Artificial Analysis Makes Time per Task Its Headline Performance Metric: GPT-6 Astra Finishes a Max-Effort Task in About 17 Minutes, Ahead of Opus 5.5 and Gemini 4 Carbon
Independent benchmarker Artificial Analysis announced on X on November 4 (17:34 UTC) a new version of its API performance benchmarking: the headline metric moves from output speed to Time per Task, the time to finish one Intelligence Index task (output tokens per task divided by end-to-end output speed). It covers about 1,000 serverless endpoints, measures speed from sending the request to the final token, and adds multi-turn agentic coding and knowledge-work workloads. Per its data, GPT-6 Astra at max effort runs at 51 tokens/s yet finishes a task in about 17 minutes, against about 26 for Claude Opus 5.5 (max) and about 29 for Gemini 4 Carbon (high).
Key Takeaways
- Time per Task equals output tokens per Intelligence Index task divided by end-to-end output speed, so a model that is fast per token but verbose can finish later than a slower model that uses fewer tokens.
- End-to-end output speed runs from sending the request to the final token, including the wait before the first token (prefill and hidden reasoning); output tokens now come from each provider's API-reported counts instead of the o200k_base tokenizer.
- GPT-6 Astra (max) runs at 51 tokens/s with about 50k output tokens per task and takes about 17 minutes; Claude Opus 5.5 (max, 111 tokens/s) takes about 26 minutes and Gemini 4 Carbon (high, 71 tokens/s) about 29, using roughly 120k to 170k tokens per task.
- At each lab's API default effort (medium for all three), GPT-6 Astra, GPT-6.1 Sol and Opus 5.5 cut Time per Task by about 3 to 5x and Cost per Task by about 2 to 4x, scoring about 5 to 11 points lower; Opus 5.5 drops from about 26 minutes and $10.04 to about 6.8 minutes and $2.45.
- The official methodology page lists this as version 3.0.0 (October 30), with runs now every 6 hours and a new endpoint error rate metric.
Linked Models & Agents:
