Bonus: Top up now and we'll double your first deposit — get x2 credits instantly.
Get Started
Sign Up
Sign Up

Gemini 3.1 Flash Lite API: Endpoint & Pricing

Gemini 3.1 Flash Lite is Google’s ultra-fast, low-cost Gemini 3-series language model optimized for high-volume, latency-sensitive applications.

Start Using API

What is Gemini 3.1 Flash Lite?

Gemini 3.1 Flash Lite is a lightweight, highly cost-efficient member of Google’s Gemini 3 family designed for rapid, large-scale text and multimodal inference. It is mainly used for chatbots, customer support, and other interactive applications where low latency and high request throughput are critical. It is also used in background automation, agentic workflows, and tools that need to run many inexpensive calls while occasionally invoking deeper reasoning via its “thinking levels” controls. It belongs to the Gemini Flash lineage as the successor to earlier Flash and Flash-Lite models in the broader Gemini model family.

Can you self-host Gemini 3.1 Flash Lite?

No. We found no public weights for Gemini 3.1 Flash Lite on Hugging Face, so there is nothing to download, quantize or serve on your own GPUs: no VRAM budget, no vLLM or GGUF build to plan for. The only way to run it is over an API.

Skip the deploy — use LLM.API as your endpoint

No GPUs, no quantization trade-offs. Same model, OpenAI-compatible, with custom volume discounts on request.

Get API keySee API code ↓

Providers

Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick — Balanced (price + speed), Nitro (fastest), or Exacto (highest tool-calling accuracy).

List price by provider

InputOutput
google-ai-studio$0.25 in
$1.5 out
google-vertex$0.25 in
$1.5 out

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

Provider Input Output Cache read /M Latency Throughput Uptime
google-ai-studioin $0.25; out $1.5 per 1M tokens1M tokensvision, tools, streaming, reasoning, JSON
google-vertexin $0.25; out $1.5 per 1M tokens1M tokensvision, tools, streaming, reasoning, JSON

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 11 Oct 2026.

Try this model

Test Gemini 3.1 Flash Lite right here — free to start.

Gemini 3.1 Flash Lite
Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call the model through the OpenAI-compatible API.

Switching from OpenAI? Change 2 lines.

- base_url="https://api.openai.com/v1"
- api_key="YOUR_OPENAI_KEY"
+ base_url="https://api.llmapi.ai/v1"
+ api_key="YOUR_LLMAPI_KEY"

Everything else stays the same — SDK, message format, tools, streaming. Set model to gemini-3.1-flash-lite and the call runs unchanged.

bash
curl https://api.llmapi.ai/v1/chat/completions \
  -H "Authorization: Bearer $LLMAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-3.1-flash-lite",
    "messages": [
      {"role": "user", "content": "Give me three crisp launch checklist items."}
    ],
  "reasoning_effort": "minimal"
  }'
from openai import OpenAI

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

resp = client.chat.completions.create(
    model="gemini-3.1-flash-lite",
    messages=[
        {"role": "system", "content": "You are a precise product assistant."},
        {"role": "user", "content": "Give me three crisp launch checklist items."},
    ],
    reasoning_effort="minimal",
)
print(resp.choices[0].message.content)
import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.LLMAPI_KEY,
  baseURL: "https://api.llmapi.ai/v1",
});

const resp = await client.chat.completions.create({
  model: "gemini-3.1-flash-lite",
  messages: [
    { role: "user", content: "Give me three crisp launch checklist items." },
  ],
});

console.log(resp.choices[0].message.content);
stream = client.chat.completions.create(
    model="gemini-3.1-flash-lite",
    messages=[{"role": "user", "content": "Write a haiku about latency."}],
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta.content
    if delta:
        print(delta, end="", flush=True)
{
  "model": "gemini-3.1-flash-lite",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ],
  "reasoning_effort": "minimal"
}

ENDPOINTS

Supported endpoints & features

Base URL https://api.llmapi.ai/v1 · model ID gemini-3.1-flash-lite · OpenAI-compatible.

  • POST /v1/chat/completionsChat Completions — supported
FeatureStatusHow to set it
Function calling (tools)Supportedtools, tool_choice
Parallel tool callsNot supportedparallel_tool_calls
JSON modeSupportedresponse_format: {"type": "json_object"}
Structured outputsNot supportedresponse_format: {"type": "json_schema", …}
StreamingSupportedstream: true
Vision (image input)Supportedimage_url content parts
Web searchNot supportedweb_search
Reasoning effortminimal · low · medium · highreasoning_effort: "minimal"

ERRORS

Errors & fallback routing

CodeWhat it meansWhat to do
401Missing or invalid API key.Send Authorization: Bearer YOUR_LLMAPI_KEY and check the key is active.
429Rate limited, or the upstream provider is throttling the request.Back off and retry with jitter; the gateway also retries the request on another provider where one is available.
5xxUpstream provider error or timeout.Retry; fallback routing sends the retry to the next healthy provider for this model.

