Bug: Flash 2.5 transcription exhausts output tokens due to repeated `[unclear]`, resulting in truncated JSON (recent regression)

[ish-codes-magic]

Describe the bug
When using Flash 2.5 on Vertex AI for audio transcription with the google-genai package with batching enabled, the model repeatedly outputs the literal token [unclear]. This repetition consumes the entire max_output_tokens budget before transcription completes, causing the response to be truncated and resulting in invalid or incomplete JSON.

This behavior appears to be a recent regression. The same transcription pipeline was significantly more reliable approximately 1–1.5 months ago, with far fewer [unclear] repetitions and successful completion of JSON responses.

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Environment

• Platform: Vertex AI
• Model: Flash 2.5
• Library: google-genai
• Task: Audio transcription with batching
• Response MIME type: application/json
• Response schema: Enabled
• Thinking mode: Diabled

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Steps to reproduce

1. Send an audio file via file_uri with a transcription prompt
2. Enable structured JSON output using response_schema
3. Set max_output_tokens appropriate for the expected transcription length
4. Invoke Flash 2.5 on Vertex AI with batching

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Expected behavior

• The model should avoid excessive repetition of [unclear]
• The model should complete transcription within the token budget
• The model should consistently return a valid JSON response conforming to the schema

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Actual behavior

• The model repeatedly emits [unclear] segments
• Output tokens are exhausted before transcription completes
• JSON output is truncated or malformed

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Code snippet

parts = [
    {
        "file_data": {
            "file_uri": uri,
            "mime_type": self._get_mime_type(file_path)
        }
    },
    {
        "text": final_prompt
    }
]

generation_config = {
    "response_mime_type": "application/json",
    "temperature": transcription_config.TEMPERATURE,
    "max_output_tokens": transcription_config.get_max_output_tokens(model),
}

schema_class = get_transcription_result_class(model, phase)
if schema_class:
    if isinstance(schema_class, dict):
        generation_config["response_schema"] = schema_class
    else:
        schema_dict = schema_class.model_json_schema()
        schema_dict = self._resolve_json_schema_refs(schema_dict)
        schema_dict.pop("$defs", None)
        generation_config["response_schema"] = schema_dict

generation_config["thinking_config"] = {
    "thinking_budget": transcription_config.THINKING_BUDGET
}

instance = {
    "id": str(i - 1),
    "request": {
        "contents": [
            {
                "role": "user",
                "parts": parts
            }
        ],
        "generation_config": generation_config
    }
}

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Additional context

• Increasing max_output_tokens doesn't reduce the issue.
• The regression has been observed consistently over the last 1–1.5 months

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Questions

• Is this a known regression in Flash 2.5 transcription behavior?
• Are there recommended mitigations to prevent token exhaustion due to repeated [unclear] output?
• Is Flash 2.5 currently recommended for transcription workloads on Vertex AI?