2026 Text2SQL Model Showdown: Which One Performs Best?

Overview

The article evaluates the Text2SQL capabilities of twelve large models using the two most respected benchmarks—BIRD and Spider—and then maps each model to concrete usage scenarios such as enterprise deployment, low‑cost API access, edge devices, and rapid prototyping.

Benchmark Methodology

BIRD Benchmark : 12,751+ query‑SQL pairs from 95 real databases (33.4 GB), covering 37+ domains (blockchain, medical, finance, etc.). The primary metric is execution accuracy (EX), i.e., whether the generated SQL returns the correct result.

Why BIRD? It contains dirty data, inconsistent naming, and hidden business logic, reflecting real‑world enterprise databases.

Spider 1.0 : 200 databases, 10,181 queries; a simpler but still industry‑standard reference.

General‑Purpose Model Baseline Results

Claude Opus 4.6 – BIRD Dev 68.77 %, Test 70.15 % – currently the strongest general model.

Qwen3‑Coder‑480B‑A35B – Dev 66.17 %, Test 68.14 % – top Chinese model, only 2.6 pp behind Claude.

DeepSeek‑R1 – Dev 61.67 %, Test 60.93 % – strong reasoning but weaker SQL generation.

DeepSeek V3 (236B) – Dev 56.13 %, Test 56.68 % – a large generic model with modest Text2SQL performance.

GPT‑4 (original) – Dev 46.35 %, Test 54.89 % – outdated version.

Key Findings

Claude Opus 4.6 achieves >68 % baseline accuracy, close to human engineers.

Qwen3‑Coder narrows the gap to Claude and supports private deployment, making it cost‑effective for Chinese use cases.

DeepSeek‑R1 demonstrates that high reasoning ability does not guarantee strong SQL generation; specialized training matters.

Specialized Text2SQL Models (Top‑10 BIRD Rankings)

1️⃣ AskData + GPT‑4o – 81.95 % (agent framework, AT&T)

2️⃣ Agentar‑Scale‑SQL – 81.67 % (Ant Group)

3️⃣ LongData‑SQL – 77.53 % (LongShine AI)

4️⃣ DeepEye‑SQL – 76.63 % (HKUST)

5️⃣ Q‑SQL – 76.47 % (AWS, MoE)

6️⃣ MIC2‑SQL – 76.41 % (Hunan University)

7️⃣ SiriusAI‑Text2SQL‑Agent – 76.30 % (Tencent)

8️⃣ CHASE‑SQL + Gemini – 76.02 % (Google Cloud)

9️⃣ xiaoyi‑text‑to‑sql – 75.96 % (wenyuai)

🔟 RED‑SQL – 75.91 % (South China Normal University)

Human Engineer Baseline

BIRD Dev 92.96 % – the top model’s 81.95 % is already close to human performance for many structured enterprise databases.

Single‑Model Track (No Ensemble)

1️⃣ Gemini‑SQL – 77.14 % (Google Cloud)

2️⃣ Q‑SQL – 76.47 % (AWS, 30B‑3B MoE)

3️⃣ Databricks‑RLVR – 75.68 % (32B)

4️⃣ SiriusAI‑32B v2 – 75.01 % (Tencent)

5️⃣ Arctic‑Text2SQL‑R1‑32B – 73.84 % (Snowflake)

6️⃣ Arctic‑Text2SQL‑R1‑14B – 72.22 % (Snowflake)

7️⃣ Arctic‑Text2SQL‑R1‑7B – 68.47 % (Snowflake) – a 7B model beating a 671B generic model.

Deep Dives

XiYan‑SQL (Alibaba)

Core Architecture : Schema Linking → Multi‑generator parallel SQL generation (ICL few‑shot + SFT) → Refiner + Selector (voting).

Why it fits Chinese enterprises :

M‑Schema: a semi‑structured Markdown representation optimized for Chinese business contexts.

Latest version XiYanSQL‑QwenCoder‑2504 (April 2025) built on Qwen2.5‑Coder.

Fully open‑source (Apache 2.0), private‑deployment ready.

Consistently in BIRD Top 10, often rank 1.

Target audience : Chinese‑language business domains with high data‑security requirements (finance, government, healthcare).

Arctic‑Text2SQL‑R1 (Snowflake)

Released 2025, uses reinforcement learning with a minimal reward scheme: only two questions—“Can the SQL execute?” and “Is the result correct?”—are asked; correct answers receive reward, incorrect receive penalty. This forces the model to learn genuine correctness rather than exploiting partial credit.

