I’ve been actively using Gemini Pro in Google AI Studio, particularly leveraging its advertised 1 million token context window — a major attraction for handling long documents, chat memory, or agent workflows.
However, I’ve consistently encountered “Out of tokens” or related memory errors around the 500,000 token mark, well below the stated capacity. This discrepancy poses real limitations for developers building on top of Gemini’s long-context promise.
Problem Observed
- Environment: Google AI Studio (Gemini Pro)
- Expected behavior: Support up to 1M tokens in prompt + history
- Actual behavior: Errors begin showing at ~450K–500K tokens, often halting further processing or output generation.
This isn’t just an edge case — it’s repeatable in various sessions, even when inputs are well-structured (e.g., large but clean document prompts with straightforward questions).
Why This Matters
The promise of 1M tokens is a game-changer for:
- Enterprise-level summarization
- Long-term memory agents
- Legal, scientific, or code base analysis
But hitting a wall at half that effectively undermines the use case, especially when devs are building workflows assuming full-range support.
Suggested Areas for Clarification or Improvement
- Clarify real usable token limit inside AI Studio:
- Distinguish between model capability vs environment constraints.
- Improve memory handling in Studio runtime:
- Offload history rendering or cache segments intelligently.
- Expose token usage stats and thresholds:
- Let devs see how close we are to hitting limits.
Call to Action
This needs to be addressed — either through:
- Documentation updates
- Studio improvements
- Direct feedback from the Gemini product team
If anyone from the @GoogleDeepMind or @GoogleAI team can weigh in, it would help a lot of developers plan realistic solutions around Gemini’s current capabilities.