Output speed348 tokens/s
Reference input price$0.30 / 1M tokens
Reference output price$2.50 / 1M tokens

Independent scores published by Artificial Analysis (source). Reference prices are provider list prices, not LLM.API pricing.

RELIABILITY

Gemini 3.5 Flash Lite uptime, last 30 days

30-Day Uptime
100.00%
Past Incidents (30d)
0
Error rate (24h)
0.00%

Last 30 days

30/30 days operational | 100.00% uptime

Availability tracked for this model. Full history on the LLM Uptime Status page or the status hub. LLM.API routes around provider outages automatically.

COMMUNITY

What developers say about Gemini models

Summarised from publicly published developer and community reviews of this model family. Opinions are the sources’, not LLM.API’s, and may not be specific to Gemini 3.5 Flash Lite.

  • The Flash line is praised for speed and cost — reviewers regularly report code generated in seconds where slower models take minutes.
  • The main caveat raised is confident hallucination on under-specified tasks, so reviewers recommend verification steps or a stronger model for critical output.
  • Community reports highlight good long-context handling and strong multimodal input as the reasons teams keep Flash in the loop despite the accuracy caveats.

SOURCES

Frequently Asked Questions

  • Does Gemini 3.5 Flash Lite support streaming?

    Yes—at least one listed provider advertises streaming.

  • How is Gemini 3.5 Flash Lite priced on LLM.API?

    Listed pricing metadata shows: In $0.3 / 1M tokens · Out $2.5 / 1M tokens. LLM.API may offer discounted effective rates (illustrative ~30% callout vs list when available).. Confirm live rates in the LLM.API dashboard or docs before production budgeting.

  • What is the context length for Gemini 3.5 Flash Lite?

    Reported context for Gemini 3.5 Flash Lite is 1M tokens. Always verify the active provider row if multiple providers are listed.

  • When should I choose Gemini 3.5 Flash Lite?

    You need a general-purpose text model for assistants, agents, or content workflows — especially when you specifically need Gemini 3.5 Flash Lite.

  • What are limitations of Gemini 3.5 Flash Lite?

    Like other API models, Gemini 3.5 Flash Lite can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.

  • Which providers serve Gemini 3.5 Flash Lite?

    LLM.API currently lists: google-ai-studio, google-vertex. Availability can vary by region and account.

  • Can I use tools or structured outputs with Gemini 3.5 Flash Lite?

    Tool and/or structured-output flags appear on one or more providers for this model—confirm in the providers table.

  • What modalities does Gemini 3.5 Flash Lite support?

    Gemini 3.5 Flash Lite accepts text, image, video, audio and produces text according to its architecture metadata on LLM.API.

  • Where is the canonical page for Gemini 3.5 Flash Lite?

    https://llmapi.ai/models/google-gemini-3-5-flash-lite/

  • How do I call Gemini 3.5 Flash Lite via API?

    Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "gemini-3.5-flash-lite" and your LLM.API key. See the code snippet on this page.

  • Is Gemini 3.5 Flash Lite a chat model?

    Yes—Gemini 3.5 Flash Lite is exposed as a chat/completions-style endpoint on LLM.API.

  • What is Gemini 3.5 Flash Lite?

    Fast Gemini model balancing multimodal reasoning, tool use, and cost. On LLM.API it is addressed as `gemini-3.5-flash-lite`.

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