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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 emit a weather tool call

Meta models side by side on "Emit a weather tool call": 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 one tool:
{"name": "get_weather", "parameters": {"city": "string", "unit": "one of 'c' or 'f'"}}

Produce the tool call for this user message as a JSON object of the shape {"name": string, "arguments": object}.

User: What's it like in Lisbon right now? Fahrenheit please.

Rubric for the judge: Exactly {"name":"get_weather","arguments":{"city":"Lisbon","unit":"f"}} with no extra keys or text.

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
  • regex
(empty output)

Judge:

Muse Glimmer 30B

meta/muse-glimmer-30b

10/10
Latency
163ms
Cost
$9.6e-5
Per 1,000
$0.10

121 in · 54 out (21 reasoning) · 7 words · checks 5/5

  • json_valid
  • json_keys
  • regex
  • regex
  • regex
{"name": "get_weather", "arguments": {"city": "Lisbon", "unit": "f"}}

Judge: Output exactly matches the required format with correct tool name, city, and unit parameters, no extra keys or commentary.

Llama 4 Maverick

meta-llama/llama-4-maverick

10/10
Latency
306ms
Cost
$3.9e-5
Per 1,000
$0.04

109 in · 24 out · 7 words · checks 5/5

  • json_valid
  • json_keys
  • regex
  • regex
  • regex
{"name": "get_weather", "arguments": {"city": "Lisbon", "unit": "f"}}

Judge: Output exactly matches the required format with correct tool name, city, and unit parameters, no extra keys or commentary.

Llama 4 Scout

meta-llama/llama-4-scout

10/10
top judge scorecheapest scoring 8+
Latency
2.8s
Cost
$1.8e-5
Per 1,000
$0.02

109 in · 24 out · 7 words · checks 5/5

  • json_valid
  • json_keys
  • regex
  • regex
  • regex
{"name": "get_weather", "arguments": {"city": "Lisbon", "unit": "f"}}

Judge: Output exactly matches the required format with correct tool name, city, and unit parameters, no extra keys or commentary.

Frequently asked

What does this prompt test?

Tool call: Exactly {"name":"get_weather","arguments":{"city":"Lisbon","unit":"f"}} with no extra keys or text. The deterministic checks are json_valid, json_keys, regex, regex, regex.

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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