OpenAI o1
Flagship deep reasoning model trained with reinforcement learning for frontier science, math, and coding.
200k
Tokens
100k
Output limit
Reinforcement Learning Reasoning Model
Model family
Not Disclosed
Total / Active
$15.00
Per 1M tokens
$60.00
Per 1M tokens
Model Overview
OpenAI o1 generates private chain-of-thought tokens before responding, allowing it to think through complex multi-step reasoning, self-correct errors, and excel in competitive mathematics (AIME), scientific research (GPQA Diamond), and coding.
Developer Implementation Notes
Reasoning tokens are billed as output tokens ($60/MTok). Supports reasoning effort parameters (`low`, `medium`, `high`).
Key Strengths
- Superhuman-level performance on competition mathematics (AIME) and GPQA
- Excels at intricate logic puzzles, algorithm design, and security audit
- 200k context window with up to 100k output tokens
Limitations & Boundaries
- Premium pricing ($15 / $60 per million tokens)
- Higher time-to-first-token latency due to chain-of-thought generation
Capabilities & Modalities
Best Production Use Cases
- Frontier scientific research and biotech modeling
- Complex algorithmic design and cryptography analysis
- Complex legal contract logic verification
Verified Benchmark Results
Standardized evaluations with methodology notes and authoritative citation links.
48.9%
Pass@1, high reasoning effort without external agent scaffolding
83.3%
Pass@1 with high reasoning effort (12.5/15 problems solved)
19.5%
Zero-shot with high reasoning tokens on CAIS benchmark
Pricing & Inference Cost Calculator
Token Cost Estimator – OpenAI o1
Calculate projected inference spend with prompt caching
Quick Workload Presets
$30.0000
2.00M input tokens
$30.0000
0.50M output tokens
$60.00
Avg: $0.06000 / req
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Data Accuracy & Verification Notice
AI model specifications, pricing records, and benchmark metrics published on this platform are compiled directly from authoritative sources (official provider documentation, research papers, and verified evaluation harnesses). Benchmark results reflect specific test harnesses and prompting methodologies; scores are not directly comparable across differing evaluation setups.