Google · Balanced
Gemini 2.5 Pro Preview 0605
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.
| Gemini 2.5 Pro Preview 0605GoogleThis model | Gemini 2.5 Pro Preview 0325GoogleRemove | |
|---|---|---|
| Provider | ||
| Discovery tier | Balanced | Balanced |
| Added | Jun 5, 2025 | Mar 25, 2025 |
| Limits | ||
| Context window | 1.05M | 1.05M |
| Max output | 65.54K | 65.54K |
| Average output | 727 tokens | 1.3K tokens |
| Pricing, USD per 1M tokens | ||
| Input | $2.50 | $2.50 |
| Output | $10.00 | $10.00 |
| Cache read | $0.25 | $0.25 |
| Performance | ||
| Request success (24h) | 99.5% | — |
| Benchmark indexes | ||
| Intelligence Index | 16.1 | 16.1 |
| Coding Index | 33.3 | 33.3 |
| Math Index | 87.7 | 87.7 |
| Capabilities | ||
| Reasoning | Yes | Yes |
| Vision | Yes | Yes |
| Audio input | Yes | Yes |
| Reasoning effort | low, medium, high | low, medium, 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
- Gemini 2.5 Pro Preview 0325
Intelligence Index
- Gemini 2.5 Pro Preview 060516.1
- Gemini 2.5 Pro Preview 032516.1
Gemini 2.5 Pro Preview 0605: Better than 41% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- Gemini 2.5 Pro Preview 0605: 33.3Top
- Gemini 2.5 Pro Preview 0325: 33.3Top
Math Index
Composite of the math evaluations
- Gemini 2.5 Pro Preview 0605: 87.7Top
- Gemini 2.5 Pro Preview 0325: 87.7Top
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- Gemini 2.5 Pro Preview 0605: 2.2%Top
- Gemini 2.5 Pro Preview 0325: 2.2%Top
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- Gemini 2.5 Pro Preview 0605: 0.0%Top
- Gemini 2.5 Pro Preview 0325: 0.0%Top
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- Gemini 2.5 Pro Preview 0605: 302 EloTop
- Gemini 2.5 Pro Preview 0325: 302 EloTop
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- Gemini 2.5 Pro Preview 0605: 455 EloTop
- Gemini 2.5 Pro Preview 0325: 455 EloTop
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- Gemini 2.5 Pro Preview 0605: 10.2%Top
- Gemini 2.5 Pro Preview 0325: 10.2%Top
AA-LCRv1.1
Reasoning across long documents
- Gemini 2.5 Pro Preview 0605: 69.0%Top
- Gemini 2.5 Pro Preview 0325: 69.0%Top
MLCR-AA
Long-context reasoning over medical records
- Gemini 2.5 Pro Preview 0605: 12.2%Top
- Gemini 2.5 Pro Preview 0325: 12.2%Top
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Gemini 2.5 Pro Preview 0605: 22.5%Top
- Gemini 2.5 Pro Preview 0325: 22.5%Top
GPQA Diamond
Graduate-level science questions
- Gemini 2.5 Pro Preview 0605: 84.4%Top
- Gemini 2.5 Pro Preview 0325: 84.4%Top
MMLU-Pro
Multi-subject knowledge and reasoning
- Gemini 2.5 Pro Preview 0605: 86.2%Top
- Gemini 2.5 Pro Preview 0325: 86.2%Top
IFBench
Following precise output instructions
- Gemini 2.5 Pro Preview 0605: 48.7%Top
- Gemini 2.5 Pro Preview 0325: 48.7%Top
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- Gemini 2.5 Pro Preview 0605: 0.0%Top
- Gemini 2.5 Pro Preview 0325: 0.0%Top
SciCode
Scientific computing in Python
- Gemini 2.5 Pro Preview 0605: 46.3%Top
- Gemini 2.5 Pro Preview 0325: 46.3%Top
LiveCodeBench
Recent competitive programming problems
- Gemini 2.5 Pro Preview 0605: 80.1%Top
- Gemini 2.5 Pro Preview 0325: 80.1%Top
Math
AIME 2025
Competition mathematics, 2025 problems
- Gemini 2.5 Pro Preview 0605: 87.7%Top
- Gemini 2.5 Pro Preview 0325: 87.7%Top
AIME
Competition mathematics
- Gemini 2.5 Pro Preview 0605: 88.7%Top
- Gemini 2.5 Pro Preview 0325: 88.7%Top
MATH-500
Competition math problems
- Gemini 2.5 Pro Preview 0605: 96.7%Top
- Gemini 2.5 Pro Preview 0325: 96.7%Top
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- Gemini 2.5 Pro Preview 0605: 26.5%Top
- Gemini 2.5 Pro Preview 0325: 26.5%Top
τ²-Bench Telecom
Tool use in a customer-support simulation
- Gemini 2.5 Pro Preview 0605: 54.1%Top
- Gemini 2.5 Pro Preview 0325: 54.1%Top
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Gemini 2.5 Pro Preview 0605: 69.0%Top
- Gemini 2.5 Pro Preview 0325: 69.0%Top
Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis
- Gemini 2.5 Pro Preview 0605
- Gemini 2.5 Pro Preview 0325
| Leaderboard | Gemini 2.5 Pro Preview 0605 | Gemini 2.5 Pro Preview 0325 |
|---|---|---|
| EQ-Bench 3 | #17 · 1523 | Not in top 20 |
Ranks among Noumen catalog models on EQ-Bench leaderboards. Only top-20 placements are listed.EQ-Bench
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.
- 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.
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)Related models
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