C-04 · Output Quality leadersarchive-recorded · snapshot 2026-09-17
Output Quality Leaders: Top 25
The 25 highest output quality scores (score.dims.quality) across all 448 archives, each shown against its overall score so the deviation is visible. Aggregation is a plain sort of recorded values — no re-weighting; method and N are published below.
Key numbers
Archives ranked
448
data/tools · N=448
Output Quality median
80
score.dims.quality · N=448
Top score
96
score.dims.quality · N=448
Leader
Midjourney
score.dims.quality · N=448
Correlation with overall
r=0.82
pearson(dim, overall) · N=448
Output Quality — top 25 of 448
sort: dims desc, then overall desc, then slug; deviation = dim − overall
| # | Tool | Category | Output Quality | Overall | Deviation |
|---|---|---|---|---|---|
| 1 | Midjourney | Video | 96 | 85.7 | +10.3 |
| 2 | ElevenLabs | Audio & Voice | 95 | 87.8 | +7.2 |
| 3 | Claude | Research | 94 | 88.9 | +5.1 |
| 4 | Claude Code | Coding | 94 | 86.1 | +7.9 |
| 5 | ChatGPT | Research | 93 | 90.9 | +2.1 |
| 6 | Cursor | Coding | 93 | 89.2 | +3.8 |
| 7 | Google Veo | Video | 92 | 83.2 | +8.8 |
| 8 | PlayHT | Audio & Voice | 91 | 89.2 | +1.8 |
| 9 | Perplexity | Research | 91 | 88.4 | +2.6 |
| 10 | OpenAI Codex | Coding | 90 | 84.6 | +5.4 |
| 11 | Nano Banana | Design | 90 | 83.4 | +6.6 |
| 12 | AlphaSense | Research | 90 | 80.3 | +9.7 |
| 13 | Respeecher | Audio & Voice | 90 | 79.7 | +10.3 |
| 14 | Magnific AI | Design | 90 | 77.4 | +12.6 |
| 15 | Sora | Video | 90 | 76.5 | +13.5 |
| 16 | Gemini | Research | 89 | 88.5 | +0.5 |
| 17 | FLUX | Design | 89 | 88.3 | +0.7 |
| 18 | GitHub Copilot | Coding | 88 | 88.1 | -0.1 |
| 19 | Zed | Coding | 88 | 86.1 | +1.9 |
| 20 | Aider | Coding | 88 | 86 | +2 |
| 21 | Cartesia | Audio & Voice | 88 | 85.8 | +2.2 |
| 22 | DeepL Write | Writing | 88 | 85.8 | +2.2 |
| 23 | Adobe Podcast | Audio & Voice | 88 | 85.6 | +2.4 |
| 24 | NotebookLM | Research | 88 | 85.2 | +2.8 |
| 25 | Deepgram | Audio & Voice | 88 | 85.1 | +2.9 |
Output Quality — top 10
Recorded score.dims.quality of the ten leading archives (scale 0–100).
Method
- Population: all 448 archives in data/tools with a recorded score.dims.quality.
- Ordering: dimension score descending; ties broken by overall descending, then slug ascending — a deterministic total order.
- Deviation column = score.dims.quality − score.overall per archive; positive values lead their own overall.
- Pearson r between the dimension and overall is computed over all 448 pairs (standard formula, rounded to 2 decimals).
- Scores are quoted as recorded in each archive; nothing is recomputed or re-weighted.