Parameter vs. Accuracy :

R1‑7B – 68.47 % (runs on a single RTX 3090)

R1‑14B – 72.22 % (dual RTX 3090)

R1‑32B – 73.84 % (dual RTX 4090)

Target audience : Resource‑constrained teams needing fully offline deployment.

SiriusAI (Tencent)

Ranks Top 7 on BIRD; 32B version scores 75.01 % on the single‑model track. Its main advantage is engineering stability, validated on tens of thousands of internal business tables. Available on HuggingFace with a usage‑license requirement for commercial use.

Vanna (RAG Framework)

Provides the quickest way to build a Text2SQL system (7.7k GitHub stars). Core principle: Retrieval‑Augmented Generation.

Training: feed DDL + example SQL + business docs
Query: user question → vector retrieve examples → inject context → LLM generates SQL → auto‑execute → return result

Example Python snippet (5‑minute setup):

import vanna
from vanna.openai import OpenAI_Chat
from vanna.chromadb import ChromaDB_VectorStore

class MyVanna(ChromaDB_VectorStore, OpenAI_Chat):
    def __init__(self, config=None):
        ChromaDB_VectorStore.__init__(self, config=config)
        OpenAI_Chat.__init__(self, config=config)

vn = MyVanna(config={
    'api_key': 'your_api_key',
    'model': 'qwen3-coder'  # use Qwen in China instead of GPT
})

vn.connect_to_mysql(host='localhost', dbname='sales', user='root', password='xxx')
vn.train(ddl="CREATE TABLE orders (...)")
vn.train(question="上周销售额最高的产品？", sql="SELECT ...")
sql, df, fig = vn.ask("上个月北京的订单量")
print(df)

Target audience : Small technical teams (1‑3 people) or MVP validation phases without heavy customization.

Cost Comparison (100 万 queries per month, 2000‑token input, 200‑token output)

GPT‑4o API – ¥30,000 (best performance, highest cost)

Claude Opus 4.6 API – ¥40,000 (most accurate, most expensive)

Qwen3‑Coder API (Alibaba Bailei) – ¥6,000 (high cost‑performance for Chinese use)

DeepSeek API – ¥3,000 (cheapest API today)

Local Arctic‑R1‑7B (dual 3090) – ~¥2,000 (one‑time hardware, long‑term cheapest)

Local XiYan‑32B (4 × 3090) – ~¥3,500 (Chinese‑language best, private‑secure)

Cost conclusion : Domestic APIs cost roughly one‑tenth of GPT‑4o; deploying a 7B model locally becomes the most economical at scale.

Decision Guide

Scenario 1 – Enterprise IT / Data Department

If data cannot be moved to the cloud → deploy XiYan‑SQL‑32B or Arctic‑R1‑7B locally.

If cloud‑ready and Chinese‑centric → Qwen3‑Coder API + few‑shot optimization.

If English / international → Claude Opus API or GPT‑4o API.

Scenario 2 – SaaS Product Team

Query volume < 100 k/month → Vanna + GPT‑4o for rapid validation.

10 k–100 k/month → DeepSeek API + self‑consistency voting for controlled cost.

> 100 k/month → Specialized model deployment for economies of scale.

Scenario 3 – Developer / Researcher

Learning / exploration → Vanna (quick start).

Benchmarking / paper submission → reproduce Arctic‑R1 training pipeline.

Production rollout → XiYan‑SQL with private deployment.

General vs. Specialized Models

SQL accuracy : General 60‑70 % (baseline) vs. Specialized 68‑77 % after task‑specific training.

Chinese understanding : General models excel; specialized models are trained mainly on English data, so Chinese performance may lag.

Multi‑turn dialogue : Native in general models; requires extra engineering in specialized models.

Parameter efficiency : General models need hundreds of billions of parameters; specialized models achieve comparable results with as few as 7 B.

Deployment cost : General – API pay‑per‑use; Specialized – one‑time hardware, low marginal cost.

Maintenance : General – vendor updates; Specialized – self‑managed model versions.

Practical Combination Strategy

Use specialized models (XiYan‑SQL or Arctic‑R1) for high‑frequency, standardized reporting queries, and route complex semantic or multi‑turn requests to a general LLM. Confidence thresholds can automate the routing.

Future Outlook

The BIRD top score is 81.95 % versus human engineers at 92.96 % – an 11 pp gap caused by hidden business logic, cross‑system identifier mismatches, and evolving data definitions. Closing this gap requires integrating enterprise knowledge bases and continuous feedback loops, not merely larger models. 2026 is positioned as the year Text2SQL moves from research to large‑scale production, with engineering implementation becoming the decisive factor.