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Google · Efficient

Gemini 2.5 Flash

Added Jun 5, 2025

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Use in the app

Fast, cost efficient performance on complex tasks. The workhorse of the Gemini series. Stable release with improved capabilities. Audio input costs $1.00 per million audio tokens.

  • Reasoning
  • Tools
  • Vision
  • Audio input
  • Video input
Input
Text, Images, Video, 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
Average output (7 days)
368 tokens
Longer than 38% of 468 models

Side by side

Gemini 2.5 Flash 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 FlashGoogleThis modelGemini 3.5 Flash ThinkingGoogleRemove
ProviderGoogleGoogle
Discovery tierEfficientEfficient
AddedJun 5, 2025May 19, 2026
Limits
Context window1.05M1.05M
Max output65.54K65.54K
Average output368 tokens1.7K tokens
Pricing, USD per 1M tokens
Input$0.30Best$1.50
Output$2.50Best$9.00
Cache read$0.03Best$0.15
Cache write—$0.0833
Performance
Throughput325.9 tok/sBest174.1 tok/s
Time to first token4.4 sBest9.4 s
Request success (24h)99.7%Best89.1%
Benchmark indexes
Intelligence Index9.932.6Best
Coding Index—70.1
Math Index60.3—
Capabilities
ReasoningYesYes
ToolsYesNo
VisionYesYes
Audio inputYesYes
Video inputYesYes
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
  • Gemini 3.5 Flash Thinking

Intelligence Index

  • Gemini 2.5 Flash9.9
  • Gemini 3.5 Flash Thinking32.6

Gemini 2.5 Flash: Better than 20% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Gemini 2.5 Flash: —
    • Gemini 3.5 Flash Thinking: 70.1

  • Math Index

    Composite of the math evaluations

    • Gemini 2.5 Flash: 60.3
    • Gemini 3.5 Flash Thinking: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Gemini 2.5 Flash: —
    • Gemini 3.5 Flash Thinking: 42.1%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Gemini 2.5 Flash: —
    • Gemini 3.5 Flash Thinking: 13.5%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Gemini 2.5 Flash: —
    • Gemini 3.5 Flash Thinking: 874 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Gemini 2.5 Flash: —
    • Gemini 3.5 Flash Thinking: 1,202 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Gemini 2.5 Flash: —
    • Gemini 3.5 Flash Thinking: 19.8%

  • AA-LCRv1.1

    Reasoning across long documents

    • Gemini 2.5 Flash: 49.9%
    • Gemini 3.5 Flash Thinking: 73.3%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • Gemini 2.5 Flash: —
    • Gemini 3.5 Flash Thinking: 10.0%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Gemini 2.5 Flash: 4.7%
    • Gemini 3.5 Flash Thinking: 42.7%Top

  • GPQA Diamond

    Graduate-level science questions

    • Gemini 2.5 Flash: 68.3%
    • Gemini 3.5 Flash Thinking: 92.2%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Gemini 2.5 Flash: 80.9%
    • Gemini 3.5 Flash Thinking: —

  • IFBench

    Following precise output instructions

    • Gemini 2.5 Flash: 39.0%
    • Gemini 3.5 Flash Thinking: 76.3%Top

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Gemini 2.5 Flash: —
    • Gemini 3.5 Flash Thinking: 6.6%

  • SciCode

    Scientific computing in Python

    • Gemini 2.5 Flash: —
    • Gemini 3.5 Flash Thinking: 53.9%

  • LiveCodeBench

    Recent competitive programming problems

    • Gemini 2.5 Flash: 49.5%
    • Gemini 3.5 Flash Thinking: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Gemini 2.5 Flash: 60.3%
    • Gemini 3.5 Flash Thinking: —

  • AIME

    Competition mathematics

    • Gemini 2.5 Flash: 50.0%
    • Gemini 3.5 Flash Thinking: —

  • MATH-500

    Competition math problems

    • Gemini 2.5 Flash: 93.2%
    • Gemini 3.5 Flash Thinking: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Gemini 2.5 Flash: 12.1%
    • Gemini 3.5 Flash Thinking: 40.9%Top

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Gemini 2.5 Flash: 14.9%
    • Gemini 3.5 Flash Thinking: 95.3%Top

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Gemini 2.5 Flash: 49.9%
    • Gemini 3.5 Flash Thinking: 73.3%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.

Throughput
325.9 tok/s
Faster than 95% of 259 models
Time to first token
4.4 s
Quicker than 38% of 259 models
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",
    "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",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Google

Other Google models, starting with the same family.