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DeepSeek V4.1 Flash 1017 vs Muse Spark 1.3

Muse Spark 1.3 rates 92 points higher; the 95% intervals do not overlap.

All comparisons

Rating

  1. DeepSeek V4.1 Flash 1017883±55
  2. Muse Spark 1.3975±25
850
900
950
1000
95% intervals from one fit; n = 21 models, each rated from at least 30 comparisons.

Head to head

  1. DeepSeek V4.1 Flash 101722%
  2. No decisive preference22%
  3. Muse Spark 1.356%
0%50%100%
Share of eligible comparisons between the two; ties stay in the denominator. Provisional: fewer than 30 comparisons.

Side by side

DeepSeek V4.1 Flash 1017Muse Spark 1.3
Leaderboard rank21 of 2115 of 21
Win rate17.8%28.6%
Completion97.3%97.9%
Speed0.9 min1.5 min
Recovery91.4%78–97%, n = 3585.4%73–93%, n = 48
Cost per completion$0.132$0.034

Under Recovery, its 95% interval and n, in failed actions. Last updated Nov 5, 2026, 10:43 PM UTC.

Keep comparing

DeepSeek V4.1 Flash 1017

  • Claude Haiku 5.5
  • MiMo V2.6 Pro
  • GLM 5.3 Flash
  • MiniMax M3.1 Flash

Muse Spark 1.3

  • Kimi K3
  • GPT-6 Luna
  • Claude Sonnet 5.5
  • Gemini 3.8 Flash

Questions

Is DeepSeek V4.1 Flash 1017 or Muse Spark 1.3 better for computer use?

In eligible head-to-head comparisons, DeepSeek V4.1 Flash 1017 has a 22.2% decisive win rate and Muse Spark 1.3 has 55.6%. 22.2% end without a decisive preference. This matchup is provisional; the evidence is limited.

Does completion mean the task was verified?

No. Completion for DeepSeek V4.1 Flash 1017 and Muse Spark 1.3 records whether the agent reported finishing. Preferences, task success and next-action recovery are different measurements.

How should I compare speed and cost?

Read speed and estimated cost alongside completion and preference. An early failure can be fast. Cost per completion includes tokens spent on unsuccessful runs, uses published list rates and excludes infrastructure, caching discounts and negotiated rates.

How often are these comparison results updated?

Comparison pages refresh their benchmark snapshot every five minutes. The last-updated timestamp records when the metrics were computed, not when the last battle occurred. Model rank may move between snapshots.

How it’s measured
Rank and rating
A Bradley-Terry fit over eligible comparisons from the latest 20,000 judged computer battles. The rank is the rating’s position, and ± is its 95% interval: where two intervals overlap, the data does not settle the order between those two. A model under the provisional floor is listed and not rated.
Win rate
Decisive wins over the same eligible comparisons, ties kept in the denominator. Opponent mixes differ between models.
Completion, speed and cost
The Computer board’s non-synthetic runs; a battle the arena replayed counts through its replay. Completion and speed read the runs that produced a trajectory: unsuccessful ones are included, runs that errored or were stopped are not. Completion is the agent’s own report, not verified success. Speed is the median run time. Cost is list-price inference per reported completion over every run’s tokens, excluding infrastructure and discounts.
Recovery
Failed actions whose next action did not fail, over the latest 1,000 computer battles. A proxy for recovery, not task success.
What this covers
Operator experiments are excluded. The windows measure different things and should not be pooled. A preference is not a verified success label. Results refresh every five minutes.

MethodologyBenchmark dataCite Coarena