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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 modelMiniMax M1MiniMaxRemove
ProviderGoogleMiniMax
Discovery tierEfficientOpen weights
AddedJun 5, 2025Jan 8, 2025
Limits
Context window1.05MBest1M
Max output65.54K131.07KBest
Average output368 tokens—
Pricing, USD per 1M tokens
Input$0.30$0.14Best
Output$2.50$1.33Best
Cache read$0.03—
Performance
Throughput325.9 tok/s—
Time to first token4.4 s—
Request success (24h)99.7%—
Benchmark indexes
Intelligence Index9.910.0Best
Math Index60.3Best13.7
Capabilities
ReasoningYesNo
ToolsYesNo
VisionYesNo
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
  • MiniMax M1

Intelligence Index

  • Gemini 2.5 Flash9.9
  • MiniMax M110.0

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

3.8Catalog models by score55.1

Indexes

  • Math Index

    Composite of the math evaluations

    • Gemini 2.5 Flash: 60.3Top
    • MiniMax M1: 13.7

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Gemini 2.5 Flash: 49.9%
    • MiniMax M1: —

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Gemini 2.5 Flash: 4.7%
    • MiniMax M1: 7.8%Top

  • GPQA Diamond

    Graduate-level science questions

    • Gemini 2.5 Flash: 68.3%Top
    • MiniMax M1: 68.2%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Gemini 2.5 Flash: 80.9%Top
    • MiniMax M1: 80.8%

  • IFBench

    Following precise output instructions

    • Gemini 2.5 Flash: 39.0%
    • MiniMax M1: 41.2%Top

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • Gemini 2.5 Flash: 49.5%
    • MiniMax M1: 65.7%Top

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Gemini 2.5 Flash: 60.3%Top
    • MiniMax M1: 13.7%

  • AIME

    Competition mathematics

    • Gemini 2.5 Flash: 50.0%
    • MiniMax M1: 81.3%Top

  • MATH-500

    Competition math problems

    • Gemini 2.5 Flash: 93.2%
    • MiniMax M1: 97.2%Top

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Gemini 2.5 Flash: 12.1%Top
    • MiniMax M1: 2.3%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Gemini 2.5 Flash: 14.9%
    • MiniMax M1: 31.6%Top

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Gemini 2.5 Flash: 49.9%
    • MiniMax M1: —

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
46.6%
About $0.17 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.