TL;DR
Muse Spark 1.1 had the highest raw planning mean, 0.759, in the August 8, 2026 study of 29 frozen conversations. Its standard error was 0.079, with historical cost of $0.0229 per decision and 9.6-second median latency. Luna scored 0.731 at $0.0012 per decision. The close means do not establish a decisive quality advantage; this comparison concerns planning rather than trading performance.
- Exact tested ID
- meta/muse-spark-1.1
- Published studies
- 1
- Provider
- meta
Planning models on 29 frozen conversations: compare the tested outputs
Tick: recorded mean score. Whisker: mean ± one recorded standard error (SE), not a confidence interval. Overlap alone does not establish a ranking.
16-model campaign, 29 frozen conversations; 14 model rows shown in the public table. Published 2026-08-08. Values retain their original units and grading scale.
Show the table · 2 rows, 5 columns
Scroll sideways to compare all columns. Select a heading to sort.
| meta/muse-spark-1.1 | 0.759 | 0.079 | $0.0229 | 9.6s |
| openai/gpt-5.6-luna | 0.731 | 0.068 | $0.0012 | 8.6s |
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16-model campaign, 29 frozen conversations; 14 model rows shown in the public table. Published 2026-08-08. Values retain their original units and grading scale.
meta/muse-spark-1.1 was tested on 2026-08-08. 16-model campaign, 29 frozen conversations; 14 model rows shown in the public table. Its row and the other published arm use the same task and measurement units.
What this version helps you choose
Muse Spark had the highest raw planning mean. Luna was the lower-cost recorded choice among close scores.
Route a request and plan the work: follow the published task
A useful planner chooses the right kind of work before doing it. The complete customer conversations and system prompts remain private.
Published task summary
An incoming request and its existing conversation.
Choose whether to write a plan, call a catalog tool, ask for clarification or continue an existing agent.
Read what came back
A routing decision and plan. Every model receives the same saved conversation; the test compares that decision before fresh research begins.
Replay 29 frozen conversations across the model arms, then compare graded plan scores, billing and response time.
Planning models on 29 frozen conversations
Inspect the original Muse planning metrics
For routing and planning, Muse Spark 1.1 recorded a mean score of 0.759 with standard error 0.079. Historical billing was $0.0229 per decision, and median response time was 9.6s. Compare that cost and response time with the other models in the same 29-conversation study; the mean alone does not establish a decisive quality advantage.
| Published metric | Value |
|---|---|
| Score | 0.759 |
| +/- SE | 0.079 |
| $ / decision | $0.0229 |
| p50 | 9.6s |
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Planning models on 29 frozen conversations: read these measurements
Read the complete rubric, sample and billing notes
16-model campaign, 29 frozen conversations; 14 model rows shown in the public table
Route an incoming request: write a plan, call a catalog tool, ask for clarification or continue an existing agent.
Scoring
Top planning means were close at this sample size. The source's paired comparison with Opus was inconclusive.
Cost units
Cost is provider-billed dollars per decision. Prefix reuse can make it differ from list price multiplied by every input token.
Time units
p50 is median latency in seconds, not the duration of complete research.
Frozen conversation
A saved input kept identical across model arms so their decisions can be compared.
Standard error (SE)
An estimate of uncertainty around the sample mean. A small mean difference needs more evidence than the ranking alone.
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