DeepSeek V4.1 Flash (max) vs Muse Spark 1.3 (thinking)
Should you write with DeepSeek V4.1 Flash (max) or Muse Spark 1.3 (thinking)? On this page the only thing that changes is the model. Both wrote their own version of the scripts behind 10 of my What's AI videos, five drafts per script, and four judges (three LLM judges from three model families plus Jev, a typed judge from TypeSafe) scored every draft blind, with my finished script as the reference. DeepSeek V4.1 Flash (max) leads overall, 87.8 to 82.5. Heads-up: DeepSeek V4.1 Flash (max) shares its model line with one of the four judges. The panel spans three LLM families plus Jev, a typed judge from TypeSafe, so no model here is scored by its own family alone (how the panel works).
The verdict
Easy one. DeepSeek V4.1 Flash (max) is #27 and Muse Spark 1.3 (thinking) is #83, and DeepSeek wins every metric while costing less per script, about a cent against four. The widest gap is Length Adherence, about twenty-eight points, so Muse's scripts come back the wrong length far more often. Both take just over a minute, and DeepSeek ships open weights. I can't find a writing reason to pick Muse Spark over it.
Pick DeepSeek V4.1 Flash (max) for cheap scripts that land on length, with open weights.
Pick Muse Spark 1.3 (thinking) only if you're already committed to Meta's models and can't add a provider.
How to read these numbers
Overall vs Elo. Overall is the weighted rubric score. Elo compares each model's five-draft average with every other model's, script by script, and counts gaps inside run-to-run noise as draws, so it rewards winning often and can disagree with overall.
Reading the Elo intervals. Each 95% range comes from resampling every model's recorded run scores on each task and re-ranking. Comparing these separate ranges does not test the Elo gap between two models: far apart is strong evidence, and overlap isn't a tie. The tasks stay fixed, so the ranges say nothing about new tasks (the long version).
Metric by metric: where the gap comes from
Blue is DeepSeek V4.1 Flash (max), orange is Muse Spark 1.3 (thinking), and the solid bar takes the metric. The weight under each name is how much it counts toward the overall. Weighted by how much each metric counts, Length Adherence does the most to put DeepSeek V4.1 Flash (max) ahead: it leads there by 28.1 points. If you care about one thing, say voice or length, go straight to that row.
Cost, speed, and consistency
Quality is half the decision. The other half is what each script costs you, how long you wait, and how often you get a bad draft. DeepSeek V4.1 Flash (max) is ahead and it's also the cheaper one: Muse Spark 1.3 (thinking) costs about 3.2x as much per script.
| DeepSeek V4.1 Flash (max) | Muse Spark 1.3 (thinking) | |
|---|---|---|
| Overall / 100 | 87.8 | 82.5 |
| Writing Elo | 2165 | 1629 |
| Score spread across drafts (± overall std) | 1.280 | 2.600 |
| Cost per script (USD) | 0.013 | 0.041 |
| Avg time per script (min) | 1.3 | 1.3 |
| Open weights | Yes | No |
Green wins the row, and for spread, cost and time, lower wins. Cost is one script at recorded list prices, uncached and without judging, so your real bill will differ. Spread is how much the overall score moves across a model's drafts, and time is the average time to get one accepted script, retries included.
Want the script-by-script detail? Full scorecards: DeepSeek V4.1 Flash (max) · Muse Spark 1.3 (thinking). The whole scoring pipeline is on the methodology page.
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