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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 465 models

Side by side

Gemini 3.5 Flash Lite compared

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

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Gemini 3.5 Flash LiteGoogleThis modelKimi K2 ThinkingMoonshotRemove
ProviderGoogleMoonshot
Discovery tierEfficientOpen weights
AddedJul 21, 2026Nov 6, 2025
Limits
Context window1.05MBest262.14K
Max output65.54K98.3KBest
Average output178 tokens1.9K tokens
Pricing, USD per 1M tokens
Input$0.30Best$0.60
Output$2.50$2.50
Cache read$0.03Best$0.15
Cache write$0.0833—
Performance
Throughput237.4 tok/sBest48.0 tok/s
Time to first token1.4 sBest2.6 s
Request success (24h)99.9%Best99.6%
Benchmark indexes
Intelligence Index22.2Best22.0
Coding Index49.3—
Math Index—94.7
Capabilities
ReasoningYesYes
ToolsYesYes
VisionYesNo
Structured outputYesYes
PDF inputYesNo
Audio inputYesNo
Video inputYesNo
Reasoning effortminimal, low, medium, high—

Public evaluations

Benchmarks

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

  • Gemini 3.5 Flash Lite
  • Kimi K2 Thinking

Intelligence Index

  • Gemini 3.5 Flash Lite22.2
  • Kimi K2 Thinking22.0

Gemini 3.5 Flash Lite: Better than 53% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Gemini 3.5 Flash Lite: 49.3
    • Kimi K2 Thinking: —

  • Math Index

    Composite of the math evaluations

    • Gemini 3.5 Flash Lite: —
    • Kimi K2 Thinking: 94.7

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Gemini 3.5 Flash Lite: 25.0%
    • Kimi K2 Thinking: —

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Gemini 3.5 Flash Lite: 3.3%
    • Kimi K2 Thinking: —

  • Harvey LAB-AAv1.1

    Legal tasks passed in full, with no material hallucination

    • Gemini 3.5 Flash Lite: 0.0%
    • Kimi K2 Thinking: —

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Gemini 3.5 Flash Lite: 650 Elo
    • Kimi K2 Thinking: —

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Gemini 3.5 Flash Lite: 985 Elo
    • Kimi K2 Thinking: —

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Gemini 3.5 Flash Lite: 13.6%
    • Kimi K2 Thinking: —

  • AA-LCRv1.1

    Reasoning across long documents

    • Gemini 3.5 Flash Lite: 76.0%Top
    • Kimi K2 Thinking: 72.0%

  • MLCR-AA

    Long-context reasoning over medical records

    • Gemini 3.5 Flash Lite: 10.6%
    • Kimi K2 Thinking: —

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Gemini 3.5 Flash Lite: 18.8%
    • Kimi K2 Thinking: 23.8%Top

  • GPQA Diamond

    Graduate-level science questions

    • Gemini 3.5 Flash Lite: 83.8%Top
    • Kimi K2 Thinking: 83.8%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Gemini 3.5 Flash Lite: —
    • Kimi K2 Thinking: 84.8%

  • IFBench

    Following precise output instructions

    • Gemini 3.5 Flash Lite: —
    • Kimi K2 Thinking: 68.1%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Gemini 3.5 Flash Lite: 1.0%
    • Kimi K2 Thinking: —

  • SciCode

    Scientific computing in Python

    • Gemini 3.5 Flash Lite: 41.3%
    • Kimi K2 Thinking: —

  • LiveCodeBench

    Recent competitive programming problems

    • Gemini 3.5 Flash Lite: —
    • Kimi K2 Thinking: 85.3%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Gemini 3.5 Flash Lite: —
    • Kimi K2 Thinking: 94.7%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Gemini 3.5 Flash Lite: —
    • Kimi K2 Thinking: 31.1%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Gemini 3.5 Flash Lite: —
    • Kimi K2 Thinking: 93.0%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Gemini 3.5 Flash Lite: 76.0%Top
    • Kimi K2 Thinking: 72.0%

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
237.4 tok/s
Faster than 89% of 250 models
Time to first token
1.4 s
Quicker than 88% of 250 models
Prompt cache hits
21.4%
About $0.24 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.