Google's Gemini 3.1 Pro Rollout Marred by User Issues
Despite its official launch with improved reasoning capabilities, Google's Gemini 3.1 Pro is reportedly suffering from significant usability problems. Users have reported 99-hour account lockouts, phantom quota drains, and broken tool-calling functionality in popular frameworks like LangChain. While new third-party SDKs like Pydantic-AI have added support for the model, these reliability issues are hampering its adoption in production and embedded automation workflows.
- The 99-hour account lockouts affecting some paying subscribers are reportedly due to a bug that incorrectly applies free-tier usage limits to their accounts after the update to Gemini 3.1 Pro. Numerous users have posted on Google's AI developer forums about these multi-day lockouts. - A primary cause of the broken tool-calling functionality is the new mandatory "thought_signature" in the API. This encrypted token represents the model's reasoning state and must be sent back in subsequent requests, a change that broke many existing open-source frameworks that were not updated to handle it. - Developers have also documented a "phantom quota drain" where usage limits deplete even with no activity, and have noted that Gemini 3.1 Pro can consume quota nearly twice as fast as the previous version for the same tasks. Google has not provided an official explanation for this reported behavior. - In response to some account restrictions, Google's lead for the Antigravity platform, Varun Mohan, stated there was a "massive increase in malicious usage" that negatively impacted service quality, necessitating quick action to block users who were not using the product as intended. This does not, however, directly address the widespread bug reports from paying subscribers. - A specialized "customtools" version of the Gemini 3.1 Pro API is available to force the model to prioritize developer-defined tools over its default ability to execute general bash commands. - The unreliability of large language models, including API instability and the potential for factually incorrect "hallucinations," poses significant hurdles for their use in embedded systems where predictable and safe operation is often a critical requirement. - The rollout problems have led some developers to move to competing models, such as Anthropic's Claude. Some have adopted a "plan-in-Gemini, execute-in-Claude" workflow, with some finding it more efficient to use Claude for the entire process. - The Pydantic-AI library provides type-safe methods for building AI agents in Python. By using Python's type hints to define schemas, it helps ensure that the data produced by AI workflows is structured and predictable, which is crucial for integration into production systems.
