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Google · Frontier

Gemini 2.5 Pro

Added Jun 5, 2025

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Use in the app

Gemini 2.5 Pro stable release. Google's most capable generalist model with strong performance across a wide range of tasks.

  • Reasoning
  • Tools
  • Vision
  • Audio input
  • Video input
Input
Text, Images, Video, Audio
Output
Text
Reasoning effort
low · medium · high
Discovery tier
Frontier

Pricing

USD per 1M tokens
Input
$1.25
Output
$10.00
Cache read
$0.13
Cache write
$0.38

Reference prices as published by the model provider. API example

Specifications

Context window
1M
Max output
65.5K
Average output (7 days)
1.1K tokens
Longer than 72% of 465 models

Side by side

Gemini 2.5 Pro 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 ProGoogleThis modelQwen3 MaxQwenRemove
ProviderGoogleQwen
Discovery tierFrontierBalanced
AddedJun 5, 2025Sep 5, 2025
Limits
Context window1.05MBest256K
Max output65.54KBest32.77K
Average output1.1K tokens—
Pricing, USD per 1M tokens
Input$1.25$1.20Best
Output$10.00$6.00Best
Cache read$0.13—
Cache write$0.38—
Performance
Throughput189.7 tok/s—
Time to first token12.3 s—
Request success (24h)99.5%—
Benchmark indexes
Intelligence Index16.1Best15.6
Coding Index33.3—
Math Index87.7Best80.7
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 Pro
  • Qwen3 Max

Intelligence Index

  • Gemini 2.5 Pro16.1
  • Qwen3 Max15.6

Gemini 2.5 Pro: Better than 41% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Gemini 2.5 Pro: 33.3
    • Qwen3 Max: —

  • Math Index

    Composite of the math evaluations

    • Gemini 2.5 Pro: 87.7Top
    • Qwen3 Max: 80.7

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Gemini 2.5 Pro: 2.2%
    • Qwen3 Max: —

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Gemini 2.5 Pro: 0.0%
    • Qwen3 Max: —

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Gemini 2.5 Pro: 302 Elo
    • Qwen3 Max: —

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Gemini 2.5 Pro: 455 Elo
    • Qwen3 Max: —

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Gemini 2.5 Pro: 10.2%
    • Qwen3 Max: —

  • AA-LCRv1.1

    Reasoning across long documents

    • Gemini 2.5 Pro: 69.0%Top
    • Qwen3 Max: 50.0%

  • MLCR-AA

    Long-context reasoning over medical records

    • Gemini 2.5 Pro: 12.2%
    • Qwen3 Max: —

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Gemini 2.5 Pro: 22.5%Top
    • Qwen3 Max: 11.9%

  • GPQA Diamond

    Graduate-level science questions

    • Gemini 2.5 Pro: 84.4%Top
    • Qwen3 Max: 76.4%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Gemini 2.5 Pro: 86.2%Top
    • Qwen3 Max: 84.1%

  • IFBench

    Following precise output instructions

    • Gemini 2.5 Pro: 48.7%Top
    • Qwen3 Max: 44.1%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Gemini 2.5 Pro: 0.0%
    • Qwen3 Max: —

  • SciCode

    Scientific computing in Python

    • Gemini 2.5 Pro: 46.3%
    • Qwen3 Max: —

  • LiveCodeBench

    Recent competitive programming problems

    • Gemini 2.5 Pro: 80.1%Top
    • Qwen3 Max: 76.7%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Gemini 2.5 Pro: 87.7%Top
    • Qwen3 Max: 80.7%

  • AIME

    Competition mathematics

    • Gemini 2.5 Pro: 88.7%
    • Qwen3 Max: —

  • MATH-500

    Competition math problems

    • Gemini 2.5 Pro: 96.7%
    • Qwen3 Max: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Gemini 2.5 Pro: 26.5%Top
    • Qwen3 Max: 20.5%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Gemini 2.5 Pro: 54.1%
    • Qwen3 Max: 74.3%Top

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Gemini 2.5 Pro: 69.0%Top
    • Qwen3 Max: 50.0%

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.

Throughput
189.7 tok/s
Faster than 84% of 250 models
Time to first token
12.3 s
Quicker than 7% of 250 models
Prompt cache hits
79.6%
About $0.35 per 1M input tokens with caching
Request success (24h)
99.5%
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 96.6%
  • 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-pro",
    "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-pro",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Google

Other Google models, starting with the same family.