In this article, pws7438 discusses personal experiences comparing GitHub Copilot’s Beastmode-3.1 and Sonnet 4 models in VS Code, focusing on tool-calling and code commit reliability.

User Review: Beastmode-3.1 vs. Sonnet 4 in GitHub Copilot

Author: pws7438

Background & Use Case

The author, an experienced architect and manager, shares insights from personal embedded software and hardware projects, primarily using Visual Studio Code (VS Code) with GitHub Copilot on the Pro plan. Having moved away from professional coding but regularly developing for personal projects, the author observes recent changes in Copilot’s available models and rate limits.

Experience with Sonnet 3.7 and 4

Prior to the recent model and rate limit updates, Sonnet 3.7 and later Sonnet 4 provided a strong developer experience. These models were noted for their structured approach, accuracy in tool calls, and thorough execution. The author credits these models for delivering results aligned with actual software development practices, especially in areas like code structure and tool integration.

Testing Beastmode-3.1 and GPT-4.1

In response to rate limit changes, the author began testing Beastmode-3.1 alongside GPT-4.1 to compare practical output. The verdict was negative: Beastmode-3.1 was found to be “lazy” and consistently failed at basic tasks, notably in tool-calling scenarios such as committing code and pushing to GitHub. Commands would execute, but with no actual effect, and feedback from the model was minimal and uninformative. In contrast, switching to Sonnet 4 resulted in immediate, accurate execution and more detailed commit comments.

Insights on Model Training and Prompting

The article highlights how proper prompting contributes to outcome quality. However, the author likens asking GPT-4.1 to “be a senior software developer” to asking an actor to impersonate one: both may simulate the role, but lack authentic, practical thinking. This metaphor illustrates frustration with models that only approximate developer reasoning. Sonnet 4, in the author’s view, seems better trained to replicate an actual developer’s logic and problem-solving approach, possibly due to differences in training data or objectives.

Model Selection for Productive Development

The author advises that, while rate limits and credit consumption may shape model usage, developers and teams must select models capable of meaningful automation and integration into real-world development workflows. Having effective tool calls and authentic developer logic in model output is essential for future productivity gains.

Conclusion

Despite personal limitations on credits, the author expresses a preference for using GitHub Copilot paired with Sonnet 4 due to superior reliability and developer-aligned output. The article encourages objective assessment and tool selection as critical for long-term software development effectiveness.

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