Best databases for web apps

We asked 10 models: The best databases for a new web application. Each named its own top 10.

Asked 4 ways: List request · Buyer asking for advice · Search query · Worth paying for?. Share of voice pools every wording.

Agreement between models
82%
Same model, run twice
87%
Different picks
18

When agreement between models is well below a model's agreement with itself, the split reflects real differences in taste rather than randomness.

The consensus

Each cell is the position that model gave the pick; blank means it left the pick off. Click a row for each model's reasoning.

#PickScoreListed byClaude Haiku 5.5GPT-6 LunaGemini 4 ArgonQwen 3.8 FlashDeepSeek V4 ProGLM-5.3Kimi K3MiniMax M3Mistral Large 4Nemotron 3 Ultra

3 lower-scoring picks are not shown; every ballot below lists them in full.

Most divisive

Picks most models listed, at very different positions

Hot takes

Picks that appear on only one ballot

Every ballot

Hover a pick to find it on the other ballots. Marked picks appear on no other ballot.

Claude Haiku 5.5

  1. PostgreSQL
  2. MySQL
  3. MongoDB
  4. SQLite
  5. Redis
  6. Microsoft SQL Server
  7. Oracle Database
  8. MariaDB
  9. Firebase
  10. Amazon DynamoDB

92% with consensus · 93% self-consistent

GPT-6 Luna

  1. PostgreSQL
  2. MySQL
  3. MongoDB
  4. SQLite
  5. Redis
  6. CockroachDB
  7. Amazon DynamoDB
  8. Supabase
  9. PlanetScale
  10. Microsoft SQL Server

94% with consensus · 90% self-consistent

Gemini 4 Argon

  1. PostgreSQL
  2. MySQL
  3. Supabase
  4. SQLite
  5. MongoDB
  6. Redis
  7. Amazon DynamoDB
  8. CockroachDB
  9. PlanetScale
  10. Cloud Firestore

86% with consensus · 84% self-consistent

Qwen 3.8 Flash

  1. PostgreSQL
  2. MongoDB
  3. MySQL
  4. Supabase
  5. Amazon DynamoDB
  6. Redis
  7. Firebase Realtime Database
  8. SQLite
  9. Cloud Firestore
  10. Microsoft SQL Server

80% with consensus · 84% self-consistent

DeepSeek V4 Pro

  1. PostgreSQL
  2. MySQL
  3. MongoDB
  4. SQLite
  5. Redis
  6. Supabase
  7. Amazon DynamoDB
  8. Firebase Realtime Database
  9. Microsoft SQL Server
  10. Neon

96% with consensus · 87% self-consistent

GLM-5.3

  1. PostgreSQL
  2. MySQL
  3. MongoDB
  4. SQLite
  5. Redis
  6. MariaDB
  7. Supabase
  8. Amazon DynamoDB
  9. Firebase
  10. Amazon Aurora

95% with consensus · 82% self-consistent

Kimi K3

  1. PostgreSQL
  2. MySQL
  3. MongoDB
  4. Redis
  5. SQLite
  6. Amazon DynamoDB
  7. MariaDB
  8. Elasticsearch
  9. Microsoft SQL Server
  10. CockroachDB

94% with consensus · 91% self-consistent

MiniMax M3

  1. PostgreSQL
  2. MySQL
  3. MongoDB
  4. SQLite
  5. Redis
  6. MariaDB
  7. Amazon DynamoDB
  8. Microsoft SQL Server
  9. Oracle Database
  10. Firebase Realtime Database

95% with consensus · 92% self-consistent

Mistral Large 4

  1. PostgreSQL
  2. MongoDB
  3. Redis
  4. MySQL
  5. SQLite
  6. Supabase
  7. Microsoft SQL Server
  8. Amazon Aurora
  9. Amazon DynamoDB
  10. PlanetScale

80% with consensus · 84% self-consistent

Nemotron 3 Ultra

  1. PostgreSQL
  2. MySQL
  3. MongoDB
  4. Redis
  5. SQLite
  6. Amazon DynamoDB
  7. MariaDB
  8. Supabase
  9. Microsoft SQL Server
  10. Cloud Firestore

95% with consensus · 84% self-consistent

Who agrees with whom

Overlap between two models' lists, weighted toward the top · 100 means identical · Color runs from 72 (lightest) to 97 (darkest)

Claude Haiku 5.5GPT-6 LunaGemini 4 ArgonQwen 3.8 FlashDeepSeek V4 ProGLM-5.3Kimi K3MiniMax M3Mistral Large 4Nemotron 3 Ultra
Claude Haiku 5.5897972899289947889
GPT-6 Luna898877949289927891
Gemini 4 Argon798878878480827282
Qwen 3.8 Flash727778827777768179
DeepSeek V4 Pro899487829389938192
GLM-5.3929284779390957993
Kimi K3898980778990937997
MiniMax M3949282769395937793
Mistral Large 4787872818179797781
Nemotron 3 Ultra899182799293979381