OpenAI o3-mini vs DeepSeek R1 API Pricing & STEM Reasoning Benchmark (2026)
Compare OpenAI o3-mini ($1.10/$4.40) vs DeepSeek R1 ($0.55/$2.19): STEM math benchmarks, coding IQ, reasoning token multipliers, and latency trade-offs.
OpenAI o3-mini
DeepSeek R1
Head-to-Head Monthly Cost Simulator
Simulate real workload expenditures for OpenAI o3-mini vs DeepSeek R1 at your expected token volume.
Comprehensive Technical & Financial Breakdown
Side-by-side audit of pricing tiers, token speeds, benchmark intelligence, and architecture limits.
| Specification / Metric | OpenAI o3-mini | DeepSeek R1 | Financial & Engineering Impact |
|---|---|---|---|
| Input Price / 1M | $1.10 | $0.55 | DeepSeek R1 is 50% cheaper |
| Output Price / 1M | $4.40 | $2.19 | DeepSeek R1 is 50% cheaper |
| Cached Read / 1M | $0.55 | $0.14 | DeepSeek R1 saves 75% on cache |
| Context Window | 200,000 tokens | 64,000 tokens | o3-mini offers 3.1x larger context |
| Max Output Tokens | 100,000 tokens | 8,192 tokens | o3-mini supports massive multi-file code synthesis |
| AIME 2024 Math | 87.3% (High Effort) | 79.8% | o3-mini wins on Olympiad mathematics |
| SWE-bench Verified | 49.3% | 49.2% | Statistical dead-heat on autonomous bug fixing |
| Time To First Token | 1,400 ms | 3,800 ms | o3-mini is significantly faster for interactive UI |
| Reasoning Control | reasoning_effort ('low', 'medium', 'high') | Fixed CoT |
o3-mini gives precise developer cost control |
Choose **OpenAI o3-mini** if you need low-latency interactive applications, fine-grained control via `reasoning_effort`, or large context (>64K tokens). Choose **DeepSeek R1** for high-volume offline batch evaluations, asynchronous pipelines, or where token unit economics are your #1 priority.
Frequently Asked Questions: OpenAI o3-mini vs DeepSeek R1
Real-world answers covering token budgeting, prompt caching, latency, and SLA differences.
Which model is cheaper for production coding pipelines?
DeepSeek R1 is approximately 50% cheaper on both input and output tokens ($0.55/$2.19 vs $1.10/$4.40). However, o3-mini with `reasoning_effort='low'` generates fewer reasoning tokens, which can narrow the net invoice difference.
How do reasoning tokens affect billing on both models?
On both models, internal reasoning tokens count as generated output tokens and are billed at full output rates. On o3-mini, reasoning tokens are hidden but billed; on DeepSeek R1, reasoning tokens appear in `
Does o3-mini support prompt caching?
Yes, OpenAI provides a 50% discount on prompt prefixes exceeding 1,024 tokens ($0.55/M). DeepSeek provides a 74% discount on cached reads ($0.14/M).
Can either model run locally?
DeepSeek R1 has open weights available under the MIT license, allowing self-hosting on 8x H100 clusters or quantized runs. OpenAI o3-mini is proprietary and accessible only via OpenAI or Azure API endpoints.