Unverified50% confidenceBenchmarkExact time
According to Artificial Analysis's test data, the Muse Wonder model consumes about 54,000 output tokens per task on average at max effort.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/16/2026
First Seen
Valid until: 1/14/2027
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Related Claims
UnverifiedEvaluating the per-task cost of large models requires considering both the token unit price and the number of tokens consumed to complete the task; the output token consumption of chain-of-thought reasoning models may increase by several times or even dozens of times.71% similarUnverifiedA WANDR score of 0.610 does not mean the model fully succeeds on 60% of tasks; the actual partial completion rate is related to the distribution of task complexity.68% similarUnverifiedThe idealized optimal task-model matching achieves a solution rate of 97.6%, at a cost of only $1.88 per task.67% similarVerifiedBefore outputting a final answer, a reasoning model's internal chain of thought may consume 3,000 to 20,000 tokens, far exceeding the approximately 500 tokens of an ordinary model.67% similarUnverifiedIn a simulation of 2000 requests, the small model (1 unit cost/request) had an accuracy of 74.5% and a cost of 1.34 per correct answer; the large model (20 unit cost/request) had an accuracy of 95.8% and a cost of 20.88 per correct answer.67% similar
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