ai Muse Spark 1.3
Other · proprietary · writing benchmark
The short version
Muse Spark 1.3 sits at #61 of 146 on ToneBench, in the middle of the pack, with a writing Elo of 1655 and an overall score of 82.2 out of 100. We measured it by having it write all 10 of our real YouTube scripts, five times each, then scoring every draft blind against our own finished versions. Here is how it shook out.
Within Other it ranks 2nd of 6. Muse Spark 1.3 (thinking) is the family's top writer here, about 44 Elo ahead, so this config trades some quality for whatever it saves you in cost or speed.
Among proprietary models, it comes in 50th of 96. That ranking is against the stronger half of the board: closed models still set the pace on voice fidelity here.
What it does best is YouTube Structure: it ranks 45th on that dimension at 83.2, well above the board average. Its softer spot is Length Discipline (63.3, 98th), which is the thing to watch if that metric matters most for your use.
It was uneven across the 10 scripts. Its best run was the career guide / hiring analysis (85.7) and its weakest was the engineering process walkthrough / presentation adaptation (77.5), a spread of about 8 points. Worth knowing if your writing skews toward one of those formats.
On cost, it runs about $0.034/task. DeepSeek V4 Flash 0731 scores higher for less money, at $0.005/task, so on pure value this config is not the frontier. Still, it beats 18 models that cost noticeably more.
Skill profile
The shape is the story: the further a corner reaches, the stronger that dimension. Biggest edge over the board: YouTube Structure (+7.7); furthest behind: Length Discipline (-5.2).
Per-metric scores
Nine writing dimensions, each scored 0–100 and blended by the editorial weight shown. Rank is against all 146 current-ranked models. The thick bar is this model; the thin lines above and below are the current field's best model (darker) and average (lighter) on the same scale. The small ± number is its run-to-run variation.
Per-article scores
The same 10 real scripts every current-ranked model writes, scored individually. Different formats stress different skills.
Consistency: run-to-run variability
Every cell on this page is the mean of 5 independent runs per script; the ± numbers are the run-to-run standard deviation. Overall, Muse Spark 1.3 varies by ± 2.8 points between runs versus a board median of ± 3.1, so it is about as repeatable as the typical model on the board.
| Metric | This model | Board median | Verdict |
|---|---|---|---|
| Tone & Voice ? | 84.8 ± 1.8 | ± 2.0 | typical |
| Writing Craft ? | 83.5 ± 2.1 | ± 1.9 | typical |
| Substance & Value ? | 84.5 ± 2.2 | ± 2.3 | typical |
| Flow & Emotion ? | 82.2 ± 2.3 | ± 2.3 | typical |
| YouTube Structure ? | 83.2 ± 2.5 | ± 3.4 | typical |
| Hook ? | 85.4 ± 2.2 | ± 2.4 | typical |
| Length Discipline ? | 63.3 ± 11.5 | ± 9.4 | typical |
| Anti-Slop ? | 88.6 ± 1.8 | ± 2.2 | typical |
| Visual Cues ? | 75.3 ± 5.0 | ± 4.2 | typical |
Measured, not modeled
Numbers we log on every run and rarely talk about. None of these affect the writing scores; cost and latency are informational.
What each judge scored it
The published overall of 82.2 is the consensus of three family-disjoint judges scoring the same 50 stored drafts blind with the identical rubric. The highest and lowest judge differ by 2.4 points on its overall. The three judges essentially agree on this model. How the panel works: methodology.
How we ran Muse Spark 1.3
- Accessed viaOpenRouter · model page ↗
- Exact model / route id
meta/muse-spark-1.3(provider default reasoning) - Generations5 independent runs × 10 scripts = 50 drafts, stored immutably after generation
- Run dates2026-09-03
- Judges3-judge panel: Claude Opus 5, high effort (Anthropic) + DeepSeek V4 Flash (OpenRouter) + GPT-5.6 Sol, medium reasoning (Codex CLI): every draft scored independently by all three with the identical rubric, blind to model identity, evidence quotes required before each metric score; published score = per-metric consensus mean, verified with a Bradley-Terry refit (methodology)
- Samplingprovider-default sampling, no fine-tuning, no system-prompt changes; every current-ranked model gets the identical brief, style contract and research packet
- What stays privatethe exact rubric and any non-public reference material; source-publication timing is disclosed for exceptions (why)
Frequently asked questions
How good is Muse Spark 1.3 at writing?
On ToneBench it ranks #61 of 146 with a writing Elo of 1655 and an overall score of 82.2/100. That score comes from writing our 10 real YouTube scripts five times each and scoring every draft blind against our own finished versions across nine writing dimensions.
Is Muse Spark 1.3 the best Other model for writing?
Not quite. Within Other it ranks 2nd of 6; Muse Spark 1.3 (thinking) is the family's best writer here.
Is Muse Spark 1.3 good value for the money?
It costs about $0.034/task. DeepSeek V4 Flash 0731 scores higher for less, so it is not the value pick.
What are Muse Spark 1.3's strengths and weaknesses?
Its strongest dimension is YouTube Structure (45th on the board, 83.2). Its weakest is Length Discipline (98th on the board, 63.3). The full nine-metric breakdown is on this page.
Does Muse Spark 1.3 write some formats better than others?
Yes. Its best of our 10 scripts was the career guide / hiring analysis (85.7) and its weakest was the engineering process walkthrough / presentation adaptation (77.5).
How consistent is Muse Spark 1.3 between runs?
We run every script 5 times. Muse Spark 1.3's overall score varies by about ±2.8 points between runs, versus a board median of ±3.1. That is typical consistency for this board. The full per-metric variability table is on this page.
How was Muse Spark 1.3 evaluated?
Via OpenRouter using the exact model/route id meta/muse-spark-1.3, run on 2026-09-03. 5 runs per script, provider-default sampling, no fine-tuning; every draft scored blind with a fixed rubric by a three-family judge panel. Full details in the 'How we ran it' section and the methodology.
Is Muse Spark 1.3 open source?
No, it is a proprietary (closed-weights) model. Among proprietary models it ranks 50th of 96 for writing.