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Gemini 3.8 Flash

Up to 30%

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.

Providers

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

List price by provider ($ / 1M tokens)

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

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

ProviderPricingContextCapabilities
google-ai-studio30% offin $750; out $3750 per 1M tokens1M tokensvision, tools, streaming, reasoning, web search, JSON, structured
google-vertex30% offin $750; out $3750 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 3.8 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.

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

5 Core Capabilities

  • 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.

6 Most Valuable Use Cases

  • Internal knowledge assistants grounded with your retrieval layer with Gemini 3.8 Flash
  • Meeting-note cleanup and action-item generation
  • Multilingual localization drafts for UX copy with Gemini 3.8 Flash
  • Customer support copilots that draft accurate, on-brand replies
  • Product analytics narration and anomaly explanations with Gemini 3.8 Flash
  • Policy Q&A bots with careful refusal behavior

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 3.8 Flash on LLM.API?

  • Unified AI Routing

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

  • Cost Control

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

  • Reliability Layer

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

  • Observability

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

  • Drop-in SDKs

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

  • Model Breadth

    Swap Gemini 3.8 Flash 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 3.8 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

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.

Artificial Analysis Intelligence Index41
Median index across all tracked models24
Output speed261 tokens/s
Reference input price$0.75 / 1M tokens
Reference output price$3.75 / 1M tokens

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

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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