API Pricing
OpenAI o3 Pricing 2026:
Cost, Benchmarks & When to Use It
OpenAI o3 is the most powerful reasoning model available in 2026 — but it comes at a steep cost. Here's everything you need to know before using o3 in production.
10 min read·Updated March 2026
o3 Pricing at a Glance
$10.00
per 1M input tokens
$40.00
per 1M output tokens
$2.50
cached input per 1M
200K
context window
OpenAI o3 Model Pricing 2026
| Model | Input (per 1M) | Output (per 1M) | Context |
|---|---|---|---|
| o3 | $10.00 | $40.00 | 200K |
| o3-mini | $1.10 | $4.40 | 128K |
| o1 | $15.00 | $60.00 | 128K |
| o1-mini | $1.10 | $4.40 | 128K |
| GPT-4o | $2.50 | $10.00 | 128K |
| GPT-4o mini | $0.15 | $0.60 | 128K |
o3 is 4× more expensive than GPT-4o on input and output. Compared to o3-mini, o3 costs 9× more. This pricing reflects the model's significantly higher compute requirements for its extended reasoning chains.
o3 vs o3-mini: Which Should You Use?
| Task | o3-mini | o3 |
|---|---|---|
| Math proofs, competitive math | Often sufficient | Marginal gain |
| PhD-level scientific reasoning | Good but misses edge cases | Significantly better |
| Complex multi-step code debugging | Very good | Best in class |
| Long-document analysis (100K+ tokens) | Limited context | 200K context, better |
| Legal/medical document interpretation | Good for most cases | Required for critical decisions |
| Creative writing | Use GPT-4o instead | Overkill — use GPT-4o |
Real-World o3 Cost Examples
Scientific Research Assistant (1,000 queries/month)
- Average query: 2,000 tokens input + 1,500 tokens output (with reasoning)
- Total: 2M input + 1.5M output tokens
- o3 cost: $20 + $60 = $80/month
- o3-mini cost: $2.20 + $6.60 = $8.80/month
- o3 is 9× more expensive — worth it only if accuracy is critical
Legal Document Review (500 documents/month)
- Average document: 10,000 tokens input + 2,000 tokens output
- Total: 5M input + 1M output
- o3 cost: $50 + $40 = $90/month
- GPT-4o cost: $12.50 + $10 = $22.50/month
- o3 is worth it if the $67.50 difference is less than the risk of errors
o3 Reasoning Effort Levels
OpenAI allows you to control o3's reasoning depth via the reasoning_effort parameter:
| Setting | Reasoning Tokens | Cost Impact | Best For |
|---|---|---|---|
| low | ~500–1,000 | Lowest | Simple tasks, rapid iteration |
| medium | ~2,000–5,000 | Moderate | Most production use cases |
| high | ~10,000–30,000 | Highest | Critical decisions, maximum accuracy |
Key insight: Reasoning tokens are billed as output tokens at $40/M. A "high" effort query can generate 30,000 reasoning tokens — costing $1.20 in reasoning alone per query.
When Does o3 Make Financial Sense?
Use o3 when:
- The cost of an error exceeds the cost of o3 (legal, medical, financial)
- You need the 200K context window (o3-mini only has 128K)
- Tasks require true PhD-level reasoning that o3-mini consistently fails
- You've already tested o3-mini and found unacceptable error rates
Avoid o3 for:
- High-volume, low-stakes tasks (use GPT-4o mini instead)
- Creative writing or summarization
- Simple Q&A and classification
- Cost-sensitive production systems without a clear ROI
Cost Optimization for o3
- Use reasoning_effort=low or medium — only use "high" when accuracy is truly critical
- Route simpler queries to o3-mini — 90% of "hard" tasks are fine with o3-mini
- Prompt caching: o3 supports caching at $2.50/M (vs $10/M standard) — great for repeated system prompts
- Batch API: 50% discount for async workloads — o3 input drops to $5/M, output to $20/M
Compare o3 vs GPT-4o vs Claude
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