UniQL - Hive: leaderboard

Execution accuracy on the Hive dialect of UniQL (2026), which ports the 1,534 BIRD development questions to 16 SQL dialects: the percent of questions whose generated Hive query returns the gold result. Higher is better.

Metric: Execution accuracy (%) on the Hive dialect of UniQL (1,534 BIRD development-set questions with human-verified SQL in 16 aligned dialects): given the target dialect, schema and question, the model writes one query whose execution result must match the gold result; inference-only, one run; higher is better. Source: arxiv.org. Saturation forecast: Around May 2027. 13 models tracked.

Top models

#ModelScoreOverall rank
1Claude Sonnet 4.559.58#250
2Gemini 2.5 Pro59.32#266
3GPT-5.1 Codex55.48#288
4GPT-5 Mini54.04#348
5DeepSeek V4 Flash52.8#258
6Qwen 3 32B51.96#960
7Qwen 3 4B46.74#2281
8Qwen 3 8B44.85#1830
9Llama 3 70B Instruct42.63#1676
10Qwen 3 1.7B36.7#5520
11GPT-3.5 Turbo36.18#2753
12Llama 3 8B Instruct22.23#4612

No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.

Interactive version: theaggregate.ai/benchmark?slug=uniql-hive · How It Works · Data refreshed daily, snapshot 2026-11-13.