Parallel（在新窗口中打开）为在网络上进行知识工作的AI代理构建开发者基础设施。其工具支持从语音代理的网络基础到金融机构和法律客户的研究等一切事务，将前沿模型与网络搜索相结合。
对于Parallel运行时间最长的研究任务，获得高质量答案通常意味着使用具有扩展推理能力的更大模型，这会消耗更多时间和资源。该公司借助GPT‑6 Astra在时间和成本上实现了重大改进。
“借助Astra，我们已经证明，你可以用少得多的研究调用和更少的token，更快、更快地获得同样高质量的研究结果。”
——Devin Gupta，Parallel Web Systems技术员工
以两倍速度汇编六个月的数据
在GPT‑6 Astra的一项测试中，Parallel要求其代理研究四个州在六个月内六项不同的劳动力市场统计数据。该代理必须在多个网站之间搜索、收集信息，并将发现汇编成一份研究报告。
GPT‑6 Astra能够在先前模型一半的时间内完成这项工作，代码成本大约降低50%，同时提供相同质量的研究结果。
以更少步骤获得高质量答案
Parallel还观察到，GPT‑6 Astra进行了更有针对性的搜索，并以更少步骤获得有用结果。
“与之前的模型相比，Astra发出了更有针对性的搜索查询，并更好地专注于最终任务，同时融入了其世界知识。”
——Devin Gupta，Parallel Web Systems技术员工
效率的提升也使Parallel更实际地将研究分配给多个代理。GPT‑6 Astra可以将特定研究任务委派给子代理，使工作能够同时进行，并减少在单一搜索序列中花费的时间。
Parallel现在拥有了一条从复杂问题到经过研究的答案的更好路径，等待时间更少、成本更低，并且有更多空间来大规模处理高要求的研究任务。
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.
“With Astra, we’ve demonstrated that you can get the same high-quality research much, much faster with fewer research calls and less tokens.”
—Devin Gupta, Member of Technical Staff, Parallel Web Systems
Compiling six months of data twice as fast
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.
Reaching high-quality answers in fewer steps
Parallel also observed that GPT‑6 Astra made more focused searches and took fewer steps to reach a useful result.
“Astra issued more targeted search queries and focused on the ultimate task better, incorporating its world knowledge compared to previous models.”
—Devin Gupta, Member of Technical Staff, Parallel Web Systems
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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