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 ProGoogleRemove | |
|---|---|---|
| Provider | ||
| Discovery tier | Frontier | Frontier |
| Added | Mar 29, 2026 | Jun 5, 2025 |
| Limits | ||
| Context window | 1.05M | 1.05M |
| Max output | 65.54K | 65.54K |
| Average output | — | 1.1K tokens |
| Pricing, USD per 1M tokens | ||
| Input | $2.00 | $1.25Best |
| Output | $12.00 | $10.00Best |
| Cache read | $0.20 | $0.13Best |
| Cache write | $0.38 | $0.38 |
| Performance | ||
| Throughput | 36.1 tok/s | 189.7 tok/sBest |
| Time to first token | 8.7 sBest | 12.3 s |
| Request success (24h) | 100.0%Best | 99.5% |
| Benchmark indexes | ||
| Intelligence Index | 29.7Best | 16.1 |
| Coding Index | 68.8Best | 33.3 |
| Math Index | — | 87.7 |
| Capabilities | ||
| Reasoning | Yes | Yes |
| Tools | Yes | Yes |
| Vision | Yes | Yes |
| Structured output | Yes | No |
| Audio input | Yes | Yes |
| Video input | Yes | Yes |
| 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
Intelligence Index
- Gemini Pro Latest29.7
- Gemini 2.5 Pro16.1
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: 33.3
Math Index
Composite of the math evaluations
- Gemini Pro Latest: —
- Gemini 2.5 Pro: 87.7
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- Gemini Pro Latest: 35.4%Top
- Gemini 2.5 Pro: 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: 0.0%
Harvey LAB-AAv1.1
Legal tasks passed in full, with no material hallucination
- Gemini Pro Latest: 0.0%
- Gemini 2.5 Pro: —
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- Gemini Pro Latest: 456 EloTop
- Gemini 2.5 Pro: 302 Elo
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- Gemini Pro Latest: 791 EloTop
- Gemini 2.5 Pro: 455 Elo
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- Gemini Pro Latest: 17.8%Top
- Gemini 2.5 Pro: 10.2%
AA-LCRv1.1
Reasoning across long documents
- Gemini Pro Latest: 82.0%Top
- Gemini 2.5 Pro: 69.0%
MLCR-AA
Long-context reasoning over medical records
- Gemini Pro Latest: 11.1%
- Gemini 2.5 Pro: 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: 22.5%
GPQA Diamond
Graduate-level science questions
- Gemini Pro Latest: 94.1%Top
- Gemini 2.5 Pro: 84.4%
MMLU-Pro
Multi-subject knowledge and reasoning
- Gemini Pro Latest: —
- Gemini 2.5 Pro: 86.2%
IFBench
Following precise output instructions
- Gemini Pro Latest: 77.1%Top
- Gemini 2.5 Pro: 48.7%
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- Gemini Pro Latest: 4.0%Top
- Gemini 2.5 Pro: 0.0%
SciCode
Scientific computing in Python
- Gemini Pro Latest: 58.7%Top
- Gemini 2.5 Pro: 46.3%
LiveCodeBench
Recent competitive programming problems
- Gemini Pro Latest: —
- Gemini 2.5 Pro: 80.1%
Math
AIME 2025
Competition mathematics, 2025 problems
- Gemini Pro Latest: —
- Gemini 2.5 Pro: 87.7%
AIME
Competition mathematics
- Gemini Pro Latest: —
- Gemini 2.5 Pro: 88.7%
MATH-500
Competition math problems
- Gemini Pro Latest: —
- Gemini 2.5 Pro: 96.7%
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- Gemini Pro Latest: 53.8%Top
- Gemini 2.5 Pro: 26.5%
τ²-Bench Telecom
Tool use in a customer-support simulation
- Gemini Pro Latest: 95.6%Top
- Gemini 2.5 Pro: 54.1%
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Gemini Pro Latest: 82.0%Top
- Gemini 2.5 Pro: 69.0%
Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis
- Gemini Pro Latest
- Gemini 2.5 Pro
| Leaderboard | Gemini Pro Latest | Gemini 2.5 Pro |
|---|---|---|
| Vision Style Control · Captioning | Not in top 20 | #3 · 1250.6 |
| Vision Style Control · Entity Recognition | Not in top 20 | #5 · 1250.8 |
Ranks among Noumen catalog models on LMArena leaderboards. Only top-20 placements are listed.LMArena
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
- 66.4%
- About $0.81 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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