Gemini 1.0 Ultra
Gemini 1.0 Ultra is an API model from Google. It’s positioned for general text tasks—work that benefits from iteration, not just one-shot answers.
Intelligence
Below avg
Context
33K
Up to 32,768 tokens
How are Intelligence, Speed & Cost bucketed?
Intelligence and Speed buckets are percentile ranks on
Artificial Analysis. Cost buckets are fixed dollar
thresholds keyed off output-token price ($/M out).
Intelligence
- Top 1%≤ 1%
- Top 5%≤ 5%
- Top 10%≤ 10%
- Good≤ 25%
- Medium≤ 50%
- Below avg> 50%
Speed
- Top 1%≥ 345 tok/s
- Top 5%≥ 237 tok/s
- Top 10%≥ 196 tok/s
- Good≥ 146 tok/s
- Medium≥ 90 tok/s
- Slow< 90 tok/s
Cost
- Freeopen weights · self-host
- Low< $1 / M out
- Moderate$1–5 / M out
- High≥ $5 / M out
Core Capabilities
Long Documents
Handles entire codebases, books, and multi-doc RAG.
Context Window
33k tokens
≈ 25 pages
4k Chat 聊天
32k This model 本模型
128k Books 整本书
400k Multi-doc 多文档
1M Codebase 整个代码库
10M
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
4.0
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
- Original Gemini 1 — historical; superseded by 1.5+.
- 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