Artificial Analysis Makes Time per Task Headline
Artificial Analysis replaced tokens per second with Time per Task as its headline performance metric, incorporating full-request latency and agentic workloads. Claude Opus 5.5 streams over twice as fast as GPT-6 Astra yet takes roughly seven minutes longer to finish its benchmark tasks because it generates far more tokens.
This is a necessary correction: streaming speed measures how quickly a model types, not how quickly it completes useful work.
- –Opus reaches roughly 112 tokens per second versus Astra’s 47, but takes 25.8 minutes per task versus 18.4 minutes.
- –Astra’s advantage comes from generating dramatically fewer tokens, highlighting the cost of verbosity and extended reasoning.
- –The new metric better matches coding agents and knowledge-work systems, where latency, reasoning length, and multi-turn execution compound.
- –It remains a derived benchmark estimate rather than a stopwatch measurement of every production workflow.
- –Developers should compare models at their intended API effort level, since default and maximum effort can produce radically different speed, cost, and quality tradeoffs.
DISCOVERED
1h ago
2026-11-04
PUBLISHED
2h ago
2026-11-04
RELEVANCE
AUTHOR
AdamHoltererer