Gemini 3 Pro Preview
1M context with mandatory reasoning mode for careful analysis of retrieved content before responding. Multimodal support enables RAG across text, images, audio, and video with robust tool-calling for dynamic retrieval. If you want to compare the best LLMs for your data, try Agentset.
Model Information
- Provider
- License
- Proprietary
- Input Price per 1M
- $2.00
- Output Price per 1M
- $12.00
- Context Window
- 1049K
- Release Date
- 2025-11-18
- Model Name
- gemini-3-pro-preview
- Total Evaluations
- 1350
Performance Record
LLMs Are Just One Piece of RAG
Agentset gives you a managed RAG pipeline with the top-ranked models and best practices baked in. No infrastructure to maintain, no LLM orchestration to manage.
Trusted by teams building production RAG applications
Performance Overview
ELO ratings by dataset
Gemini 3 Pro Preview's ELO performance varies across different benchmark datasets, showing its strengths in specific domains.
Gemini 3 Pro Preview - ELO by Dataset
Detailed Metrics
Dataset breakdown
Performance metrics across different benchmark datasets, including accuracy and latency percentiles.
PG
Quality Metrics
- Correctness
- 4.90
- Faithfulness
- 4.93
- Grounding
- 4.93
- Relevance
- 5.00
- Completeness
- 4.73
- Overall
- 4.90
Latency Distribution
- Mean
- 25137ms
- Min
- 13317ms
- Max
- 62299ms
SciFact
Quality Metrics
- Correctness
- 4.93
- Faithfulness
- 4.97
- Grounding
- 4.97
- Relevance
- 4.97
- Completeness
- 4.83
- Overall
- 4.93
Latency Distribution
- Mean
- 14583ms
- Min
- 10135ms
- Max
- 21489ms
MSMARCO
Quality Metrics
- Correctness
- 4.83
- Faithfulness
- 4.83
- Grounding
- 4.83
- Relevance
- 5.00
- Completeness
- 4.90
- Overall
- 4.88
Latency Distribution
- Mean
- 13990ms
- Min
- 7461ms
- Max
- 26343ms
Build RAG in Minutes, Not Months
Agentset gives you a complete RAG API with top-ranked LLMs and smart retrieval built in. Upload your data, call the API, and get grounded answers from day one.
import { Agentset } from "agentset";
const agentset = new Agentset();
const ns = agentset.namespace("ns_1234");
const results = await ns.search(
"What is multi-head attention?"
);
for (const result of results) {
console.log(result.text);
}Compare Models
See how it stacks up
Compare Gemini 3 Pro Preview with other top llms to understand the differences in performance, accuracy, and latency.