Gemini 1.5 Pro (May '24)
Gemini 1.5 Pro (May '24) is an API model from Google. It’s positioned for general text tasks—work that benefits from iteration, not just one-shot answers.
Context
2M
Up to 2,000,000 tokens
Core Capabilities
Long Documents
Handles entire codebases, books, and multi-doc RAG.
Context Window
2M tokens
≈ entire codebase
4k Chat 聊天
32k Long docs 长文档
128k Books 整本书
400k Multi-doc 多文档
1M Codebase 整个代码库
10M
2M
Availability
API
Available
Product / App
Not available
Open Source
Not released
Enterprise
Contact sales
Pricing Model
Pay per token
Input and output billed separately.
Pay-per-token Capability / Performance
Where this model sits relative to the middle 60% of models in the tree. All scores are 0–10 (higher is better).
Lower 20% Upper 80% This model
Context / memory
Context window size · log-scaled
6.0
9.0
10.0
Lower 20% 20th percentile — 20% of models score below this This model Where the current model lands Upper 80% 80th percentile — only 20% of models score above this
Percentile boundaries are computed across every model in the tree that reports the underlying benchmark for each capability.
What it feels like
- First model where 1M-token context was a real product feature, not a benchmark headline
- 99% needle-in-haystack accuracy at 1M tokens; 99.2% even at 10M in research configurations
- Multi-needle recall drops to ~60% — single fact retrieval is solid, multi-fact is harder
Best use cases
- Whole-codebase analysis (30K+ lines) without tedious chunking pipelines
- Document QA over 1,500-page PDFs / batches of 100 emails
- Hour-long video summarisation and audio transcription QA
Tools to try
Not ideal for
- Tasks requiring multi-fact retrieval across long contexts (recall drops sharply)
- Pure short-context chat — Flash variants are cheaper and faster