Public
avyayv / seqdataloader
Benchmark updated: 9/2/202630 Tasks
Sequence data label generation and ingestion into deep learning models
Languages
Python100%
Harness | |||||||
|---|---|---|---|---|---|---|---|
1 |  Mini-SWE-agent | $0.47 | 117.73s |
Key Takeaways
- This 30-task result is directional rather than statistically significant.
- The run averages 117.73 seconds per test, with 29,154.60 input tokens and 4,746.90 output tokens.
Cost Analysis
Cost / Test vs. Accuracy
ACCURACYCOST
Lab:
Pinch or Ctrl/Cmd + scroll to zoom. Once zoomed, scroll or swipe with two fingers to pan, or drag to pan. Shift + drag draws a box to zoom into; turn on box zoom to draw one without Shift. Plus and minus keys zoom, arrow keys pan, 0 resets.
Average Token Use / Test
Token Usage
InputOutputReasoningCache readCache write
GPT-5.6 Sol
Cost is the clearest tradeoff in this comparison. GPT-5.6 Sol leads at 76.67% for $0.47 per test. No other model in this comparison is cheaper.
Latency Analysis
Latency vs. Accuracy
ACCURACYLATENCY
Lab:
Pinch or Ctrl/Cmd + scroll to zoom. Once zoomed, scroll or swipe with two fingers to pan, or drag to pan. Shift + drag draws a box to zoom into; turn on box zoom to draw one without Shift. Plus and minus keys zoom, arrow keys pan, 0 resets.
Average Response Time / Test
Response Time
GPT-5.6 Sol
GPT-5.6 Sol is both the most accurate and fastest model in this comparison at 76.67% and 1m 58s.