GPT‑6 Astra allowed Parallel’s agents to research and synthesize labor-market data in half the time and at half the cost vs. prior models.

Parallel⁠(opens in a new window) builds developer infrastructure for AI agents that do knowledge work over the web. Its tools support everything from web grounding for voice agents to research for financial institutions and legal customers, combining frontier models with web search.

For Parallel’s longest-running research tasks, getting a high-quality answer typically meant using a bigger model with extended reasoning, which consumed more time and resources. The company has seen a major improvement in time and cost with GPT‑6 Astra.

In one test of GPT‑6 Astra, Parallel asked its agent to research six different labor-market statistics across four states over six months. The agent had to search across multiple websites, collect information, and compile the findings into a single research report.

GPT‑6 Astra was able to complete the work in half the time of prior models, with roughly 50% code cost reduction, while delivering the same quality of research.

Parallel also observed that GPT‑6 Astra made more focused searches and took fewer steps to reach a useful result.

The increased efficiency also makes it more practical for Parallel to divide research among multiple agents. GPT‑6 Astra can delegate specific research tasks to sub-agents, allowing work to happen simultaneously and reducing the time spent moving through a single sequence of searches.

Parallel now has a better path from a complex question to a researched answer, with less time waiting, lower costs, and more room to tackle demanding research tasks at scale.

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