Anthropic's new model Fable is designed to handle complex tasks autonomously, leveraging its agentic coding capability. This feature enables it to find multiple paths to reach a goal, making it particularly effective in areas like modernisation and maintenance activities. However, the model is not without its limitations, as it can suffer from 'jagged intelligence'—a phenomenon where it excels in advanced tasks but falters in basic logic, spelling, and simple counting.

Fable has been positioned as a more affordable alternative to OpenAI’s GPT 5.5 Pro, priced 30% lower. Despite this cost advantage, the model currently faces challenges related to speed and token usage, which could impact its practicality for certain applications. These limitations highlight the ongoing trade-offs between cost, performance, and efficiency in the rapidly evolving field of large language models.

Fable has demonstrated impressive capabilities in specific use cases, such as migrating 50 million lines of code for Stripe in a single day—a task that would typically take at least two months of human effort. It has also shown potential in drug design, completing the process 10 times faster than humans, and even drafting an S-1 filing for the SpaceX IPO that was nearly identical to the actual document. These achievements underscore the model’s potential in specialized domains.

In India, the introduction of Fable could have significant implications for AI services firms and enterprises relying on large language models. The model’s affordability and advanced coding capabilities may provide a competitive edge for Indian builders, particularly in sectors such as healthcare, finance, and legal services. However, its current limitations in basic logic and token efficiency may require careful consideration when integrating it into enterprise workflows.

As Fable continues to develop, its impact on the AI landscape will depend on how effectively its limitations are addressed. While its agentic coding capability and cost advantages make it an attractive option for certain applications, ongoing improvements in speed, token efficiency, and general intelligence will be crucial for broader adoption. The model’s evolution will likely shape the competitive dynamics among AI providers and influence the trajectory of enterprise AI integration.