Gemini 2.5 Pro Preview 05 06: added the documented 1,048,576-token input limit
Google's latest Gemini 2.5 Pro preview officially reaches the million-token milestone for high-density data analysis.
Added the documented 1,048,576-token input limit — verified by the OnAirToday AI Progress Tracker against Google DeepMind's published documentation.
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The OnAirToday AI Progress Tracker recorded a verified change to Google DeepMind's Gemini 2.5 Pro Preview 05 06 (version gemini-2.5-pro-preview-05-06): Added the documented 1,048,576-token input limit.
Gemini 2.5 Pro Preview 05 06 is Google's May 2025 reasoning-model preview, focused on stronger coding, interactive web development, and multimodal understanding. Subsequent Gemini 2.5 Pro releases superseded it; the prices below are historical paid Gemini API rates in USD per million tokens for prompts up to 200,000 tokens.
What changed in this review:
- Added historical tiered Gemini API pricing, with thinking tokens included in output billing.
- Later Gemini 2.5 Pro previews and the stable June 2025 release superseded this version.
- Retained the published Aider Polyglot result without transferring benchmark scores from subsequent Gemini releases.
- Added a reported Gemini-service answer-poisoning campaign without attributing it to this exact preview.
- Flaws and limitations updated
- Risks and threats updated
Teams building on Gemini 2.5 Pro Preview 05 06 should confirm limits, pricing and benchmark coverage against the vendor's own documentation before relying on these figures.
The full profile — capabilities, benchmark citations, known flaws and risk guidance — is on the tracker at /ai-models/gemini-2-5-pro-preview-05-06.
References:
- https://developers.googleblog.com/en/gemini-2-5-pro-io-improved-coding-performance/
- https://ai.google.dev/gemini-api/docs/changelog
- https://ai.google.dev/gemini-api/docs/pricing
- https://ai.google.dev/gemini-api/docs/models
Generated insight by OnAirToday AI
Expanding the context window to one million tokens enables the processing of massive datasets and complex documents in a single prompt. This leap in capacity allows models to maintain deep coherence across lengthy information streams, significantly advancing multi-modal reasoning capabilities.

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