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Google · Efficient

Gemini 2.5 Flash Lite Preview (09/2025)

Added Sep 25, 2025

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Use in the app

Deprecated compatibility alias. Requests route to the stable Gemini 2.5 Flash Lite model.

  • Reasoning
  • Tools
  • Vision
  • Structured output
  • PDF input
  • Audio input
Input
Text, Images, Audio
Output
Text
Reasoning effort
low · medium · high
Discovery tier
Efficient

Pricing

USD per 1M tokens
Input
$0.10
Output
$0.40
Cache read
$0.01

Reference prices as published by the model provider. API example

Specifications

Context window
1M
Max output
65.5K

Side by side

Gemini 2.5 Flash Lite Preview (09/2025) compared

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

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Gemini 2.5 Flash Lite Preview (09/2025)GoogleThis modelMistral Medium 3.1MistralRemove
ProviderGoogleMistral
Discovery tierEfficientBalanced
AddedSep 25, 2025Sep 5, 2025
Limits
Context window1.05MBest131.07K
Max output65.54KBest32.77K
Average output—60 tokens
Pricing, USD per 1M tokens
Input$0.10Best$0.40
Output$0.40Best$2.00
Cache read$0.01—
Performance
Request success (24h)99.5%100.0%Best
Benchmark indexes
Intelligence Index9.3Best9.2
Coding Index—20.5
Math Index46.7Best38.3
Capabilities
ReasoningYesNo
ToolsYesNo
VisionYesNo
Structured outputYesNo
PDF inputYesNo
Audio inputYesNo
Reasoning effortlow, medium, high—

Public evaluations

Benchmarks

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

  • Gemini 2.5 Flash Lite Preview (09/2025)
  • Mistral Medium 3.1

Intelligence Index

  • Gemini 2.5 Flash Lite Preview (09/2025)9.3
  • Mistral Medium 3.19.2

Gemini 2.5 Flash Lite Preview (09/2025): Better than 18% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Gemini 2.5 Flash Lite Preview (09/2025): —
    • Mistral Medium 3.1: 20.5

  • Math Index

    Composite of the math evaluations

    • Gemini 2.5 Flash Lite Preview (09/2025): 46.7Top
    • Mistral Medium 3.1: 38.3

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Gemini 2.5 Flash Lite Preview (09/2025): —
    • Mistral Medium 3.1: 4.1%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Gemini 2.5 Flash Lite Preview (09/2025): —
    • Mistral Medium 3.1: 0.2%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Gemini 2.5 Flash Lite Preview (09/2025): —
    • Mistral Medium 3.1: 530 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Gemini 2.5 Flash Lite Preview (09/2025): —
    • Mistral Medium 3.1: 385 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Gemini 2.5 Flash Lite Preview (09/2025): —
    • Mistral Medium 3.1: 1.2%

  • AA-LCRv1.1

    Reasoning across long documents

    • Gemini 2.5 Flash Lite Preview (09/2025): 49.7%Top
    • Mistral Medium 3.1: 42.7%

  • MLCR-AA

    Long-context reasoning over medical records

    • Gemini 2.5 Flash Lite Preview (09/2025): —
    • Mistral Medium 3.1: 2.8%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Gemini 2.5 Flash Lite Preview (09/2025): 5.1%Top
    • Mistral Medium 3.1: 4.7%

  • GPQA Diamond

    Graduate-level science questions

    • Gemini 2.5 Flash Lite Preview (09/2025): 65.1%Top
    • Mistral Medium 3.1: 58.8%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Gemini 2.5 Flash Lite Preview (09/2025): 79.6%Top
    • Mistral Medium 3.1: 68.3%

  • IFBench

    Following precise output instructions

    • Gemini 2.5 Flash Lite Preview (09/2025): 41.8%Top
    • Mistral Medium 3.1: 39.8%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Gemini 2.5 Flash Lite Preview (09/2025): —
    • Mistral Medium 3.1: 0.0%

  • SciCode

    Scientific computing in Python

    • Gemini 2.5 Flash Lite Preview (09/2025): —
    • Mistral Medium 3.1: 32.4%

  • LiveCodeBench

    Recent competitive programming problems

    • Gemini 2.5 Flash Lite Preview (09/2025): 64.1%Top
    • Mistral Medium 3.1: 40.6%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Gemini 2.5 Flash Lite Preview (09/2025): 46.7%Top
    • Mistral Medium 3.1: 38.3%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Gemini 2.5 Flash Lite Preview (09/2025): 7.6%
    • Mistral Medium 3.1: 10.6%Top

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Gemini 2.5 Flash Lite Preview (09/2025): 30.4%
    • Mistral Medium 3.1: 40.6%Top

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Gemini 2.5 Flash Lite Preview (09/2025): 49.7%Top
    • Mistral Medium 3.1: 42.7%

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.

Prompt cache hits
0.0%
About $0.10 per 1M input tokens with caching
Request success (24h)
99.5%
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 76.0%
  • 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": "gemini-2.5-flash-lite-preview-09-2025",
    "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="gemini-2.5-flash-lite-preview-09-2025",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Google

Other Google models, starting with the same family.