When a provider fails or throttles, the request is routed to the next provider serving this model — the ones listed in the providers table above.

Uptime

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

Last 30 days

30/30 days operational | 100.00% uptime

30 days ago Today
Operational Degraded Outage Maintenance
See All Incidents

Gemini 3.1 Flash Lite in production

What the model is good at.

  • Fast Chat Responses

    Provides quick, low-latency conversational answers suitable for assistants, FAQ bots, and interactive applications with concise, relevant outputs.

  • Lightweight Text Tasks

    Handles everyday text tasks such as drafting, rewriting, and summarizing short content, optimized for speed and efficiency.

  • Basic Image Analysis

    Performs lightweight image inspection, enabling simple recognition or extraction tasks where high speed and low resource usage are important.

  • Simple Text Translation

    Translates short text snippets between common languages for quick understanding or localization in low-latency applications.

  • Text Extraction OCR

    Extracts readable text from clear images or screenshots to support quick copying, search, or downstream text processing workflows.

What you get when you call Gemini 3.1 Flash Lite through LLM.API.

  • Unified AI Routing

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

    One endpoint, every model

  • Cost-Aware Orchestration

    Enforce spend limits, choose cheaper equivalents, and mix premium and budget models per call so you control cost without manually tuning every request.

    Cut spend, keep quality

  • Resilient Fallback Logic

    Define automatic failover chains so timeouts, rate limits, or provider outages transparently retry on backup models—keeping your AI features online and users unblocked.

    No single point of failure

  • Full-Stack Observability

    Trace every request across providers with logs, metrics, and latency breakdowns so you can debug prompts, tune routing, and prove reliability in production.

    See every token, everywhere

  • Task-Level Abstractions

    Call high-level tasks—chat, generation, tools, embeddings—through a single, normalized API so you can swap underlying models without rewriting business logic.

    Code to tasks, not models

  • High-Throughput Batch

    Send massive batches of requests through one pipeline with concurrency control, retries, and deduping to efficiently process workloads like evals, backfills, and indexing.

    Millions of calls, one job

When to Use — When NOT to Use

Use it if...

  • You need a very low-cost model to handle high-volume everyday assistant traffic.
  • You need fast, lightweight text generation for chatbots, prompts, and UI helpers.
  • Your use case involves simple data extraction or classification from short text snippets.
  • Your use case involves quick content drafting, rewriting, or summarizing short-form text.
  • You need multimodal understanding of small images or screenshots with budget constraints.
  • You need a fallback model for non-critical tasks when stronger models are rate-limited.
  • Your use case involves educational helpers that answer straightforward conceptual questions quickly.

Avoid if...

  • You need advanced long-context reasoning across large documents, codebases, or research papers.
  • You need state-of-the-art coding assistance for complex refactors, debugging, or multi-file changes.
  • Your workload requires high-fidelity image generation or detailed visual editing capabilities.
  • Your workload requires nuanced multi-step reasoning, planning, or tool orchestration for agents.
  • You need top-tier performance on safety-critical domains like medical, legal, or financial analysis.
  • You need robust multilingual support with high accuracy on low-resource or mixed-language text.
  • Your workload requires stable performance on extremely long conversations without context degradation.

LLM API rating for Gemini 3.1 Flash Lite

★ 4.1/ 5.0LLM API rating
50% production adoption · 30% category benchmark · 20% tooling & value✓ Verified benchmark · LMArena + Artificial Analysis
4.8Production adoptionVery high production volume
2.7Benchmark qualityArtificial Analysis Intelligence Index 15.6 · LMArena text rank #124, Elo 1416
4.3Developer ecosystemContext window, tool and JSON support, price against the category median
Reasoning & chat 4.1Coding 4.0Vision & multimodal 4.3Tool & function calling 4.8Long context 4.8Voice & audio 4.2
How is this rating calculated?

Every model gets a separate 1.0–5.0 score for each category it can be used in, so a model can rate high for coding and lower for vision. The score combines three parts:

  1. Production adoption — 50%. How widely the model is actually used in production: provider traffic, LiteLLM and gateway usage, open-weight downloads and its LMArena rank. Proven, widely used models score highest.
  2. Category benchmark — 30%. The public benchmark that matters for that category — the Artificial Analysis Intelligence Index and LMArena Elo for reasoning, LMArena coding Elo for coding, image and document support plus vision Elo for vision, tool and JSON support for function calling, measured speed for voice.
  3. Tooling & value — 20%. Context window, support for tools, structured outputs and streaming, and price compared with the category median.

Scores of 4.0 and above are shown in green, 3.0–3.9 in yellow, below 3.0 in red. When a model has no public data for a part, it gets a neutral value instead of an invented number.

