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Google · Efficient

Gemini 3.5 Flash Thinking

Added May 19, 2026

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Use in the app

Gemini 3.5 Flash with higher thinking enabled for harder reasoning, agentic coding, tool use, multimodal analysis, and long-context workflows. Audio input costs $3.00 per million audio tokens.

  • Reasoning
  • Vision
  • 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
$1.50
Output
$9.00
Cache read
$0.15
Cache write
$0.0833

Reference prices as published by the model provider. API example

Specifications

Context window
1M
Max output
65.5K
Average output (7 days)
1.7K tokens
Longer than 88% of 465 models

Side by side

Gemini 3.5 Flash Thinking compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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Gemini 3.5 Flash ThinkingGoogleThis modelGemini 3.8 FlashGoogleRemove
ProviderGoogleGoogle
Discovery tierEfficientEfficient
AddedMay 19, 2026Sep 2, 2026
Limits
Context window1.05M1.05M
Max output65.54K65.54K
Average output1.7K tokens1.2K tokens
Pricing, USD per 1M tokens
Input$1.50$0.75Best
Output$9.00$3.75Best
Cache read$0.15$0.075Best
Cache write$0.0833$0.0417Best
Performance
Throughput46.8 tok/s223.8 tok/sBest
Time to first token8.7 s7.5 sBest
Request success (24h)90.1%99.8%Best
Benchmark indexes
Intelligence Index32.639.8Best
Coding Index70.174.1Best
Capabilities
ReasoningYesYes
ToolsNoYes
VisionYesYes
Structured outputNoYes
PDF inputNoYes
Audio inputYesYes
Video inputYesYes
Reasoning effortminimal, low, medium, highminimal, low, medium, high

Public evaluations

Benchmarks

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

  • Gemini 3.5 Flash Thinking
  • Gemini 3.8 Flash

Intelligence Index

  • Gemini 3.5 Flash Thinking32.6
  • Gemini 3.8 Flash39.8

Gemini 3.5 Flash Thinking tested at high reasoning effort

Gemini 3.5 Flash Thinking: Better than 76% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Gemini 3.5 Flash Thinking: 70.1
    • Gemini 3.8 Flash: 74.1Top

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Gemini 3.5 Flash Thinking: 42.1%
    • Gemini 3.8 Flash: 60.9%Top

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Gemini 3.5 Flash Thinking: 13.5%
    • Gemini 3.8 Flash: 31.1%Top

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Gemini 3.5 Flash Thinking: 874 Elo
    • Gemini 3.8 Flash: 1,181 EloTop

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Gemini 3.5 Flash Thinking: 1,202 Elo
    • Gemini 3.8 Flash: 1,423 EloTop

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Gemini 3.5 Flash Thinking: 19.8%
    • Gemini 3.8 Flash: 22.8%Top

  • AA-LCRv1.1

    Reasoning across long documents

    • Gemini 3.5 Flash Thinking: 73.3%
    • Gemini 3.8 Flash: 84.0%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • Gemini 3.5 Flash Thinking: 10.0%
    • Gemini 3.8 Flash: 12.8%Top

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Gemini 3.5 Flash Thinking: 42.7%Top
    • Gemini 3.8 Flash: 42.1%

  • GPQA Diamond

    Graduate-level science questions

    • Gemini 3.5 Flash Thinking: 92.2%
    • Gemini 3.8 Flash: 93.5%Top

  • IFBench

    Following precise output instructions

    • Gemini 3.5 Flash Thinking: 76.3%
    • Gemini 3.8 Flash: —

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Gemini 3.5 Flash Thinking: 6.6%
    • Gemini 3.8 Flash: 19.7%Top

  • SciCode

    Scientific computing in Python

    • Gemini 3.5 Flash Thinking: 53.9%
    • Gemini 3.8 Flash: 55.1%Top

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Gemini 3.5 Flash Thinking: 40.9%
    • Gemini 3.8 Flash: —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Gemini 3.5 Flash Thinking: 95.3%
    • Gemini 3.8 Flash: —

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Gemini 3.5 Flash Thinking: 73.3%
    • Gemini 3.8 Flash: 84.0%Top

Scores from Artificial Analysis, updated Oct 26, 2026. Gemini 3.5 Flash Thinking was tested at high 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
46.8 tok/s
Faster than 34% of 250 models
Time to first token
8.7 s
Quicker than 14% of 250 models
Prompt cache hits
21.3%
About $1.21 per 1M input tokens with caching
Request success (24h)
90.2%
Unstable

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 60.0%
  • 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-3.5-flash-thinking",
    "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-3.5-flash-thinking",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Google

Other Google models, starting with the same family.