OpenAI o3-mini vs o1: Benchmark Accuracy, Token Costs, and Latency Analysis
Technical comparison of o3-mini vs o1 reasoning speeds, API pricing, and code generation performance.
70%Key Fact
3xMarket Impact
$1.10**Output Cost
$4.40**SWE-Bench Verified**
Benchmark & Cost Matrix
When evaluating reasoning models for production deployment, token cost and latency are as critical as raw accuracy. OpenAI o3-mini provides a compelling balance for technical teams.
Comparative Breakdown
| Metric | OpenAI o1 | OpenAI o3-mini (High) | OpenAI o3-mini (Low) |
|---|---|---|---|
| Input Cost / 1M Tokens | $15.00 | $1.10 | $1.10 |
| Output Cost / 1M Tokens | $60.00 | $4.40 | $4.40 |
| SWE-bench Verified | 48.9% | 49.1% | 41.2% |
| Average Response Time | ~14.2s | ~4.8s | ~1.6s |
| Function Calling Support | Yes | Yes | Yes |
Check exact budget projections in our interactive LLM Cost Calculator.
Frequently Asked Questions
Yes, o3-mini is significantly cheaper than o1, making high-volume agentic coding workflows economically viable.