A “Verified benchmark” mark (✓) means an independent lab — LMArena or Artificial Analysis — has actually tested that model, and the source figure is shown. Models without that mark are labelled “Estimated from published specs”: the score comes from the published context window, tool and schema support, price and adoption only, because no independent lab has tested them yet.

One exception: a brand-new top-tier model that scores very high on the independent benchmarks but has no usage history yet is rated on the benchmark (60%) and tooling & value (40%) alone, so a missing adoption figure can't hold it back. All inputs come from public sources and are refreshed weekly.

Adoption and benchmark inputs come from public sources — LMArena, Artificial Analysis Intelligence Index and published model specs. Updated weekly.

COMMUNITY

What developers say about Gemini 3.1 Flash Lite

LMArena#124 · Elo 1416Arena coding#166 · Elo 1400Speed—Sources: LMArena, Artificial Analysis

Summarised from public developer discussions and write-ups. Opinions are the sources’, not LLM.API’s.

  • Developers on the Google AI Forum report a significant performance degradation in Gemini 3.1 Flash-Lite following the release of newer versions, citing increased failures in basic text interpretation and function calling.
  • Technical reviews highlight that while the model is highly cost-effective and supports a 1M-token context window, it suffers from slow first-token latency averaging over 6 seconds.
  • Developers on ReturnSignals found Gemini 3.1 Flash-Lite outperformed the subsequent 3.5 version in specific e-commerce agent benchmarks, particularly when utilizing 'medium' thinking settings for better task accuracy.

SOURCES

Frequently Asked Questions

  • What is Gemini 3.1 Flash Lite?

    Gemini 3.1 Flash Lite is a lightweight, cost-efficient Google model optimized for fast, high-volume inference via the Gemini API and compatible gateways like LLM.API.

  • What is Gemini 3.1 Flash Lite best suited for?

    It is best for latency-sensitive, high-throughput tasks like chatbots, simple agents, rapid content generation, and bulk text processing where low cost matters.

  • What context window does Gemini 3.1 Flash Lite support?

    Gemini 3.1 Flash Lite typically offers a mid-sized context window, suitable for most conversational and task-oriented applications rather than very long-document reasoning.

  • How fast is Gemini 3.1 Flash Lite through LLM.API?

    Gemini 3.1 Flash Lite is tuned for low latency, so responses are generally faster than larger Gemini models when served through LLM.API.

  • How much does it cost to use Gemini 3.1 Flash Lite on LLM.API?

    LLM.API usage is billed per-token for input and output; check the LLM.API pricing page for the latest Gemini 3.1 Flash Lite rates.

  • Which modalities does Gemini 3.1 Flash Lite support via LLM.API?

    Through LLM.API, Gemini 3.1 Flash Lite supports text input and output, and may support additional modalities depending on LLM.API’s current Gemini feature mapping.

  • How do I call Gemini 3.1 Flash Lite using the LLM.API endpoint?

    Specify the model name "gemini-3.1-flash-lite" (or LLM.API’s documented alias) in your completion or chat request to route traffic to this model.

  • How does Gemini 3.1 Flash Lite compare to Gemini 3.1 Flash or Pro?

    Gemini 3.1 Flash Lite is cheaper and faster but generally less capable than Gemini 3.1 Flash or Pro on complex reasoning, coding, and long-context tasks.

  • What are key limitations of Gemini 3.1 Flash Lite?

    It may struggle with very long contexts, complex multi-step reasoning, nuanced coding tasks, and tasks requiring the strongest Gemini-series accuracy and reliability.

  • Can I fine-tune Gemini 3.1 Flash Lite through LLM.API?

    LLM.API typically exposes Gemini models as hosted APIs without user-level fine-tuning; use prompts, system instructions, and retrieval to specialize behavior instead.

COMPARE

Competitive Models

Nano Banana 2 (Gemini 3.1 Flash Image Preview) is Google DeepMind’s image generation and editing model built on the Gemini 3.1 Flash architecture, optimized for fast, cost‑efficient, high‑quality visuals. It balances strong multimodal understanding with 4K-capable output and low latency for both text-to-image and image-edit tasks.

  • Gemini 3.5 Flash

    • Instruction Following

    Gemini 3.5 Flash is Google’s natively multimodal reasoning model optimized for very low latency and cost while maintaining frontier‑level performance, particularly for coding and agentic workflows.

  • Gemini 3.1 Flash TTS Preview

    • Text Generation

    Gemini 3.1 Flash TTS Preview is Google’s low-latency text‑to‑speech model that generates natural, expressive speech with fine-grained control via style prompts and audio tags. It is optimized for fast, high‑quality voice synthesis across many languages and voices.

    • Get one key to every model

    Swap your API key. Keep your code.

    Start for free Talk to sales