How Model Size Shapes SQL Mastery: Insights from 1.5B‑32B LLMs

Introduction

With the rapid advancement of artificial intelligence, language models have become increasingly proficient in natural language processing. Their ability to generate and comprehend SQL—a core language for database operations—has emerged as a key metric for evaluating practical usefulness, and model size significantly impacts performance on SQL tasks.

Relationship Between Model Parameters and Capability

Parameter count correlates closely with a model's understanding and generation abilities. Small models (millions to tens of millions of parameters) are limited by compute resources and training data, resulting in weaker performance on complex tasks. Large models (tens of billions to hundreds of billions of parameters) excel at language modeling and contextual reasoning, handling highly structured tasks such as SQL more efficiently.

In short, larger models capture more intricate syntax rules and semantic relationships.

Performance of Models with Different Parameter Sizes

Small‑scale models (million‑level parameters)

Performance is generally weak on SQL tasks. A model with only a few million parameters may handle simple SELECT statements but often fails on nested queries or multi‑table joins due to limited capacity for modeling complex grammar.

Medium‑scale models (billion‑level parameters)

Significant improvement. Models around 1 billion parameters can manage single‑table queries and simple joins, yet may still produce logical errors on more advanced constructs such as GROUP BY or HAVING.

Large‑scale models (hundred‑billion‑level parameters and above)

Impressive capabilities. Models like GPT‑4 (or equivalents) generate complex queries, understand context, and even optimize SQL statements, thanks to their powerful language modeling and reasoning abilities.

Complex Query Case Study

We simulated a MySQL 8.0 environment and tasked three distilled models—DeepSeek‑R1‑Distill‑Qwen‑32B, DeepSeek‑R1‑Distill‑Qwen‑7B, and DeepSeek‑R1‑Distill‑Qwen‑1.5B—to execute a complex query, providing both results and execution steps.

Temperature: 0.3

Task Description

Simulate MySQL 8.0 execution of a complex SQL query and return the process and results.

Schema

CREATE TABLE departments (dept_id INT PRIMARY KEY, dept_name VARCHAR(50));
CREATE TABLE employees (id INT PRIMARY KEY, name VARCHAR(50), department_id INT, salary DECIMAL(10,2), hire_date DATE);
INSERT INTO departments (dept_id, dept_name) VALUES
  (10,'Engineering'),(20,'Sales'),(30,'Marketing'),(40,'HR'),(50,'Finance'),(60,'IT'),(70,'Operations'),(80,'Research'),(90,'Support'),(100,'Design');
INSERT INTO employees (id, name, department_id, salary, hire_date) VALUES
  (1,'Alice',10,62000.00,'2023-02-10'),
  (2,'Bob',20,78000.00,'2022-07-15'),
  (3,'Charlie',30,45000.00,'2023-09-05'),
  (4,'David',40,55000.00,'2021-12-20'),
  (5,'Eve',50,67000.00,'2022-03-18'),
  (6,'Frank',60,72000.00,'2023-06-25'),
  (7,'Grace',70,49000.00,'2022-11-30'),
  (8,'Hank',80,83000.00,'2023-04-12'),
  (9,'Ivy',NULL,59000.00,'2022-08-22'),
  (10,'Jack',90,64000.00,'2023-01-08');

SQL Query

SELECT d.dept_name, COUNT(e.id) AS emp_count, AVG(e.salary) AS avg_salary
FROM departments d
LEFT JOIN (
  SELECT id, name, department_id, salary, hire_date
  FROM employees
  WHERE salary > (
    SELECT AVG(salary)
    FROM employees
    WHERE hire_date >= '2022-01-01'
  )
) e ON d.dept_id = e.department_id
WHERE e.hire_date >= '2022-01-01'
GROUP BY d.dept_name
HAVING COUNT(e.id) > 0
ORDER BY avg_salary DESC;

Reasons for Choosing This Task

Clear differences: The task’s complexity makes performance gaps between models evident.

Tests SQL syntax understanding: Models must parse keywords, operators, and structure correctly.

Tests database comprehension: Correct execution requires interpreting the CREATE and INSERT statements.

Tests SQL processing ability: The query involves filtering, aggregation, sorting, subqueries, and joins.

Tests logical reasoning: The multi‑step process (schema creation, data insertion, query execution) must be followed precisely.

Results Comparison

32B Model Performance

Query Result

dept_name | emp_count | avg_salary
Research  | 1         | 83000.00
Sales     | 1         | 78000.00
IT        | 1         | 72000.00
Finance   | 1         | 67000.00

Execution Steps

Calculate average salary for employees hired after 2022‑01‑01 (≈64,333.33).

Filter employees with salary > 64,333.33 and hire date ≥ 2022‑01‑01 (employees 2, 5, 6, 8).

LEFT‑JOIN departments on department_id.

Group by department, count employees, compute average salary.

Keep groups with count > 0 and order by average salary descending.

7B Model Performance

Query Result

dept_name | emp_count | avg_salary
Research  | 1         | 83000.00
Sales     | 1         | 78000.00
IT        | 1         | 72000.00
Finance   | 1         | 67000.00
Support   | 1         | 64000.00

Execution Steps

Compute overall employee average salary (63,400.00).

Filter employees with salary > 63,400 and hire date ≥ 2022‑01‑01 (5 employees).

LEFT‑JOIN these employees to departments.

Group by department to count employees and average salary.

Discard empty departments and sort by average salary descending.

The steps are correct, but the result includes an extra row (Support) because the model calculated the average without applying the hire‑date filter.

1.5B Model Performance

Final Result

Department names: Engineering, Sales, Marketing, HR, Finance, IT, Operations, Research, Support, Design

Average salaries: 62000.00, 78000.00, 45000.00, 59000.00, 67000.00, 72000.00, 49000.00, 83000.00, 59000.00, 64000.00

Sorted order matches the list above.

Execution Steps

Prepare departments and employees tables.

Use a subquery to select employees with salary > (SELECT AVG(salary) FROM employees WHERE hire_date ≥ '2022‑01‑01').

LEFT‑JOIN departments and employees on department_id.

Apply filters e.hire_date ≥ '2022‑01‑01' and d.dept_id = e.department_id.

Group by department and sort by avg_salary descending.

The steps are correct, but the model failed to produce intermediate results and returned nonsensical final data, indicating it understood the task but could not reason through it.

Conclusion

Model parameter size plays a crucial role in SQL capability. Small models handle simple queries, medium models manage moderate complexity, while large models excel at intricate SQL tasks. However, even the largest models may stumble on recursive or highly specialized queries, suggesting fine‑tuning can further improve accuracy.