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Code fix · 4 checks · max 600 tokens · benched 2026-09-16

Qwen3.8 Max (0902) vs Qwen3.8 27B vs Qwen3.8 Flash vs Qwen3.7 Plus on parameterise a sql query in python

Alibaba models side by side on "Parameterise a SQL query in Python": Qwen3.8 Flash scores 10/10; Qwen3.8 Flash is the cheapest answer scoring 8+ at $0.09 per 1,000 runs. Outputs, checks, judge reasons, latency and cost.

The prompt every model received

System

You are a senior engineer. Reply with code only unless asked otherwise.

User

This sqlite3 code is vulnerable to SQL injection. Rewrite it to use a parameterised query with sqlite3's ? placeholder. Return the complete corrected function only.

def find_user(cursor, email):
    query = f"SELECT id, name FROM users WHERE email = '{email}'"
    cursor.execute(query)
    return cursor.fetchone()

Rubric for the judge: Uses execute(sql, (email,)) with a ? placeholder, keeps the same columns and return, no string formatting left.

Side by side

Every cell is one OpenRouter call at temperature 0 with the prompt's token cap and reasoning effort "low" where the model supports it. Cost is usage × the catalogue rate in models.json. Quality is one judge call to anthropic/claude-haiku-4.5 against the prompt's rubric, cached per prompt version.

Qwen3.8 Max (0902)

qwen/qwen3.8-max-0902

10/10
Latency
1.5s
Cost
$0.00083
Per 1,000
$0.83

126 in · 96 out (49 reasoning) · 20 words · checks 3/4

  • contains
  • regex
  • not_contains
  • not_contains
```python
def find_user(cursor, email):
    query = "SELECT id, name FROM users WHERE email = ?"
    cursor.execute(query, (email,))
    return cursor.fetchone()
```

Judge: The output correctly uses parameterised query with ? placeholder, execute(query, (email,)) syntax, maintains identical SELECT columns and return statement, eliminates all string formatting, and provides only the corrected function as requested.

Qwen3.8 27B

qwen/qwen3.8-27b

10/10
Latency
3.9s
Cost
$0.00040
Per 1,000
$0.40

126 in · 145 out (112 reasoning) · 20 words · checks 3/4

  • contains
  • regex
  • not_contains
  • not_contains


```python
def find_user(cursor, email):
    query = "SELECT id, name FROM users WHERE email = ?"
    cursor.execute(query, (email,))
    return cursor.fetchone()
```

Judge: The output correctly uses parameterised query with ? placeholder, passes email as tuple parameter, maintains identical SELECT columns and return statement, eliminates all string formatting, and provides only the corrected function as requested.

Qwen3.8 Flash

qwen/qwen3.8-flash

10/10
top judge scorecheapest scoring 8+
Latency
848ms
Cost
$8.9e-5
Per 1,000
$0.09

138 in · 145 out (103 reasoning) · 18 words · checks 3/4

  • contains
  • regex
  • not_contains
  • not_contains
def find_user(cursor, email):
    query = "SELECT id, name FROM users WHERE email = ?"
    cursor.execute(query, (email,))
    return cursor.fetchone()

Judge: The output correctly uses parameterised query with ? placeholder, execute(query, (email,)) syntax, maintains identical columns and return statement, eliminates all string formatting, and provides only the corrected function as requested.

Qwen3.7 Plus

qwen/qwen3.7-plus

1/10
Latency
725ms
Cost
$0.00080
Per 1,000
$0.80

100 in · 600 out (573 reasoning) · 14 words · checks 3/4

  • contains
  • regex
  • not_contains
  • not_contains
```python
def find_user(cursor, email):
    query = "SELECT id, name FROM users WHERE email =

Judge: Output is incomplete and non-functional; the query string is cut off mid-statement and lacks the parameterized placeholder, execute call, and return statement.

Frequently asked

What does this prompt test?

Code fix: Uses execute(sql, (email,)) with a ? placeholder, keeps the same columns and return, no string formatting left. The deterministic checks are contains, regex, not_contains, not_contains.

Which model should I pick for this task?

If the judge's bar of 8/10 is good enough for you, Qwen3.8 Flash at $0.09 per 1,000 runs. If you need the top score, Qwen3.8 Flash at $0.09 per 1,000 runs.

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