Persistent Failures in Google’s Gemini AI Model

Gemini, Google’s family of multimodal artificial intelligence models, is positioned as a competitor to GPT-4 and other cutting-edge AI systems. Since its announcement in December 2023 and subsequent updates (Gemini 1.5, 2.0, 2.5 Pro), users have had high expectations for this technology, capable of processing text, images, audio, and code. However, in 2025, Gemini faces criticism due to persistent failures, unstable performance, and issues with response quality.

Key Issues with Gemini

1. Server Overloads and API Errors
Users, particularly developers using Gemini via its API, report frequent errors such as "503: The model is overloaded." For instance, in April 2025, widespread complaints on the X platform highlighted API instability, rendering services dependent on Gemini unavailable. These failures are linked to high demand for computational resources and Google’s insufficient scalability to handle peak loads.

2. Language Mixing in Responses
One of the most discussed issues is the incorrect mixing of languages in Gemini’s responses. Users from the Russian-speaking community complain that responses include fragments in Korean, Russian, or other languages, even when explicitly instructed to use a single language. This creates inconvenience, requiring additional checks and query refinements. The issue likely stems from the model’s multimodal nature and errors in language data processing.

3. Instability in Handling Complex Queries
Despite a claimed context window of 1 million tokens, Gemini sometimes delivers inaccurate or incomplete responses, particularly when analysing large datasets or generating code. Users note that the model may "freeze" in "thinking" mode or produce fabricated results, indicating issues with search function integration and internal processing algorithms.

4. Limited Availability and Regional Restrictions
In Russia, access to Gemini is restricted due to geographic blocks, forcing users to rely on VPNs. This adds inconvenience and increases latency, especially when using the mobile app or integrating with Google Workspace services.

Causes of Failures

- High Infrastructure Load: Gemini is heavily used in Google Workspace, mobile apps, and via API, leading to server overloads, particularly during peak hours. Google has invested in TPUv5 chips, but scaling computational capacity appears to lag behind user base growth.
- Multimodality Complexity: Simultaneous processing of text, audio, images, and video requires complex algorithms that remain imperfect. This can lead to errors in language processing or content generation.
- Insufficient Debugging: Rapid deployment of new versions (e.g., Gemini 2.5 Pro) without thorough testing results in unpredictable failures. Users on X note that the model feels "raw" compared to competitors like Claude 3 or GPT-4.
- Search Integration Issues: Some users report that Gemini’s search function operates unstably, occasionally producing simulated results instead of real data. This reduces trust in the model for research tasks.

Google’s Response and Potential Solutions

Google acknowledges the issues and is actively working to address them. In April 2025, the company updated its documentation, emphasising efforts to enhance Gemini’s stability and security. Steps being taken include:

- Server Optimisation: Google is expanding its TPU Ironwood infrastructure and cloud services to support growing demand.
- Language Model Improvements: Developers promise to fix language mixing in upcoming updates by refining multimodal data processing algorithms.
- Expanded Beta Testing: Google encourages users to report failures via built-in feedback tools to identify and resolve errors faster.
- New Version Development: Gemini Ultra 1.5 and Gemini 2.5 Flash, announced in 2025, are expected to be more stable and efficient.

Comparison with Competitors

In 2024, Gemini 1.5 Pro lagged behind GPT-4 and Claude 3 in some benchmarks, partly explaining the pressure on Google to accelerate updates. However, Gemini 2.5 Pro, according to Habr, leads in several tests, such as LMArena and AIME, indicating progress. Nevertheless, competitors like ChatGPT encounter fewer issues with language mixing or server overloads, making them more reliable for users in 2025.

Conclusion

Google’s Gemini holds immense potential due to its multimodality and integration with the Google ecosystem, but persistent operational failures undermine user trust. Server overloads, language processing errors, and API instability are critical issues requiring urgent resolution. Google is actively working on improvements, and with the release of new versions like Gemini 2.5 Flash, the situation may improve. For now, users must contend with imperfections or seek alternatives like ChatGPT or Claude. Those reliant on Gemini are advised to monitor updates and use feedback tools to help Google address shortcomings.

For more information, visit the official Google AI website.