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Google · Efficient

Gemini 3.8 Flash

Added Sep 2, 2026

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Use in the app

Google's fast multimodal model for agentic workloads, including coding, tool use, image understanding, PDF and document extraction, audio, and video. Its capabilities, limits, reasoning behavior, and pricing currently mirror Gemini 3.7 Flash.

  • Reasoning
  • Tools
  • Vision
  • Structured output
  • PDF input
  • Audio input
  • Video input
Input
Text, Images, Video, Audio
Output
Text
Reasoning effort
minimal · low · medium · high
Discovery tier
Efficient

Pricing

USD per 1M tokens
Input
$0.75
Output
$3.75
Cache read
$0.075
Cache write
$0.0417

Reference prices as published by the model provider. API example

Specifications

Context window
1M
Max output
65.5K
Average output (7 days)
1.2K tokens
Longer than 79% of 473 models

Side by side

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TryGPT 6 SolMuse Spark 1.2DeepSeek V4.1 Flash ThinkingAll 3

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

Intelligence Index

39.8

Tested at medium reasoning effort

Better than 84% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    74.1

    Composite of the coding evaluations

    Better than 85% of 168 scored models

Agentic work

  • AutomationBench-AA

    60.9%

    Workflow automation, penalized for guardrail violations

    Better than 84% of 173 scored models

  • AutomationBench-AA tasks completed

    31.1%

    Workflows finished end to end without a guardrail violation

    Better than 88% of 173 scored models

  • AA-Briefcasev1.1

    1,181 Elo

    Agentic knowledge work, rated head to head

    Better than 57% of 166 scored models

  • GDPval-AAv2.1

    1,423 Elo

    Economically valuable occupational tasks, rated head to head

    Better than 65% of 187 scored models

Document reasoning

  • GDP.pdf

    22.8%

    Professional reasoning over PDF documents, all-pass rate

    Better than 81% of 171 scored models

  • AA-LCRv1.1

    84.0%

    Reasoning across long documents

    Better than 94% of 309 scored models

  • MLCR-AA

    12.8%

    Long-context reasoning over medical records

    Better than 57% of 121 scored models

Reasoning and knowledge

  • Humanity’s Last Exam

    42.1%

    Expert-level questions across academic fields

    Better than 81% of 323 scored models

  • GPQA Diamond

    93.5%

    Graduate-level science questions

    Better than 92% of 291 scored models

Coding

  • Terminal-Benchv4.0

    19.7%

    Practical software engineering in a terminal

    Better than 72% of 171 scored models

  • SciCode

    55.1%

    Scientific computing in Python

    Better than 77% of 174 scored models

Legacy evaluations

  • AA-LCR (unversioned)

    84.0%

    Long-context reasoning, earlier grading

    Better than 94% of 309 scored models

Scores from Artificial Analysis, updated Oct 26, 2026. Gemini 3.8 Flash was tested at medium reasoning effort; compare scores measured at the same effort.Artificial Analysis

Live routing data

Performance and reliability

Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.

Throughput
362.4 tok/s
Faster than 94% of 256 models
Time to first token
9.2 s
Quicker than 10% of 256 models
Prompt cache hits
5.4%
About $0.71 per 1M input tokens with caching
Request success (24h)
99.8%
Reliable

Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.

Hourly request success · last 72 hoursLowest hour 92.5%
  • Healthy (98% or more)
  • Degraded (90–98%)
  • Down (under 90%)
  • No requests

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart
curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "google/gemini-3.8-flash",
    "reasoning_effort": "medium",
    "messages": [{ "role": "user", "content": "Explain this stack trace." }]
  }'
import os
from openai import OpenAI

client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
    model="google/gemini-3.8-flash",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Google

Other Google models, starting with the same family.