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Gemini 2.5 Flash Lite Preview (09/2025) – Thinking

Added Sep 25, 2025

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Use in the app

Deprecated compatibility alias. Requests route to Gemini 2.5 Flash Lite with the stable thinking path.

  • 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) – Thinking 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) – ThinkingGoogleThis modelTrinity Large ThinkingarceeRemove
ProviderGooglearcee
Discovery tierEfficientOpen weights
AddedSep 25, 2025Apr 1, 2026
Limits
Context window1.05MBest262.14K
Max output65.54K80KBest
Average output—327 tokens
Pricing, USD per 1M tokens
Input$0.10Best$0.25
Output$0.40Best$0.90
Cache read$0.01—
Performance
Request success (24h)72.1%—
Benchmark indexes
Intelligence Index10.410.8Best
Coding Index—25.8
Math Index68.7—
Capabilities
ReasoningYesYes
ToolsYesYes
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) – Thinking
  • Trinity Large Thinking

Intelligence Index

  • Gemini 2.5 Flash Lite Preview (09/2025) – Thinking10.4
  • Trinity Large Thinking10.8

Gemini 2.5 Flash Lite Preview (09/2025) – Thinking: Better than 22% 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) – Thinking: —
    • Trinity Large Thinking: 25.8

  • Math Index

    Composite of the math evaluations

    • Gemini 2.5 Flash Lite Preview (09/2025) – Thinking: 68.7
    • Trinity Large Thinking: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Gemini 2.5 Flash Lite Preview (09/2025) – Thinking: —
    • Trinity Large Thinking: 1.3%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Gemini 2.5 Flash Lite Preview (09/2025) – Thinking: —
    • Trinity Large Thinking: 0.2%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Gemini 2.5 Flash Lite Preview (09/2025) – Thinking: —
    • Trinity Large Thinking: 255 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Gemini 2.5 Flash Lite Preview (09/2025) – Thinking: —
    • Trinity Large Thinking: 336 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Gemini 2.5 Flash Lite Preview (09/2025) – Thinking: —
    • Trinity Large Thinking: 1.2%

  • AA-LCRv1.1

    Reasoning across long documents

    • Gemini 2.5 Flash Lite Preview (09/2025) – Thinking: 64.7%Top
    • Trinity Large Thinking: 38.0%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Gemini 2.5 Flash Lite Preview (09/2025) – Thinking: 7.0%
    • Trinity Large Thinking: 15.8%Top

  • GPQA Diamond

    Graduate-level science questions

    • Gemini 2.5 Flash Lite Preview (09/2025) – Thinking: 70.9%
    • Trinity Large Thinking: 75.2%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Gemini 2.5 Flash Lite Preview (09/2025) – Thinking: 80.8%
    • Trinity Large Thinking: —

  • IFBench

    Following precise output instructions

    • Gemini 2.5 Flash Lite Preview (09/2025) – Thinking: 52.6%
    • Trinity Large Thinking: 56.3%Top

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Gemini 2.5 Flash Lite Preview (09/2025) – Thinking: —
    • Trinity Large Thinking: 0.5%

  • SciCode

    Scientific computing in Python

    • Gemini 2.5 Flash Lite Preview (09/2025) – Thinking: —
    • Trinity Large Thinking: 40.6%

  • LiveCodeBench

    Recent competitive programming problems

    • Gemini 2.5 Flash Lite Preview (09/2025) – Thinking: 68.8%
    • Trinity Large Thinking: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Gemini 2.5 Flash Lite Preview (09/2025) – Thinking: 68.7%
    • Trinity Large Thinking: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Gemini 2.5 Flash Lite Preview (09/2025) – Thinking: 12.9%
    • Trinity Large Thinking: 22.7%Top

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Gemini 2.5 Flash Lite Preview (09/2025) – Thinking: 30.7%
    • Trinity Large Thinking: 90.1%Top

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Gemini 2.5 Flash Lite Preview (09/2025) – Thinking: 64.7%Top
    • Trinity Large Thinking: 38.0%

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.

Request success (24h)
72.1%
Failing
Hourly request success · last 72 hoursLowest hour 33.9%
  • 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": "gemini-2.5-flash-lite-preview-09-2025-thinking",
    "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-thinking",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Google

Other Google models, starting with the same family.