Code fix · 4 checks · max 600 tokens · benched 2026-11-05

Muse Spark 1.3 vs Muse Glimmer 30B vs Llama 4 Maverick vs Llama 4 Scout on parameterise a sql query in python

Meta models side by side on "Parameterise a SQL query in Python": Llama 4 Scout scores 10/10; Llama 4 Scout is the cheapest answer scoring 8+ at $0.02 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.

Muse Spark 1.3

meta/muse-spark-1.3

10/10

Latency: 1.3s
Cost: $0.00163
Per 1,000: $1.63

107 in · 353 out (291 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.

Muse Glimmer 30B

meta/muse-glimmer-30b

10/10

Latency: 237ms
Cost: $0.00017
Per 1,000: $0.17

110 in · 138 out (96 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()

Llama 4 Maverick

meta-llama/llama-4-maverick

10/10

Latency: 170ms
Cost: $4.8e-5
Per 1,000: $0.05

97 in · 41 out · 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.

Llama 4 Scout

meta-llama/llama-4-scout

10/10

top judge scorecheapest scoring 8+

Latency: 745ms
Cost: $2.2e-5
Per 1,000: $0.02

97 in · 41 out · 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()
```

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, Llama 4 Scout at $0.02 per 1,000 runs. If you need the top score, Llama 4 Scout at $0.02 per 1,000 runs.

Same prompt, other families

Run it yourself

Re-run this prompt on Meta with your own OpenRouter key, or tweak the wording and see what changes.

Partner picks

Replit Claude (credits)Make Taskade Fireflies.ai Pressmaster.ai Murf AI

Affiliate/referral links: commissions or credits

Side by side

Muse Spark 1.3

Muse Glimmer 30B

Llama 4 Maverick

Llama 4 Scout

Frequently asked

Same prompt, other families

Stop overpaying for AI tools