Python Model Report
This mini page turns the overnight suite into a quick visual read: which models were fastest, which ones improved after the cold first prompt, and which Python task types each model handled best.
Headline Winners
These are the standout models for the metrics that matter most in the overnight run.
Primary Prompt Latency
Average time to answer the main Python task prompt. Lower is better.
No rows were available for this chart.
Follow-up Prompt Latency
Average time to answer the follow-up summary request. Lower is better.
No rows were available for this chart.
Primary Prompt Throughput
Average tokens per second while answering the main Python tasks. Higher is better.
No rows were available for this chart.
Follow-up Prompt Throughput
Average tokens per second on the summary requests. Higher is better.
No rows were available for this chart.
Primary Quality
Average marker coverage on the full Python task prompts. Higher is better.
No rows were available for this chart.
Follow-up Quality
Average marker coverage on the follow-up summary prompts. Higher is better.
No rows were available for this chart.
Cold-Start Latency Delta
Positive values mean the first primary prompt was slower than the warmed-up average. Negative values mean the first prompt was actually faster.
No delta rows were available for this chart.
Cold-Start Throughput Delta
Positive values mean token generation sped up after the first primary prompt. Negative values mean throughput dropped on later prompts.
No delta rows were available for this chart.