Top picks for SQL Generation (2026)
Writing correct, performant SQL from natural-language prompts. Ranked from 334 live models on the OpenRouter catalog, weighted for reasoning quality, structured output, tool calling.
| # | Model | Score | In / 1M | Out / 1M | Context | |
|---|---|---|---|---|---|---|
| 1 | Anthropic: Claude Sonnet 4.6anthropic/claude-sonnet-4.6 | 184 | $3.00 | $15.00 | 1,000,000 | Details → |
| 2 | Anthropic: Claude Opus 4.7anthropic/claude-opus-4.7 | 181 | $5.00 | $25.00 | 1,000,000 | Details → |
| 3 | OpenAI: GPT-5.4openai/gpt-5.4 | 174 | $2.50 | $15.00 | 1,050,000 | Details → |
| 4 | Z.ai: GLM 5.2z-ai/glm-5.2 | 173 | $1.00 | $4.00 | 1,048,576 | Details → |
| 5 | DeepSeek: DeepSeek V4 Prodeepseek/deepseek-v4-pro | 172 | $0.43 | $0.87 | 1,048,576 | Details → |
| 6 | Anthropic: Claude Opus 4.8anthropic/claude-opus-4.8 | 170 | $5.00 | $25.00 | 1,000,000 | Details → |
| 7 | DeepSeek: DeepSeek V4 Flashdeepseek/deepseek-v4-flash | 169 | $0.09 | $0.18 | 1,048,576 | Details → |
| 8 | Google: Gemini 3.1 Pro Previewgoogle/gemini-3.1-pro-preview | 168 | $2.00 | $12.00 | 1,048,576 | Details → |
| 9 | Google: Gemini 3.5 Flashgoogle/gemini-3.5-flash | 168 | $1.50 | $9.00 | 1,048,576 | Details → |
| 10 | MoonshotAI: Kimi K2.6moonshotai/kimi-k2.6 | 167 | $0.66 | $3.50 | 262,144 | Details → |
| 11 | MiniMax: MiniMax M3minimax/minimax-m3 | 167 | $0.30 | $1.20 | 1,048,576 | Details → |
| 12 | OpenAI: GPT-5.5openai/gpt-5.5 | 167 | $5.00 | $30.00 | 1,050,000 | Details → |
| 13 | Xiaomi: MiMo-V2.5-Proxiaomi/mimo-v2.5-pro | 165 | $0.43 | $0.87 | 1,048,576 | Details → |
| 14 | Qwen: Qwen3.7 Maxqwen/qwen3.7-max | 164 | $1.25 | $3.75 | 1,000,000 | Details → |
| 15 | OpenAI: GPT-5.4 Miniopenai/gpt-5.4-mini | 164 | $0.75 | $4.50 | 400,000 | Details → |
How we ranked these
For SQL Generation, we weight models on reasoning quality, structured output, tool calling. Scores combine each model's public specs with independent benchmark results (Aider Polyglot coding scores, Artificial Analysis intelligence/coding/agentic indices) and live pricing. See full methodology →
About SQL Generation
SQL generation is the task of converting natural-language requests into executable SQL queries that return correct results. You need this when you're building query interfaces, data exploration tools, or automating report generation without manual SQL writing. A strong model understands schema relationships, generates syntactically valid queries, and avoids N+1 patterns or unnecessary table scans. Weak models hallucinate column names, miss join conditions, or produce queries that run for minutes instead of seconds. Cost matters here: running a generated query against a 100M row table is expensive if the model didn't add appropriate WHERE clauses, so filtering happens on the model side before execution, not in post-processing.
When to use: Use this when a non-technical user needs to ask questions about a database ("Show me sales from last quarter") and you want an AI to write the actual SQL instead of building dozens of manual templates.
Common questions
What is the difference between a good and bad SQL generation model?
A good model knows your specific schema, understands which joins are efficient, and avoids generating queries that will timeout. Bad models produce syntactically correct SQL that either returns wrong results or scans every row unnecessarily. Claude 3.5 Sonnet and GPT-4 perform well here when given clear schema documentation, but even they need constraints on output format (no CTEs unless critical, prefer indexed columns in WHERE clauses).
How much does it actually cost to use AI for SQL generation at scale?
Model cost is negligible (a few cents per query), but execution cost dominates. One poorly generated query on a production database can cost you more in compute than a thousand model calls. Always validate generated queries on small datasets first, use query explain plans, and set execution timeouts before running against production tables.