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Gemini 3.8 Flash API: Endpoint & 15% Discount

Up to 15%

Gemini 3.8 Flash brings Google Gemini conversational intelligence to LLM.API so teams can ship assistants and agents without juggling multiple vendor SDKs.

Start Using API

What is Gemini 3.8 Flash?

On LLM.API, Gemini 3.8 Flash (gemini-3.8-flash) serves as a Google Gemini conversational model for products that need 1M tokens context windows and predictable token pricing. Google's most intelligent Flash model, engineered for long-horizon software engineering, autonomous agents, and complex enterprise workflows.

Can you self-host Gemini 3.8 Flash?

No. We found no public weights for Gemini 3.8 Flash 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, up to 30% below list price.

Get API keySee API code ↓

Providers

LLM.API routes Gemini 3.8 Flash to the providers below, with discounted effective rates versus list price.

List price by provider

InputOutput
google-ai-studio$0.75 in
$3.75 out
google-vertex$0.75 in
$3.75 out
novita$0.75 in
$3.75 out

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

ProviderPricingContextCapabilities
google-ai-studioin $0.75; out $3.75 per 1M tokens1M tokensvision, tools, streaming, reasoning, web search, JSON, structured
google-vertexin $0.75; out $3.75 per 1M tokens1M tokensvision, tools, streaming, reasoning, web search, JSON, structured
novita15% offin $0.75; out $3.75 per 1M tokens1M tokensvision, tools, streaming, reasoning, web search, JSON, structured

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 8 Oct 2026.

Try this model

Test Gemini 3.8 Flash right here — free to start.

Gemini 3.8 Flash
Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call Gemini 3.8 Flash through the OpenAI-compatible API — POST /v1/chat/completions.

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-3.8-flash 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-3.8-flash",
    "messages": [
      {"role": "user", "content": "Give me three crisp launch checklist items."}
    ],
  "reasoning_effort": "medium"
  }'
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-3.8-flash",
    messages=[
        {"role": "system", "content": "You are a precise product assistant."},
        {"role": "user", "content": "Give me three crisp launch checklist items."},
    ],
    reasoning_effort="medium",
)
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-3.8-flash",
  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-3.8-flash",
    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-3.8-flash",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ],
  "reasoning_effort": "medium"
}

ENDPOINTS

Supported endpoints & features

Base URL https://api.llmapi.ai/v1 · model ID gemini-3.8-flash · 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 outputsSupportedresponse_format: {"type": "json_schema", …}
StreamingSupportedstream: true
Vision (image input)Supportedimage_url content parts
Web searchSupportedweb_search
Reasoning effortlow · medium · highreasoning_effort: "medium" default medium

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.

Gemini 3.8 Flash in production

What the model is good at.

  • Multi-step reasoning

    Breaks down complex problems into intermediate steps before answering. Grounded in the model's chat role rather than generic chat claims.

  • Tool-ready dialogue

    Works well in agent loops that call functions, browsers, or retrieval APIs. Relevant for `gemini-3.8-flash` workloads on LLM.API.

  • Code assistance

    Helps write, explain, refactor, and debug application code across common languages. Grounded in the model's chat role rather than generic chat claims.

  • Conversational UX

    Maintains coherent multi-turn assistant behavior for product chat surfaces.

  • Long-context synthesis

    Summarizes and cross-references information across large prompts when context allows. Reflects Google Gemini positioning for this endpoint.

What you get when you call Gemini 3.8 Flash through LLM.API.

  • Unified AI Routing

    Practical: Reach Gemini 3.8 Flash and sibling models through one OpenAI-compatible endpoint.

    One endpoint, every model.

  • Cost Control

    Production: Compare provider price points and keep spend visible as you scale Gemini 3.8 Flash.

    More performance, less spend.

  • Reliability Layer

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

    Never go dark on users.

  • Observability

    Trace prompts, tokens, and errors for Gemini 3.8 Flash alongside the rest of your stack.

