Google · Balanced
Gemini 3 Pro Image
Gemini 3 Pro with native image generation support. Can generate images inline from text prompts.
- Vision
- Input
- Text, Images
- Output
- Text
- Discovery tier
- Balanced
Pricing
USD per 1M tokens- Input
- $2.00
- Output
- $12.00
- Cache read
- $0.20
Reference prices as published by the model provider. API example
Specifications
- Context window
- 65.5K
- Max output
- 32.8K
Side by side
Gemini 3 Pro Image compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| Gemini 3 Pro ImageGoogleThis model | Celeris 1celerisRemove | |
|---|---|---|
| Provider | celeris | |
| Discovery tier | Balanced | Balanced |
| Added | Nov 18, 2025 | Jul 25, 2026 |
| Limits | ||
| Context window | 65.54KBest | 8.19K |
| Max output | 32.77KBest | 8.19K |
| Pricing, USD per 1M tokens | ||
| Input | $2.00 | $2.00 |
| Output | $12.00 | $6.00Best |
| Cache read | $0.20 | — |
| Performance | ||
| Request success (24h) | 100.0% | — |
| Benchmark indexes | ||
| Intelligence Index | — | 6.3 |
| Coding Index | — | 14.4 |
| Capabilities | ||
| Vision | Yes | No |
| Reasoning effort | — | — |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- Gemini 3 Pro Image
- Celeris 1
Intelligence Index
- Gemini 3 Pro Image—
- Celeris 16.3
Indexes
Coding Index
Composite of the coding evaluations
- Gemini 3 Pro Image: —
- Celeris 1: 14.4
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- Gemini 3 Pro Image: —
- Celeris 1: 0.4%
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- Gemini 3 Pro Image: —
- Celeris 1: 0.2%
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- Gemini 3 Pro Image: —
- Celeris 1: 151 Elo
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- Gemini 3 Pro Image: —
- Celeris 1: 301 Elo
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- Gemini 3 Pro Image: —
- Celeris 1: 1.2%
AA-LCRv1.1
Reasoning across long documents
- Gemini 3 Pro Image: —
- Celeris 1: 38.0%
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Gemini 3 Pro Image: —
- Celeris 1: 6.8%
GPQA Diamond
Graduate-level science questions
- Gemini 3 Pro Image: —
- Celeris 1: 63.1%
MMLU-Pro
Multi-subject knowledge and reasoning
- Gemini 3 Pro Image: —
- Celeris 1: 78.0%
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- Gemini 3 Pro Image: —
- Celeris 1: 0.0%
SciCode
Scientific computing in Python
- Gemini 3 Pro Image: —
- Celeris 1: 21.6%
Legacy evaluations
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Gemini 3 Pro Image: —
- Celeris 1: 38.0%
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.
- Request success (24h)
- 100.0%
- Reliable
- 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-3-pro-image-preview",
"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-3-pro-image-preview",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)Related models
Other Google models, starting with the same family.
