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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 473 models

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TryMuse Spark OpenGrok 4.5DeepSeek 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

39.1

Tested at high reasoning effort

Better than 83% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    76.1

    Composite of the coding evaluations

    Better than 91% of 168 scored models

Agentic work

  • AutomationBench-AA

    62.0%

    Workflow automation, penalized for guardrail violations

    Better than 86% of 173 scored models

  • AutomationBench-AA tasks completed

    30.0%

    Workflows finished end to end without a guardrail violation

    Better than 84% of 173 scored models

  • AA-Briefcasev1.1

    1,113 Elo

    Agentic knowledge work, rated head to head

    Better than 53% of 166 scored models

  • GDPval-AAv2.1

    1,396 Elo

    Economically valuable occupational tasks, rated head to head

    Better than 64% of 187 scored models

Document reasoning

  • GDP.pdf

    23.6%

    Professional reasoning over PDF documents, all-pass rate

    Better than 84% of 171 scored models

  • AA-LCRv1.1

    81.7%

    Reasoning across long documents

    Better than 85% of 309 scored models

  • MLCR-AA

    12.2%

    Long-context reasoning over medical records

    Better than 47% of 121 scored models

Reasoning and knowledge

  • Humanity’s Last Exam

    47.9%

    Expert-level questions across academic fields

    Better than 92% of 323 scored models

  • GPQA Diamond

    94.5%

    Graduate-level science questions

    Better than 99% of 291 scored models

Coding

  • Terminal-Benchv4.0

    13.6%

    Practical software engineering in a terminal

    Better than 66% of 171 scored models

  • SciCode

    57.2%

    Scientific computing in Python

    Better than 82% of 174 scored models

Legacy evaluations

  • AA-LCR (unversioned)

    81.7%

    Long-context reasoning, earlier grading

    Better than 85% of 309 scored models

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
174.2 tok/s
Faster than 79% of 256 models
Time to first token
3.6 s
Quicker than 43% of 256 models
Prompt cache hits
7.7%
About $0.70 per 1M input tokens with caching
Request success (24h)
98.0%
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.