Best AI for SQL Generation
in 2026
A live leaderboard, re-run every month on current models: 12 hard PostgreSQL tests, queries to write and exact results to predict, scored strictly with no LLM judge. See which models lead now, then test them on your own schema.
Live Leaderboard: November 2026
Re-run every month on current model versions: a hard SQL generation task with 12 tests, 3 passes per model, and strict deterministic scoring (no LLM judge).
5 models reach 100%, and the cheapest of them costs 37x less than the priciest. A shared test can't separate them; your own prompts usually can.
| # | Model | Score | Cost/run |
|---|---|---|---|
| 1 | DeepSeek-V4.1-Flash | 100.0% | $0.0046 |
| 2 | GPT-5.6 Sol | 100.0% | $0.033 |
| 3 | GPT-5.6 Sol @high | 100.0% | $0.040 |
| 4 | Gemini 3.1 Pro | 100.0% | $0.167 |
| 5 | Gemini 3.1 Pro @high | 100.0% | $0.170 |
| 6 | Grok 4.5 | 99.1% | $0.046 |
| 7 | DeepSeek-V4-Pro | 99.1% | $0.074 |
| 8 | GPT-5.6 Luna | 96.3% | $0.0020 |
...plus 5 more models in the app leaderboard.
Run this benchmark yourself, free Then swap in your own prompts. 50 free credits, no card.
What the November 2026 Run Tests (and Does Not Test)
Key Findings: November 2026
🏆 Accuracy: 5 Models Reach 100%
DeepSeek-V4.1-Flash, GPT-5.6 Sol, GPT-5.6 Sol (high effort), Gemini 3.1 Pro and Gemini 3.1 Pro (high effort) all scored 100%. Next: Grok 4.5 at 99.1%. Across the 13 ranked model variants, scores run from 20.4% to 100%.
💰 Cost: The Same Top Score at 37x the Price
DeepSeek-V4.1-Flash reached 100% for $0.0046 per run. Gemini 3.1 Pro (high effort) reached the same score for $0.170. The cheapest model above 90% was GPT-5.6 Luna (96.3%, $0.0020 per run).
⚡ Speed: GPT-5.6 Sol Is the Fastest Near the Top
Among models within 5 points of the top score, GPT-5.6 Sol finished a run of the 12 tests in 27s on average at 100%. DeepSeek-V4-Pro took 218s for 99.1%.
📉 Paying More Didn't Score More
Claude Haiku 4.5 costs $0.0067 per run and scored 41.7%, below Gemini 3.1 Flash-Lite at 80.6% for $0.0012, 5x less.
Not ranked this month: Claude Opus 5 and Qwen3.7 Max. No run completed at the provider (errors or refusals), so a score would say nothing about these models.
What Makes These Tests Hard
⚖️ Generic Benchmarks vs. Custom Benchmarks
This leaderboard is a directional signal, not a universal SQL ranking. It tests PostgreSQL semantics and a few classic query patterns. Your workload might involve another dialect, large schemas, query performance or migrations.
When several models tie at the top, what separates them is cost, speed, and how each one handles your schema and edge cases. That is what a benchmark on your own queries shows.
These results are valid for this task design and scoring setup. Change the task, constraints, or scoring, and rankings can change, which is exactly why custom benchmarking matters.
Frequently Asked Questions
Which AI model is best for SQL generation in 2026?
It changes from month to month, so we keep a live answer: the leaderboard at the top of this page is re-run every month on current flagship and budget models, with 12 hard PostgreSQL tests. Several frontier models often share the top score, and the cheapest of them can cost a fraction of the priciest. The best model for you depends on your schema and dialect, so run the same benchmark on your own queries, free.
Can AI write complex SQL queries?
Yes, for most common patterns: current frontier models write correct window functions, anti-joins and greatest-per-group queries. Where they still slip is semantics, such as default window frames, NULL handling and date arithmetic, which is what our tests focus on. Always check generated SQL against your real data.
Is GPT or Claude better at writing SQL?
Neither wins consistently, and the answer changes with model versions. Compare them in the live table, then on your own schema. Provider safety filters can also refuse ordinary SQL questions; the leaderboard notes any model left out for that reason.
What is the cheapest AI for SQL generation?
Budget models such as DeepSeek's Flash line, GPT Luna, Gemini Flash-Lite and Grok 4.1 Fast cost 10 to 100 times less per run than flagships, and on our tests some of them reach the top score. The live table shows each model's cost per run next to its score.
How does OpenMark benchmark SQL generation?
With deterministic checks and no LLM judge: for queries we check the shape (for example NOT EXISTS instead of NOT IN, no window functions where they are forbidden), and for result questions the exact values at the start of the answer. Every model runs the same 12 tests 3 times, and we report the average score, its stability and the real cost per run.
Can OpenMark just do this for me?
Yes. For SQL generation, the done-for-you audit is from $299 with 48-hour turnaround. Send your task definition, sample inputs, and pass/fail criteria; we benchmark it across all relevant models (up to 30+) and return a report with the recommended primary, fallbacks, and cost projections at your volume. See the audit service →
Why Teams Use OpenMark AI
You define the evaluation in your words, for your use case. Not MMLU, not HumanEval — your actual prompts, your actual data.
Multiple runs per model with variance tracking. A model that scores 90 once and 60 the next isn't the same as one that scores 85 every time.
See which model is cheapest for your task — scored against quality, not just raw price-per-token.
No accounts with OpenAI, Anthropic, or Google required. OpenMark AI handles every API call — just describe your task and run.
Don't want to design the test yourself? Have us run it for you.
If you don't want to design the SQL generation test yourself or maintain it as new models ship — we run it for you on your data. Send us your task, we benchmark it across all relevant models (up to 30+) and send back a synthesized report with the recommended primary, fallbacks, cost-at-volume, and re-test triggers. From $299, 48-hour turnaround, no call required.
Benchmark AI Models on Your SQL Tasks
Test which model writes the cleanest SQL for YOUR schemas, YOUR queries, YOUR data models.
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