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Gemini 2.5 Pro Preview 0605

Added Jun 5, 2025

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Use in the app

Note: This preview is mapped to Gemini 2.5 Pro (stable).

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

Pricing

USD per 1M tokens
Input
$2.50
Output
$10.00
Cache read
$0.25

Reference prices as published by the model provider. API example

Specifications

Context window
1M
Max output
65.5K
Average output (7 days)
727 tokens
Longer than 62% of 465 models

Side by side

Gemini 2.5 Pro Preview 0605 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 Pro Preview 0605GoogleThis modelGemma 4 12B Starry OceanGoogleRemove
ProviderGoogleGoogle
Discovery tierBalancedOpen weights
AddedJun 5, 2025Oct 15, 2026
Limits
Context window1.05MBest131.07K
Max output65.54K—
Average output727 tokens—
Pricing, USD per 1M tokens
Input$2.50$0.05Best
Output$10.00$0.25Best
Cache read$0.25$0.025Best
Performance
Request success (24h)99.5%Best99.4%
Benchmark indexes
Intelligence Index16.1—
Coding Index33.3—
Math Index87.7—
Capabilities
ReasoningYesYes
ToolsNoYes
VisionYesYes
Audio inputYesNo
Reasoning effortlow, medium, highnone, high

Public evaluations

Benchmarks

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

  • Gemini 2.5 Pro Preview 0605
  • Gemma 4 12B Starry Ocean

Intelligence Index

  • Gemini 2.5 Pro Preview 060516.1
  • Gemma 4 12B Starry Ocean—

Gemini 2.5 Pro Preview 0605: 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 Preview 0605: 33.3
    • Gemma 4 12B Starry Ocean: —

  • Math Index

    Composite of the math evaluations

    • Gemini 2.5 Pro Preview 0605: 87.7
    • Gemma 4 12B Starry Ocean: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Gemini 2.5 Pro Preview 0605: 2.2%
    • Gemma 4 12B Starry Ocean: —

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Gemini 2.5 Pro Preview 0605: 0.0%
    • Gemma 4 12B Starry Ocean: —

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Gemini 2.5 Pro Preview 0605: 302 Elo
    • Gemma 4 12B Starry Ocean: —

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Gemini 2.5 Pro Preview 0605: 455 Elo
    • Gemma 4 12B Starry Ocean: —

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Gemini 2.5 Pro Preview 0605: 10.2%
    • Gemma 4 12B Starry Ocean: —

  • AA-LCRv1.1

    Reasoning across long documents

    • Gemini 2.5 Pro Preview 0605: 69.0%
    • Gemma 4 12B Starry Ocean: —

  • MLCR-AA

    Long-context reasoning over medical records

    • Gemini 2.5 Pro Preview 0605: 12.2%
    • Gemma 4 12B Starry Ocean: —

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Gemini 2.5 Pro Preview 0605: 22.5%
    • Gemma 4 12B Starry Ocean: —

  • GPQA Diamond

    Graduate-level science questions

    • Gemini 2.5 Pro Preview 0605: 84.4%
    • Gemma 4 12B Starry Ocean: —

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Gemini 2.5 Pro Preview 0605: 86.2%
    • Gemma 4 12B Starry Ocean: —

  • IFBench

    Following precise output instructions

    • Gemini 2.5 Pro Preview 0605: 48.7%
    • Gemma 4 12B Starry Ocean: —

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Gemini 2.5 Pro Preview 0605: 0.0%
    • Gemma 4 12B Starry Ocean: —

  • SciCode

    Scientific computing in Python

    • Gemini 2.5 Pro Preview 0605: 46.3%
    • Gemma 4 12B Starry Ocean: —

  • LiveCodeBench

    Recent competitive programming problems

    • Gemini 2.5 Pro Preview 0605: 80.1%
    • Gemma 4 12B Starry Ocean: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Gemini 2.5 Pro Preview 0605: 87.7%
    • Gemma 4 12B Starry Ocean: —

  • AIME

    Competition mathematics

    • Gemini 2.5 Pro Preview 0605: 88.7%
    • Gemma 4 12B Starry Ocean: —

  • MATH-500

    Competition math problems

    • Gemini 2.5 Pro Preview 0605: 96.7%
    • Gemma 4 12B Starry Ocean: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Gemini 2.5 Pro Preview 0605: 26.5%
    • Gemma 4 12B Starry Ocean: —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Gemini 2.5 Pro Preview 0605: 54.1%
    • Gemma 4 12B Starry Ocean: —

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Gemini 2.5 Pro Preview 0605: 69.0%
    • Gemma 4 12B Starry Ocean: —

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
56.0%
About $1.24 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-preview-06-05",
    "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-preview-06-05",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Google

Other Google models, starting with the same family.