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Google · Efficient

Gemini 3.5 Flash Lite

Added Jul 21, 2026

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Use in the app

Google's cost-efficient Gemini 3.5 Flash Lite model for high-volume multimodal reasoning, tool use, and structured-output workloads.

  • 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
$0.30
Output
$2.50
Cache read
$0.03
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)
178 tokens
Longer than 19% of 473 models

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TryGLM 4.7 Original ThinkingKimi K2 ThinkingOpenAI o3-pro (2025-06-10)All 3

Public evaluations

Benchmarks

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

Intelligence Index

22.2

Better than 53% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    49.3

    Composite of the coding evaluations

    Better than 45% of 168 scored models

Agentic work

  • AutomationBench-AA

    25.0%

    Workflow automation, penalized for guardrail violations

    Better than 47% of 173 scored models

  • AutomationBench-AA tasks completed

    3.3%

    Workflows finished end to end without a guardrail violation

    Better than 42% of 173 scored models

  • Harvey LAB-AAv1.1

    0.0%

    Legal tasks passed in full, with no material hallucination

    Better than 0% of 29 scored models

  • AA-Briefcasev1.1

    650 Elo

    Agentic knowledge work, rated head to head

    Better than 23% of 166 scored models

  • GDPval-AAv2.1

    985 Elo

    Economically valuable occupational tasks, rated head to head

    Better than 33% of 187 scored models

Document reasoning

  • GDP.pdf

    13.6%

    Professional reasoning over PDF documents, all-pass rate

    Better than 61% of 171 scored models

  • AA-LCRv1.1

    76.0%

    Reasoning across long documents

    Better than 62% of 309 scored models

  • MLCR-AA

    10.6%

    Long-context reasoning over medical records

    Better than 36% of 121 scored models

Reasoning and knowledge

  • Humanity’s Last Exam

    18.8%

    Expert-level questions across academic fields

    Better than 45% of 323 scored models

  • GPQA Diamond

    83.8%

    Graduate-level science questions

    Better than 55% of 291 scored models

Coding

  • Terminal-Benchv4.0

    1.0%

    Practical software engineering in a terminal

    Better than 37% of 171 scored models

  • SciCode

    41.3%

    Scientific computing in Python

    Better than 24% of 174 scored models

Legacy evaluations

  • AA-LCR (unversioned)

    76.0%

    Long-context reasoning, earlier grading

    Better than 62% of 309 scored models

Scores from Artificial Analysis, updated Oct 26, 2026. 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
182.7 tok/s
Faster than 81% of 256 models
Time to first token
1.1 s
Quicker than 94% of 256 models
Prompt cache hits
53.9%
About $0.15 per 1M input tokens with caching
Request success (24h)
99.9%
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 94.7%
  • 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-lite",
    "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-lite",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Google

Other Google models, starting with the same family.