Problem with gemini-2.5-flash after 1.5-flash deprecation. Output JSON problem

Understanding and Overcoming JSON Output Challenges with the Gemini 2.5 Flash Model

In the evolving landscape of AI-driven applications, integrating APIs seamlessly is crucial for delivering precise and reliable outputs. Recently, developers leveraging the Gemini API have encountered notable challenges following the deprecation of the 1.5-flash model and the transition to the 2.5-flash version. This article explores the common issues faced, particularly concerning JSON output formatting, and offers insights on strategies to address them.

The Challenge: Unintended Output Formats in Gemini 2.5-Flash

Developers utilizing the Gemini API often rely on prompt engineering to dictate the format and content of AI responses. Previously, with the 1.5-flash model, achieving structured JSON outputs was straightforward, provided clear instructions were included in the prompt.

However, with the release of the 2.5-flash model, users have observed inconsistent behaviors. Even when explicitly instructing the model to respond in a specific JSON format—for example, requesting questions generated from a PDF—the output frequently deviates. Instead of adhering to the desired structure, the AI may append extraneous information such as token count, summaries, or other unintended elements.

This behavior poses significant challenges for developers aiming for predictable data extraction and downstream processing. The core issue appears to stem from differences in how the newer model interprets prompt instructions or perhaps a need to fine-tune prompt phrasing.

Potential Causes and Considerations

1. Model Interpretations: The 2.5-flash version may prioritize generating comprehensive responses, including contextual summaries, unless explicitly constrained.

2. Prompt Clarity: The instructions might require more explicit or structured prompts to reinforce the expected output format.

3. Temperature and Parameters: Adjusting parameters such as temperature, top_p, or top_k can influence the randomness and specificity of responses, potentially aligning outputs closer to expectations.

Strategies for Achieving Desired JSON Responses

• Explicit and Structured Prompts: Clearly delineate the response format in your prompts. For instance, specify the JSON schema at the outset and reinforce that no additional information should be included.

• Use of System Prompts: If supported, employ system-level instructions to guide the model’s behavior more effectively.

• Post-processing: Implement parsing and validation steps to extract the relevant JSON content from broader responses, handling cases where the model diverges.

• Parameter Tuning: Experiment with lower temperature settings to promote more deterministic