o3-mini
o3-mini is an API model from OpenAI. It’s positioned for general text tasks—work that benefits from iteration, not just one-shot answers.
Intelligence
Medium
Speed
Good
161 tok/s output
Cost
Moderate
$1.10 in / $4.40 out
Context
200K
Up to 200,000 tokens
How are Intelligence, Speed & Cost bucketed?
Intelligence and Speed buckets are percentile ranks on
Artificial Analysis. Cost buckets are fixed dollar
thresholds keyed off output-token price ($/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
200k tokens
≈ 154+ pages
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
200k
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
6.7
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
- Step-change on ARC-AGI-1 — 75.7% at the public $10K compute limit, 87.5% at high compute (172x)
- But ARC-AGI-2 (March 2025) cratered the narrative: <3% on the harder benchmark vs 60% for the average human
- The released April-2025 o3 is NOT the December preview — preview had ARC-AGI-1 in its training, public version is more honest
Best use cases
- Frontier reasoning research and benchmark exploration
- Hard math (AIME, IMO-style) and graduate-level science (GPQA)
- Code that needs novel algorithm design rather than scaffolded refactors
Tools to try
Not ideal for
- Latency-sensitive chat — o3-mini or GPT-4o are far faster
- Tasks framed slightly outside training distribution — Apple's variable-rename study still applies
Model Evolution
o is OpenAI's language model family.