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

Muse Spark 1.3 vs Muse Glimmer 30B vs Llama 4 Maverick vs Llama 4 Scout on fix an off-by-one in python

Meta models side by side on "Fix an off-by-one 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 function should return the arithmetic mean of a non-empty list of numbers, but its result is wrong. Fix the bug with the smallest possible change and return the complete corrected function only, in a Python code block.

def average(nums):
    total = 0
    for n in nums:
        total += n
    return total / (len(nums) - 1)

Rubric for the judge: Returns the full function with the divisor corrected to len(nums) and no other behavioural changes.

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

unjudged
failed: OpenRouter HTTP 403: This model requires you to complete the following before use: 18+ age confirmation. Confirm at https://openrouter.ai/settings/preferences.
Latency
0ms
Cost
$0
Per 1,000
$0

0 in · 0 out · 0 words · checks 0/4

  • contains
  • regex
  • not_contains
  • not_contains
(empty output)

Judge:

Muse Glimmer 30B

meta/muse-glimmer-30b

10/10
Latency
156ms
Cost
$0.00018
Per 1,000
$0.18

117 in · 129 out (107 reasoning) · 18 words · checks 4/4

  • contains
  • regex
  • not_contains
  • not_contains
```python
def average(nums):
    total = 0
    for n in nums:
        total += n
    return total / len(nums)
```

Judge: The output correctly fixes the bug by changing the divisor from len(nums) - 1 to len(nums), returns the complete function in a Python code block, and makes no other changes.

Llama 4 Maverick

meta-llama/llama-4-maverick

10/10
Latency
338ms
Cost
$4.4e-5
Per 1,000
$0.04

105 in · 33 out · 18 words · checks 4/4

  • contains
  • regex
  • not_contains
  • not_contains
```python
def average(nums):
    total = 0
    for n in nums:
        total += n
    return total / len(nums)
```

Judge: The output correctly fixes the bug by changing the divisor from len(nums) - 1 to len(nums), returns the complete function in a Python code block, and makes no other changes.

Llama 4 Scout

meta-llama/llama-4-scout

10/10
top judge scorecheapest scoring 8+
Latency
1.7s
Cost
$2.0e-5
Per 1,000
$0.02

105 in · 33 out · 18 words · checks 4/4

  • contains
  • regex
  • not_contains
  • not_contains
```python
def average(nums):
    total = 0
    for n in nums:
        total += n
    return total / len(nums)
```

Judge: The output correctly fixes the bug by changing the divisor from len(nums) - 1 to len(nums), returns the complete function in a Python code block, and makes no other changes.

Frequently asked

What does this prompt test?

Code fix: Returns the full function with the divisor corrected to len(nums) and no other behavioural changes. 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.

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