On September 3 (Eastern Time), OpenAI officially rolled out GPT-6 Astra, touting it as the current flagship model that embodies "unparalleled intelligence and alignment." This model is tailored for diverse application scenarios, encompassing computer usage and software engineering. Since its debut, the market has greeted it with enthusiasm, with industry experts perceiving it as a direct counter to the advancements made by Anthropic's related models. In various evaluations, GPT-6 Astra has showcased exceptional prowess, particularly excelling in real-world software environments and professional workflow execution.

However, the model has not been without its share of controversies. Concerns have been raised regarding its General Intelligence Index, which has not fully eclipsed Claude Fable 5.1. Additionally, there has been an uptick in token pricing and a perceived decline in the transparency of chain-of-thought reasoning monitoring. The market has crystallized four key consensus points regarding GPT-6 Astra:

1. The focal point of model competition has now shifted to end-to-end execution capabilities, with computer usage and long-range agents emerging as the new frontiers of competition.
2. Leading models continue to wield pricing power, commanding premium prices through their high task completion rates.
3. The rationale for sustained demand for computing power has been further solidified.
4. OpenAI has significantly narrowed the gap with Anthropic in the coding domain, bringing the two companies back to a neck-and-neck competition.

Disagreements primarily revolve around the impact of architectural changes on storage requirements, the trajectory of market influence, the credibility of benchmarks, and safety constraints. At the technical level, GPT-6 Astra has achieved three pivotal advancements:

• During the training phase, leveraging over 100,000 GPUs, it pioneered the large-scale incorporation of previous-generation models in training supervision, showcasing the prototype (early form) of recursive self-improvement.
• The technical architecture may embrace Recurrent Depth or Looped Transformer architectures, which reduce explicit token consumption but escalate safety monitoring costs.
• The product capabilities are centered on computer usage, enabling it to adeptly handle various software operations, toolchains, and business process tasks.

In terms of pricing, the standard API price for GPT-6 Astra has surged by approximately 2.5 times compared to GPT-5.6 Sol. Nonetheless, OpenAI underscores single-task costs, highlighting that in specific tasks, Astra's API costs are lower than those of its competitors. However, this cost advantage is contingent on specific scenarios and may not hold in general-purpose Q&A scenarios.

Previously, coding capabilities were at the heart of model competition. However, with the advent of GPT-6, the focus has pivoted to end-to-end task delivery capabilities, with models evolving from "answerers" to "executors." The market is undergoing stratification, with flagship models shouldering complex, high-value tasks, while mid-range and smaller models carve out their respective niches.

Domestic leading models had previously reached parity with the previous generation's cutting-edge models. However, the release of GPT-6 has widened the execution-level gap. Domestic models still exhibit shortcomings in long-range agents, computer usage, and closed-loop execution, but these are not insurmountable. Relevant technologies are already buttressed by academic papers and open-source practices, and domestic models are rapidly catching up in post-training and engineering optimization.

The stock prices of related model companies have remained relatively stable, showing no significant fluctuations in the wake of GPT-6 Astra's release.