Basis 用 GPT-6 Astra 将税务工作簿处理速度提升 2 倍
Basis completes a tax workbook 2x faster with GPT-6 Astra
OpenAI 客户 Basis 测试显示，GPT-6 Astra 完成一份 50 个标签页的复杂税务工作簿耗时比 GPT-5.6 Sol 少 50%，其内部评测分数提升约 20%。GPT-6 Astra 能更好理解用户意图，在任务开始时做出更优决策，并可按步骤难度动态调整推理量且保持缓存不失效，从而降低成本和响应时间。
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Basis completes a tax workbook 2x faster with GPT-6 Astra | OpenAI
September 28, 2026
Basis completes a tax workbook 2x faster with GPT‑6 Astra
GPT‑6 Astra took 50% less time to finish complex tasks vs. GPT‑5.6 Sol and showed a deeper understanding of user intent.
00:00 01:04
Company size: Startup
Region: North America
Industry: Technology
Products: API
Workbook test
50%
Less time to complete a 50-tab tax workbook vs. Sol in Basis’s test.
Internal evaluations
20%
Approximate improvement in Basis’s internal evaluation scores.
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Completing a 50-tab tax workbook in half the time
- Completing a 50-tab tax workbook in half the time
- Matching reasoning effort to the task
- Building confidence in real-world use
- Completing a 50-tab tax workbook in half the time
- Matching reasoning effort to the task
- Building confidence in real-world use
Basis(opens in a new window) builds AI agents to automate much of the manual work that accountants do each day, helping them shift their time from repetitive tasks to strategic work. The company’s research focuses on agents that can reliably complete long tasks, and with GPT‑6 Astra, it’s seeing a stronger understanding of what accountants want to accomplish.
“GPT-6 Astra does a better job of really understanding the intent of the user and the problem.”
—Mitch Troyanovsky, Co-founder, Basis
Completing a 50-tab tax workbook in half the time
Basis compared GPT‑6 Astra and GPT‑5.6 Sol on a complicated tax workbook with 50 tabs. The task was to complete the workbook accurately and reliably, and GPT‑6 Astra was markedly faster.
“GPT-6 Astra is able to complete that workbook in half the time that GPT-5.6 Sol is able to.”
—Mitch Troyanovsky, Co-founder, Basis
Basis also noted that GPT‑6 Astra makes better decisions at the start of a task, helping Basis’s agents take a more direct path through the work with less time spent correcting mistakes. Troyanovsky says that also makes the model more efficient in its use of tokens.
Matching reasoning effort to the task
Basis also has GPT‑6 Astra adjust how much reasoning it uses as a task progresses, dialing up computation when a step is difficult, and using less when a step is easier. The model can make these adjustments while keeping its cache intact. Troyanovsky says this helps reduce cost and response time, making long-running tasks more economical for Basis and its customers.
Building confidence in real-world use
Basis saw about a 20% improvement in its internal evaluation scores with GPT‑6 Astra, driven by better understanding of user intent, including when to ask questions, flag assumptions, and follow instructions.
Basis evaluates how its agents work and their final answers, including whether they follow templates, consult primary sources for tax questions, and check their own work. GPT‑6 Astra can infer these expectations from a broader context, with fewer explicit instructions.
That reduces the need for Basis to write rules for individual situations and gives the team more confidence that its agents can handle situations beyond those covered in internal tests.
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