    See every token, everywhere.

  • Drop-in SDKs

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

    Think tasks, not models.

  • Model Breadth

    Swap Gemini 3.8 Flash for chat, media, or embedding alternatives without rewriting auth.

    Scale from 1 to millions.

When to Use — When NOT to Use

Use it if...

  • You need provider failover options exposed for this model id (Gemini 3.8 Flash)
  • Your prompts benefit from the model's family strengths (reasoning, speed, or cost) (Gemini 3.8 Flash)
  • You want OpenAI-compatible chat completions through a single LLM.API key (Gemini 3.8 Flash)
  • You need a general-purpose text model for assistants, agents, or content workflows (Gemini 3.8 Flash)

Avoid if...

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

LLM API rating for Gemini 3.8 Flash

★ 4.4/ 5.0LLM API rating
50% production adoption · 30% category benchmark · 20% tooling & value✓ Verified benchmark · LMArena + Artificial Analysis
4.8Production adoptionVery high production volume
4.2Benchmark qualityArtificial Analysis Intelligence Index 40.9 · LMArena text rank #7, Elo 1494
3.7Developer ecosystemContext window, tool and JSON support, price against the category median
Reasoning & chat 4.4Coding 4.4Vision & multimodal 4.3Tool & function calling 4.6Long context 4.6
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.

BENCHMARKS

Gemini 3.8 Flash benchmark scores

Intelligence index

This model41.
Tracked median24.

Scale: 0-100 index points

Output speed

This model261 t/s

Scale: 0-400 tokens per second

Reference price per 1M tokens

Input$0.75
Output$3.75

Bars compare input and output list prices for this model.

Intelligence index+71% vs median
Gemini 3.8 Flash API: Endpoint & 30% Discount41
Median tracked model24

Higher is better. The median covers every model tracked by Artificial Analysis.

Output speed
261tok/s

Measured throughput; the full dial is 300 tokens per second.

Reference price / 1M tokens
Input$0.75
Output$3.75

Provider list prices, before the LLM.API discount.

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

RELIABILITY

Gemini 3.8 Flash 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

LMArena#7 · Elo 1494Arena coding#13 · Elo 1512Speed261 tokens/sSources: LMArena, Artificial Analysis

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 3.8 Flash.

  • 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 3.8 Flash support streaming?

    Yes—at least one listed provider advertises streaming.

  • What are limitations of Gemini 3.8 Flash?

    Like other API models, Gemini 3.8 Flash can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.

  • Can I use tools or structured outputs with Gemini 3.8 Flash?

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

  • Which providers serve Gemini 3.8 Flash?

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

  • When should I choose Gemini 3.8 Flash?

    Your prompts benefit from the model's family strengths (reasoning, speed, or cost) — especially when you specifically need Gemini 3.8 Flash.

  • How do I call Gemini 3.8 Flash via API?

    Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "gemini-3.8-flash" and your LLM.API key. See the code snippet on this page.

  • What modalities does Gemini 3.8 Flash support?

    Gemini 3.8 Flash accepts text, image and produces text according to its architecture metadata on LLM.API.

  • What is Gemini 3.8 Flash?

    Google's most intelligent Flash model, engineered for long-horizon software engineering, autonomous agents, and complex enterprise workflows. On LLM.API it is addressed as `gemini-3.8-flash`.

  • Where is the canonical page for Gemini 3.8 Flash?

    https://llmapi.ai/models/google-gemini-3-8-flash/

  • What is the context length for Gemini 3.8 Flash?

    Reported context for Gemini 3.8 Flash is 1M tokens. Always verify the active provider row if multiple providers are listed.

  • How is Gemini 3.8 Flash priced on LLM.API?

    Listed pricing metadata shows: In $0.75 / 1M tokens · Out $3.75 / 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.

  • Is Gemini 3.8 Flash a chat model?

    Yes—Gemini 3.8 Flash is exposed as a chat/completions-style endpoint on LLM.API.

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