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Google · Efficient

Gemini 2.5 Flash Preview (09/2025)

Added Sep 25, 2025

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Use in the app

State-of-the-art Gemini 2.5 Flash checkpoint tuned for advanced reasoning, coding, math, and scientific tasks. Built-in thinking for higher accuracy and nuanced context handling. Routed to Gemini 2.5 Flash (stable).

  • 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
$0.30
Output
$2.50
Cache read
$0.03

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 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 Preview (09/2025)GoogleThis modelGemini 3.8 FlashGoogleRemove
ProviderGoogleGoogle
Discovery tierEfficientEfficient
AddedSep 25, 2025Sep 2, 2026
Limits
Context window1.05M1.05M
Max output65.54K65.54K
Average output—1.2K tokens
Pricing, USD per 1M tokens
Input$0.30Best$0.75
Output$2.50Best$3.75
Cache read$0.03Best$0.075
Cache write—$0.0417
Performance
Throughput—185.2 tok/s
Time to first token—8.5 s
Request success (24h)99.7%99.9%Best
Benchmark indexes
Intelligence Index12.439.8Best
Coding Index—74.1
Math Index56.7—
Capabilities
ReasoningYesYes
ToolsYesYes
VisionYesYes
Structured outputYesYes
PDF inputYesYes
Audio inputYesYes
Video inputNoYes
Reasoning effortlow, medium, highminimal, low, medium, high

Public evaluations

Benchmarks

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

  • Gemini 2.5 Flash Preview (09/2025)
  • Gemini 3.8 Flash

Intelligence Index

  • Gemini 2.5 Flash Preview (09/2025)12.4
  • Gemini 3.8 Flash39.8

Gemini 2.5 Flash Preview (09/2025): Better than 26% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Gemini 2.5 Flash Preview (09/2025): —
    • Gemini 3.8 Flash: 74.1

  • Math Index

    Composite of the math evaluations

    • Gemini 2.5 Flash Preview (09/2025): 56.7
    • Gemini 3.8 Flash: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Gemini 2.5 Flash Preview (09/2025): —
    • Gemini 3.8 Flash: 60.9%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Gemini 2.5 Flash Preview (09/2025): —
    • Gemini 3.8 Flash: 31.1%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Gemini 2.5 Flash Preview (09/2025): —
    • Gemini 3.8 Flash: 1,181 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Gemini 2.5 Flash Preview (09/2025): —
    • Gemini 3.8 Flash: 1,423 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Gemini 2.5 Flash Preview (09/2025): —
    • Gemini 3.8 Flash: 22.8%

  • AA-LCRv1.1

    Reasoning across long documents

    • Gemini 2.5 Flash Preview (09/2025): 60.0%
    • Gemini 3.8 Flash: 84.0%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • Gemini 2.5 Flash Preview (09/2025): —
    • Gemini 3.8 Flash: 12.8%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Gemini 2.5 Flash Preview (09/2025): 8.7%
    • Gemini 3.8 Flash: 42.1%Top

  • GPQA Diamond

    Graduate-level science questions

    • Gemini 2.5 Flash Preview (09/2025): 76.6%
    • Gemini 3.8 Flash: 93.5%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Gemini 2.5 Flash Preview (09/2025): 83.6%
    • Gemini 3.8 Flash: —

  • IFBench

    Following precise output instructions

    • Gemini 2.5 Flash Preview (09/2025): 43.5%
    • Gemini 3.8 Flash: —

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Gemini 2.5 Flash Preview (09/2025): —
    • Gemini 3.8 Flash: 19.7%

  • SciCode

    Scientific computing in Python

    • Gemini 2.5 Flash Preview (09/2025): —
    • Gemini 3.8 Flash: 55.1%

  • LiveCodeBench

    Recent competitive programming problems

    • Gemini 2.5 Flash Preview (09/2025): 62.5%
    • Gemini 3.8 Flash: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Gemini 2.5 Flash Preview (09/2025): 56.7%
    • Gemini 3.8 Flash: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Gemini 2.5 Flash Preview (09/2025): 14.4%
    • Gemini 3.8 Flash: —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Gemini 2.5 Flash Preview (09/2025): 28.4%
    • Gemini 3.8 Flash: —

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Gemini 2.5 Flash Preview (09/2025): 60.0%
    • Gemini 3.8 Flash: 84.0%Top

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
45.3%
About $0.18 per 1M input tokens with caching
Request success (24h)
99.7%
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 80.0%
  • 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-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-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.