NOUMENOpen app
NOUMEN

Google · Efficient

Gemini 3.6 Flash

Added Jul 21, 2026

Compare models

Choose up to 3 models to compare.1 of 3 chosen

Loading models…

Use in the app

Google's GA Flash model delivers near-Pro coding and agentic capability at Flash-tier speed and cost, with improved token efficiency over Gemini 3.5 Flash. It is optimized for multi-step orchestration, full-stack code refactoring, general reasoning, and multimodal workflows.

  • 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)
932 tokens
Longer than 69% of 465 models

Side by side

Gemini 3.6 Flash compared

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

Compare models

Choose up to 3 models to compare.1 of 3 chosen

Loading models…

Gemini 3.6 FlashGoogleThis modelGLM 5.2 Thinking TEEZ.aiRemove
ProviderGoogleZ.ai
Discovery tierEfficientOpen weights
AddedJul 21, 2026Jun 16, 2026
Limits
Context window1.05M1.05M
Max output65.54K131.07KBest
Average output932 tokens2K tokens
Pricing, USD per 1M tokens
Input$0.75Best$1.40
Output$3.75Best$4.40
Cache read$0.075Best$0.70
Cache write$0.0417—
Performance
Throughput234.0 tok/sBest28.8 tok/s
Time to first token7.9 sBest21.3 s
Request success (24h)99.4%Best87.5%
Benchmark indexes
Intelligence Index34.0Best33.7
Coding Index69.2Best68.8
Capabilities
ReasoningYesYes
ToolsYesYes
VisionYesNo
Structured outputYesYes
PDF inputYesNo
Audio inputYesNo
Video inputYesNo
Reasoning effortminimal, low, medium, highlow, medium, high, xhigh, max

Public evaluations

Benchmarks

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

  • Gemini 3.6 Flash
  • GLM 5.2 Thinking TEE

Intelligence Index

  • Gemini 3.6 Flash34.0
  • GLM 5.2 Thinking TEE33.7

Gemini 3.6 Flash tested at high reasoning effort

Gemini 3.6 Flash: Better than 77% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Gemini 3.6 Flash: 69.2Top
    • GLM 5.2 Thinking TEE: 68.8

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Gemini 3.6 Flash: 53.0%Top
    • GLM 5.2 Thinking TEE: 28.4%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Gemini 3.6 Flash: 16.4%Top
    • GLM 5.2 Thinking TEE: 6.2%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Gemini 3.6 Flash: 955 Elo
    • GLM 5.2 Thinking TEE: 1,227 EloTop

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Gemini 3.6 Flash: 1,286 Elo
    • GLM 5.2 Thinking TEE: 1,373 EloTop

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Gemini 3.6 Flash: 17.4%Top
    • GLM 5.2 Thinking TEE: 10.4%

  • AA-LCRv1.1

    Reasoning across long documents

    • Gemini 3.6 Flash: 80.0%Top
    • GLM 5.2 Thinking TEE: 78.3%

  • MLCR-AA

    Long-context reasoning over medical records

    • Gemini 3.6 Flash: 10.0%
    • GLM 5.2 Thinking TEE: 12.8%Top

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Gemini 3.6 Flash: 40.8%
    • GLM 5.2 Thinking TEE: 41.1%Top

  • GPQA Diamond

    Graduate-level science questions

    • Gemini 3.6 Flash: 92.8%Top
    • GLM 5.2 Thinking TEE: 89.5%

  • IFBench

    Following precise output instructions

    • Gemini 3.6 Flash: —
    • GLM 5.2 Thinking TEE: 73.3%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Gemini 3.6 Flash: 7.1%Top
    • GLM 5.2 Thinking TEE: 1.0%

  • SciCode

    Scientific computing in Python

    • Gemini 3.6 Flash: 53.4%Top
    • GLM 5.2 Thinking TEE: 51.2%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Gemini 3.6 Flash: —
    • GLM 5.2 Thinking TEE: 50.8%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Gemini 3.6 Flash: —
    • GLM 5.2 Thinking TEE: 99.1%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Gemini 3.6 Flash: 80.0%Top
    • GLM 5.2 Thinking TEE: 78.3%

Scores from Artificial Analysis, updated Oct 26, 2026. Gemini 3.6 Flash 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
234.0 tok/s
Faster than 88% of 250 models
Time to first token
7.9 s
Quicker than 16% of 250 models
Prompt cache hits
29.0%
About $0.55 per 1M input tokens with caching
Request success (24h)
99.5%
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 96.2%
  • 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-3.6-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-3.6-flash",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Google

Other Google models, starting with the same family.