Google · Frontier
Gemini Pro Latest
Compatibility alias that routes to the newest version of Gemini Pro. Currently routes to Gemini 3.1 Pro Preview High.
- Reasoning
- Tools
- Vision
- Structured output
- Audio input
- Video input
- Input
- Text, Images, Video, Audio
- Output
- Text
- Discovery tier
- Frontier
Pricing
USD per 1M tokens- Input
- $2.00
- Output
- $12.00
- Cache read
- $0.20
- Cache write
- $0.38
Reference prices as published by the model provider. API example
Specifications
- Context window
- 1M
- Max output
- 65.5K
Side by side
Gemini Pro Latest compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| Gemini Pro LatestGoogleThis model | Gemini 2.5 Pro Preview 0325GoogleRemove | |
|---|---|---|
| Provider | ||
| Discovery tier | Frontier | Balanced |
| Added | Mar 29, 2026 | Mar 25, 2025 |
| Limits | ||
| Context window | 1.05M | 1.05M |
| Max output | 65.54K | 65.54K |
| Average output | — | 1.3K tokens |
| Pricing, USD per 1M tokens | ||
| Input | $2.00Best | $2.50 |
| Output | $12.00 | $10.00Best |
| Cache read | $0.20Best | $0.25 |
| Cache write | $0.38 | — |
| Performance | ||
| Throughput | 36.1 tok/s | — |
| Time to first token | 8.7 s | — |
| Request success (24h) | 100.0% | — |
| Benchmark indexes | ||
| Intelligence Index | 29.7Best | 16.1 |
| Coding Index | 68.8Best | 33.3 |
| Math Index | — | 87.7 |
| Capabilities | ||
| Reasoning | Yes | Yes |
| Tools | Yes | No |
| Vision | Yes | Yes |
| Structured output | Yes | No |
| Audio input | Yes | Yes |
| Video input | Yes | No |
| Reasoning effort | — | low, medium, high |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- Gemini Pro Latest
- Gemini 2.5 Pro Preview 0325
Intelligence Index
- Gemini Pro Latest29.7
- Gemini 2.5 Pro Preview 032516.1
Gemini Pro Latest: Better than 71% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- Gemini Pro Latest: 68.8Top
- Gemini 2.5 Pro Preview 0325: 33.3
Math Index
Composite of the math evaluations
- Gemini Pro Latest: —
- Gemini 2.5 Pro Preview 0325: 87.7
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- Gemini Pro Latest: 35.4%Top
- Gemini 2.5 Pro Preview 0325: 2.2%
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- Gemini Pro Latest: 9.4%Top
- Gemini 2.5 Pro Preview 0325: 0.0%
Harvey LAB-AAv1.1
Legal tasks passed in full, with no material hallucination
- Gemini Pro Latest: 0.0%
- Gemini 2.5 Pro Preview 0325: —
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- Gemini Pro Latest: 456 EloTop
- Gemini 2.5 Pro Preview 0325: 302 Elo
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- Gemini Pro Latest: 791 EloTop
- Gemini 2.5 Pro Preview 0325: 455 Elo
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- Gemini Pro Latest: 17.8%Top
- Gemini 2.5 Pro Preview 0325: 10.2%
AA-LCRv1.1
Reasoning across long documents
- Gemini Pro Latest: 82.0%Top
- Gemini 2.5 Pro Preview 0325: 69.0%
MLCR-AA
Long-context reasoning over medical records
- Gemini Pro Latest: 11.1%
- Gemini 2.5 Pro Preview 0325: 12.2%Top
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Gemini Pro Latest: 47.0%Top
- Gemini 2.5 Pro Preview 0325: 22.5%
GPQA Diamond
Graduate-level science questions
- Gemini Pro Latest: 94.1%Top
- Gemini 2.5 Pro Preview 0325: 84.4%
MMLU-Pro
Multi-subject knowledge and reasoning
- Gemini Pro Latest: —
- Gemini 2.5 Pro Preview 0325: 86.2%
IFBench
Following precise output instructions
- Gemini Pro Latest: 77.1%Top
- Gemini 2.5 Pro Preview 0325: 48.7%
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- Gemini Pro Latest: 4.0%Top
- Gemini 2.5 Pro Preview 0325: 0.0%
SciCode
Scientific computing in Python
- Gemini Pro Latest: 58.7%Top
- Gemini 2.5 Pro Preview 0325: 46.3%
LiveCodeBench
Recent competitive programming problems
- Gemini Pro Latest: —
- Gemini 2.5 Pro Preview 0325: 80.1%
Math
AIME 2025
Competition mathematics, 2025 problems
- Gemini Pro Latest: —
- Gemini 2.5 Pro Preview 0325: 87.7%
AIME
Competition mathematics
- Gemini Pro Latest: —
- Gemini 2.5 Pro Preview 0325: 88.7%
MATH-500
Competition math problems
- Gemini Pro Latest: —
- Gemini 2.5 Pro Preview 0325: 96.7%
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- Gemini Pro Latest: 53.8%Top
- Gemini 2.5 Pro Preview 0325: 26.5%
τ²-Bench Telecom
Tool use in a customer-support simulation
- Gemini Pro Latest: 95.6%Top
- Gemini 2.5 Pro Preview 0325: 54.1%
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Gemini Pro Latest: 82.0%Top
- Gemini 2.5 Pro Preview 0325: 69.0%
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
- 36.1 tok/s
- Faster than 26% of 250 models
- Time to first token
- 8.7 s
- Quicker than 13% of 250 models
- Prompt cache hits
- 69.2%
- About $0.75 per 1M input tokens with caching
- Request success (24h)
- 100.0%
- 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": "google/gemini-pro-latest",
"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="google/gemini-pro-latest",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)Related models
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