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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 modelMuse Spark OpenMetaRemove
ProviderGoogleMeta
Discovery tierEfficientFrontier
AddedAug 13, 2026Oct 15, 2026
Limits
Context window1.05M1.05M
Max output65.54K—
Average output850 tokens1.3K tokens
Pricing, USD per 1M tokens
Input$0.75Best$1.25
Output$3.75Best$4.25
Cache read$0.075Best$0.15
Cache write$0.0417—
Performance
Throughput146.6 tok/s—
Time to first token11.3 s—
Request success (24h)98.3%—
Benchmark indexes
Intelligence Index39.1Best38.9
Coding Index76.1—
Capabilities
ReasoningYesYes
ToolsYesYes
VisionYesYes
Structured outputYesYes
PDF inputYesNo
Audio inputYesNo
Video inputYesNo
Reasoning effortminimal, low, medium, highlow, medium, high, xhigh

Public evaluations

Benchmarks

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

  • Gemini 3.7 Flash
  • Muse Spark Open

Intelligence Index

  • Gemini 3.7 Flash39.1
  • Muse Spark Open38.9

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
    • Muse Spark Open: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Gemini 3.7 Flash: 62.0%Top
    • Muse Spark Open: 49.7%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Gemini 3.7 Flash: 30.0%Top
    • Muse Spark Open: 20.9%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Gemini 3.7 Flash: 1,113 Elo
    • Muse Spark Open: 1,279 EloTop

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Gemini 3.7 Flash: 1,396 Elo
    • Muse Spark Open: 1,401 EloTop

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Gemini 3.7 Flash: 23.6%Top
    • Muse Spark Open: 18.6%

  • AA-LCRv1.1

    Reasoning across long documents

    • Gemini 3.7 Flash: 81.7%Top
    • Muse Spark Open: 78.3%

  • MLCR-AA

    Long-context reasoning over medical records

    • Gemini 3.7 Flash: 12.2%Top
    • Muse Spark Open: 7.8%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Gemini 3.7 Flash: 47.9%Top
    • Muse Spark Open: 43.9%

  • GPQA Diamond

    Graduate-level science questions

    • Gemini 3.7 Flash: 94.5%Top
    • Muse Spark Open: 89.0%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Gemini 3.7 Flash: 13.6%Top
    • Muse Spark Open: 8.1%

  • SciCode

    Scientific computing in Python

    • Gemini 3.7 Flash: 57.2%
    • Muse Spark Open: 58.4%Top

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Gemini 3.7 Flash: 81.7%Top
    • Muse Spark Open: 78.3%

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.