Gemini 2.5 Pro
Google · Gemini 2.5
Stable high-capability Gemini 2.5 tier for long-context multimodal reasoning and enterprise workflows.
Overview
Freshness note: Model capabilities, limits, and pricing can change quickly. This profile is a point-in-time snapshot last verified on May 16, 2026.
Gemini 2.5 Pro is Google’s stable high-capability 2.5 tier for difficult multimodal and long-context tasks. Google’s May 2026 docs now put the Gemini 3 and 3.1 line above it, but 2.5 Pro remains an important production route because it is stable, broadly documented, and priced separately from the newer preview lanes.
Capabilities
The model is effective for large-context reasoning, technical analysis, multimodal interpretation, and harder coding or STEM-style reasoning tasks. It is frequently used for complex planning and assistant workflows that require higher quality across diverse inputs.
Technical Details
Google’s model docs still list Gemini 2.5 Pro with a 1,048,576 token input window and a 65,536 token output limit. It supports text, image, video, audio, and PDF inputs plus tooling features such as code execution, file search, function calling, search grounding, Google Maps grounding, URL context, and structured outputs.
Pricing & Access
Google’s current Gemini API pricing lists Gemini 2.5 Pro at 10 per 1M output tokens for prompts up to 200K tokens, with 15 above that threshold. Batch pricing is half that level, and context caching/storage pricing is published separately for the paid tier. Access is available through Google AI Studio and Vertex AI.
Best Use Cases
Best for complex enterprise copilots, multimodal document workflows, advanced retrieval assistants, coding-heavy analysis, and difficult reasoning tasks requiring long context when preview-model churn is not acceptable.
Comparisons
Compared with GPT-5.4, Gemini 2.5 Pro is often selected for Google ecosystem alignment and multimodal-heavy pipelines. Compared with Gemini 3.1 Pro Preview, 2.5 Pro is the safer stable route while 3.1 is the newer preview model. Compared with Claude Opus 4.6, tradeoffs usually center on reasoning style and platform integration.