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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 modelDeepSeek Chat 0324DeepSeekRemove
ProviderGoogleDeepSeek
Discovery tierEfficientOpen weights
AddedJun 5, 2025Jan 1, 2024
Limits
Context window1.05MBest128K
Max output65.54KBest8.19K
Average output368 tokens280 tokens
Pricing, USD per 1M tokens
Input$0.30$0.20Best
Output$2.50$0.77Best
Cache read$0.03Best$0.14
Performance
Throughput325.9 tok/sBest26.6 tok/s
Time to first token4.4 s2.7 sBest
Request success (24h)99.7%99.9%Best
Benchmark indexes
Intelligence Index9.9Best9.7
Coding Index—21.2
Math Index60.3Best41.0
Capabilities
ReasoningYesNo
ToolsYesYes
VisionYesNo
Structured outputNoYes
Audio inputYesNo
Video 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
  • DeepSeek Chat 0324

Intelligence Index

  • Gemini 2.5 Flash9.9
  • DeepSeek Chat 03249.7

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: —
    • DeepSeek Chat 0324: 21.2

  • Math Index

    Composite of the math evaluations

    • Gemini 2.5 Flash: 60.3Top
    • DeepSeek Chat 0324: 41.0

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Gemini 2.5 Flash: —
    • DeepSeek Chat 0324: 0.3%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Gemini 2.5 Flash: —
    • DeepSeek Chat 0324: 0.0%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Gemini 2.5 Flash: —
    • DeepSeek Chat 0324: 68 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Gemini 2.5 Flash: —
    • DeepSeek Chat 0324: 69 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Gemini 2.5 Flash: —
    • DeepSeek Chat 0324: 1.6%

  • AA-LCRv1.1

    Reasoning across long documents

    • Gemini 2.5 Flash: 49.9%Top
    • DeepSeek Chat 0324: 40.7%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Gemini 2.5 Flash: 4.7%Top
    • DeepSeek Chat 0324: 4.7%Top

  • GPQA Diamond

    Graduate-level science questions

    • Gemini 2.5 Flash: 68.3%Top
    • DeepSeek Chat 0324: 65.5%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Gemini 2.5 Flash: 80.9%
    • DeepSeek Chat 0324: 81.9%Top

  • IFBench

    Following precise output instructions

    • Gemini 2.5 Flash: 39.0%
    • DeepSeek Chat 0324: 41.0%Top

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Gemini 2.5 Flash: —
    • DeepSeek Chat 0324: 0.0%

  • SciCode

    Scientific computing in Python

    • Gemini 2.5 Flash: —
    • DeepSeek Chat 0324: 39.0%

  • LiveCodeBench

    Recent competitive programming problems

    • Gemini 2.5 Flash: 49.5%Top
    • DeepSeek Chat 0324: 40.5%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Gemini 2.5 Flash: 60.3%Top
    • DeepSeek Chat 0324: 41.0%

  • AIME

    Competition mathematics

    • Gemini 2.5 Flash: 50.0%
    • DeepSeek Chat 0324: 52.0%Top

  • MATH-500

    Competition math problems

    • Gemini 2.5 Flash: 93.2%
    • DeepSeek Chat 0324: 94.2%Top

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Gemini 2.5 Flash: 12.1%
    • DeepSeek Chat 0324: 15.2%Top

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Gemini 2.5 Flash: 14.9%
    • DeepSeek Chat 0324: 47.1%Top

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Gemini 2.5 Flash: 49.9%Top
    • DeepSeek Chat 0324: 40.7%

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.

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.