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Google · Efficient

Gemini 3.7 Flash

Added Aug 13, 2026

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Use in the app

Google's frontier-performance Flash model for multimodal and agentic workloads, including coding, tool use, image understanding, PDF and document extraction, audio, and video. Google reports 65.3% on DeepSWE v1.1, up from 49.0% for Gemini 3.6 Flash, and 34% on GDP.pdf, up from 14%.

  • 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)
850 tokens
Longer than 67% of 465 models

Side by side

Gemini 3.7 Flash compared

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

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Gemini 3.7 FlashGoogleThis modelGemini 3 Flash (Preview)GoogleRemove
ProviderGoogleGoogle
Discovery tierEfficientEfficient
AddedAug 13, 2026Dec 17, 2025
Limits
Context window1.05M1.05M
Max output65.54K65.54K
Average output850 tokens592 tokens
Pricing, USD per 1M tokens
Input$0.75$0.50Best
Output$3.75$3.00Best
Cache read$0.075$0.05Best
Cache write$0.0417—
Performance
Throughput146.6 tok/s211.2 tok/sBest
Time to first token11.3 s1.3 sBest
Request success (24h)98.3%99.8%Best
Benchmark indexes
Intelligence Index39.1Best17.9
Coding Index76.1—
Math Index—55.7
Capabilities
ReasoningYesYes
ToolsYesYes
VisionYesYes
Structured outputYesYes
PDF inputYesNo
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 3.7 Flash
  • Gemini 3 Flash (Preview)

Intelligence Index

  • Gemini 3.7 Flash39.1
  • Gemini 3 Flash (Preview)17.9

Gemini 3.7 Flash tested at high reasoning effort

Gemini 3.7 Flash: Better than 83% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Gemini 3.7 Flash: 76.1
    • Gemini 3 Flash (Preview): —

  • Math Index

    Composite of the math evaluations

    • Gemini 3.7 Flash: —
    • Gemini 3 Flash (Preview): 55.7

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Gemini 3.7 Flash: 62.0%
    • Gemini 3 Flash (Preview): —

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Gemini 3.7 Flash: 30.0%
    • Gemini 3 Flash (Preview): —

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Gemini 3.7 Flash: 1,113 Elo
    • Gemini 3 Flash (Preview): —

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Gemini 3.7 Flash: 1,396 Elo
    • Gemini 3 Flash (Preview): —

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Gemini 3.7 Flash: 23.6%
    • Gemini 3 Flash (Preview): —

  • AA-LCRv1.1

    Reasoning across long documents

    • Gemini 3.7 Flash: 81.7%Top
    • Gemini 3 Flash (Preview): 55.3%

  • MLCR-AA

    Long-context reasoning over medical records

    • Gemini 3.7 Flash: 12.2%
    • Gemini 3 Flash (Preview): —

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Gemini 3.7 Flash: 47.9%Top
    • Gemini 3 Flash (Preview): 15.0%

  • GPQA Diamond

    Graduate-level science questions

    • Gemini 3.7 Flash: 94.5%Top
    • Gemini 3 Flash (Preview): 81.2%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Gemini 3.7 Flash: —
    • Gemini 3 Flash (Preview): 88.2%

  • IFBench

    Following precise output instructions

    • Gemini 3.7 Flash: —
    • Gemini 3 Flash (Preview): 55.1%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Gemini 3.7 Flash: 13.6%
    • Gemini 3 Flash (Preview): —

  • SciCode

    Scientific computing in Python

    • Gemini 3.7 Flash: 57.2%
    • Gemini 3 Flash (Preview): —

  • LiveCodeBench

    Recent competitive programming problems

    • Gemini 3.7 Flash: —
    • Gemini 3 Flash (Preview): 79.7%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Gemini 3.7 Flash: —
    • Gemini 3 Flash (Preview): 55.7%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Gemini 3.7 Flash: —
    • Gemini 3 Flash (Preview): 31.8%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Gemini 3.7 Flash: —
    • Gemini 3 Flash (Preview): 43.3%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Gemini 3.7 Flash: 81.7%Top
    • Gemini 3 Flash (Preview): 55.3%

Scores from Artificial Analysis, updated Oct 26, 2026. Gemini 3.7 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
146.6 tok/s
Faster than 74% of 250 models
Time to first token
11.3 s
Quicker than 8% of 250 models
Prompt cache hits
14.3%
About $0.65 per 1M input tokens with caching
Request success (24h)
98.3%
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 78.9%
  • 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-3.7-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-3.7-flash",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Google

Other Google models, starting with the same family.