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Tool call · 5 checks · max 400 tokens · benched 2026-09-16

Muse Spark 1.3 vs Muse Glimmer 30B vs Llama 4 Maverick vs Llama 4 Scout on pick the right tool of three

Meta models side by side on "Pick the right tool of three": 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 function-calling engine. Reply with JSON only, no prose, no code fences.

User

You have three tools:
1. {"name": "search_docs", "parameters": {"query": "string"}}
2. {"name": "create_ticket", "parameters": {"subject": "string", "body": "string"}}
3. {"name": "get_order_status", "parameters": {"order_id": "string"}}

Choose exactly one tool for the user message and produce the call as a JSON object of the shape {"name": string, "arguments": object}.

User: Where is my order A1029? It was supposed to arrive yesterday.

Rubric for the judge: Selects get_order_status with order_id A1029 and nothing else; does not open a ticket or search docs.

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 0/5

  • json_valid
  • json_keys
  • regex
  • regex
  • not_contains
(empty output)

Judge:

Muse Glimmer 30B

meta/muse-glimmer-30b

10/10
Latency
101ms
Cost
$0.00016
Per 1,000
$0.16

163 in · 98 out (75 reasoning) · 5 words · checks 5/5

  • json_valid
  • json_keys
  • regex
  • regex
  • not_contains
{"name": "get_order_status", "arguments": {"order_id": "A1029"}}

Judge: Output correctly selects get_order_status with order_id A1029, uses proper JSON format, and avoids unnecessary tools or commentary.

Llama 4 Maverick

meta-llama/llama-4-maverick

10/10
Latency
521ms
Cost
$4.5e-5
Per 1,000
$0.05

151 in · 21 out · 5 words · checks 5/5

  • json_valid
  • json_keys
  • regex
  • regex
  • not_contains
{"name": "get_order_status", "arguments": {"order_id": "A1029"}}

Judge: Output correctly selects get_order_status with order_id A1029 in proper JSON format, matching the rubric requirements exactly.

Llama 4 Scout

meta-llama/llama-4-scout

10/10
top judge scorecheapest scoring 8+
Latency
736ms
Cost
$2.1e-5
Per 1,000
$0.02

151 in · 21 out · 5 words · checks 4/5

  • json_valid
  • json_keys
  • regex
  • regex
  • not_contains
{"name": "get_order_status", "parameters": {"order_id": "A1029"}}

Judge: Output correctly selects get_order_status with order_id A1029 in proper JSON format, matching the rubric requirements exactly.

Frequently asked

What does this prompt test?

Tool call: Selects get_order_status with order_id A1029 and nothing else; does not open a ticket or search docs. The deterministic checks are json_valid, json_keys, regex, regex, 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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