OpenAI o1 vs OpenAI o3-mini
Detailed performance comparison across SWE-bench Verified, LiveCodeBench, AIME, GPQA Diamond, pricing per million tokens, and hardware requirements.
Flagship deep reasoning model trained with reinforcement learning for frontier science, math, and coding.
Next-generation cost-efficient reasoning model optimized for STEM, competitive math, and coding.
Key Strengths & Architectural Trade-offs
Direct synthesis of practical production advantages and limitations.
OpenAI o1
- 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
- Premium pricing ($15 / $60 per million tokens)
- Higher time-to-first-token latency due to chain-of-thought generation
OpenAI o3-mini
- Exceptional cost-to-reasoning ratio ($1.10 / $4.40 per MTok)
- 87.3% on AIME 2024 (high effort)
- Full support for tool calling, structured output, and developer messages
- Text-only model; vision inputs are not supported
- Reasoning token usage consumes output token budget
Recommended Use Cases by Workload
- •Frontier scientific research and biotech modeling
- •Complex algorithmic design and cryptography analysis
- •Complex legal contract logic verification
- •Competitive math & algorithmic coding pipelines
- •Automated code review & unit test synthesis
- •High-complexity agent reasoning steps at affordable cost
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.
Select 2 to 5 Models to Compare
Compare benchmark scores, architecture, context limits, pricing, and hardware requirements side-by-side.
Choose from Model Catalog (18 models)
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