Gemini 3 Pro on Database AI Suite - Claude 4.5 still leads

Published: November 21, 2025

Performance Scores

Multi-step database analysis workflows:

• Claude Sonnet 4.5: 115
• GPT-5.1: 100
• Gemini 3 Pro: 90
• 90-tier: Gemini 2.5 Pro, GPT-4.1, KIMI 2 Thinking
• 85-tier: Gemini 2.5 Flash, Qwen 3 Max, GLM 4.6, DeepSeek R1
• 80-tier: Gemini 2.0 Flash

Model Findings

• Claude Sonnet 4.5: Creates multiple segmentation variables beyond the prompt. Quality leader.
• GPT-5.1: Strong. Token bloat gone vs GPT-5. Clear second.
• Gemini 3 Pro: Similar to Gemini 2.5 Pro. Better explanations, same output quality. Doesn't match top two.

What Was Tested

Credit card analysis: 1M customer + 10M transaction tables (AWS RDS MySQL). Multi-step workflow - plan, summarize, create derived variables, merge, segment profiles.

Weighted ranking of Indian credit card issuers from RBI data (Postgres).

Practical Choices

• High quality → Claude Sonnet 4.5
• Balance → GPT-4.1
• Low cost- great value→ Gemini 2.5 / 2.0 Flash

Cost Breakdown

Planning vs Execution

Single iteration: reasoning LLM runs once (20% cost), execution agent runs 7-10 queries with debugging (80% cost).

Reasoning / Planning Cost (Per 100 Questions)

• High: Claude, GPT-5.1, Gemini 3 Pro (approx. $7.00)
• Mid: GPT-4.1, KIMI 2, Qwen, GLM, DeepSeek (approx. $2.50)
• Budget: Gemini 2.5 Flash (approx. $1.50), Gemini 2.0 Flash ($0.30)

Avoid: Gemini 2.5 Pro ($10) and GPT-5 ($15) - token bloat.

Execution Cost (Per 100 Questions)

• Advanced analysis: approx. $12.50 (GPT-4.1, consistent across reasoning models)
• Single-step: $0.40 (GPT-4.1-mini) to approx. $3.50 (GPT-5.1)

I use OpenAI for SQL execution - more reliable. Multi-step workflows multiply costs fast. Use only when needed.

These costs relate to my typical uses. Your numbers will vary based on context, architecture & output volume. Always test with your use case. Always check actual billing, not token based estimates.

DATS-4: Database AI Suite- v4

Remote database AI app. Postgres or MySQL. Single-step queries or multi-step analysis. Python charts, table upload, PDF reports. Open source, live, free.

Try It

Use Sample button. Data loads to temp Postgres. Or connect your database.

Public app routes through my backend - sandbox only. Deploy on your servers for live work

Resources

• Database AI Field guide- usage, architecture, process flows
• Previous post: GPT-5.1 + KIMI 2 evaluation with short video
• Posts & guides - AI for Analytics: tigzig.com

Direct access to the main database AI apps:

• ChatGPT + Your Database (connect any Postgres/MySQL): ChatGPT here
• ChatGPT + Fixed Data (Simultaneous connection across 3 Databases): ChatGPT here

Related resources - Database AI

• Implementing Database AI: Field Guide - 49-page PDF - architecture, agent setups, cost analysis - Read Here
• In-Browser Database AI - 1.5 GB files: process locally with DuckDB - Read here
• 10 Options for Talking to your databases - 10 micro-apps across 5 categories - Remote, In-Browser, ChatGPT, Voice, Rapid Deploy - Read here