C-05 · Feature Depth leadersarchive-recorded · snapshot 2026-09-17
Feature Depth Leaders: Top 25
The 25 highest feature depth scores (score.dims.features) 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
Feature Depth median
78
score.dims.features · N=448
Top score
94
score.dims.features · N=448
Leader
ChatGPT
score.dims.features · N=448
Correlation with overall
r=0.67
pearson(dim, overall) · N=448
Feature Depth — top 25 of 448
sort: dims desc, then overall desc, then slug; deviation = dim − overall
| # | Tool | Category | Feature Depth | Overall | Deviation |
|---|---|---|---|---|---|
| 1 | ChatGPT | Research | 94 | 90.9 | +3.1 |
| 2 | Cursor | Coding | 92 | 89.2 | +2.8 |
| 3 | GitHub Copilot | Coding | 90 | 88.1 | +1.9 |
| 4 | ElevenLabs | Audio & Voice | 90 | 87.8 | +2.2 |
| 5 | Poe | Research | 90 | 87.1 | +2.9 |
| 6 | n8n | Workflow Automation | 90 | 84.8 | +5.2 |
| 7 | Zapier | Workflow Automation | 90 | 84.3 | +5.7 |
| 8 | PlayHT | Audio & Voice | 89 | 89.2 | -0.2 |
| 9 | Claude | Research | 88 | 88.9 | -0.9 |
| 10 | Gemini | Research | 88 | 88.5 | -0.5 |
| 11 | Perplexity | Research | 88 | 88.4 | -0.4 |
| 12 | FLUX | Design | 88 | 88.3 | -0.3 |
| 13 | Canva | Design | 88 | 87.1 | +0.9 |
| 14 | Claude Code | Coding | 88 | 86.1 | +1.9 |
| 15 | Make | Workflow Automation | 88 | 84.7 | +3.3 |
| 16 | OpenAI Codex | Coding | 88 | 84.6 | +3.4 |
| 17 | Tavus | Video | 88 | 81.3 | +6.7 |
| 18 | AlphaSense | Research | 88 | 80.3 | +7.7 |
| 19 | Agentforce | Workflow Automation | 88 | 77.9 | +10.1 |
| 20 | Aider | Coding | 86 | 86 | +0 |
| 21 | Deepgram | Audio & Voice | 86 | 85.1 | +0.9 |
| 22 | Elicit | Research | 86 | 84.8 | +1.2 |
| 23 | AssemblyAI | Audio & Voice | 86 | 84.5 | +1.5 |
| 24 | Amazon Q Developer | Coding | 86 | 84.3 | +1.7 |
| 25 | Firecrawl | Research | 86 | 84.2 | +1.8 |
Feature Depth — top 10
Recorded score.dims.features of the ten leading archives (scale 0–100).
Method
- Population: all 448 archives in data/tools with a recorded score.dims.features.
- Ordering: dimension score descending; ties broken by overall descending, then slug ascending — a deterministic total order.
- Deviation column = score.dims.features − 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.