o3 Pro OpenAI
💰 Total Cost Calculation (from Plugin)
Output: $0.020000
Output: $0.020000
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 5,000,000 input tokens and 1,000 output tokens:
- Input Cost: $25.000000
- Output Cost: $0.020000
- Total Cost: $20.520000
- Cost per 1K tokens: $0.004103
- Tokens per dollar: 243,713 tokens
- Context Window: 200000 tokens
Speed & Performance Analysis
With a processing speed of 350 tokens per second and 300ms time to first token:
- Processing Time: 4 hours, 2 minutes, 54.52 seconds
- Latency: 300 milliseconds to first token
- Base Throughput: 350 tokens/second
- Effective Throughput: 343 tokens/second (temperature-adjusted)
Best Use Cases
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Get my instant AI audit — $39 →Claude Sonnet 4.6 Anthropic 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.003750
Output: $0.003750
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 5,000,000 input tokens and 1,000 output tokens:
- Input Cost: $3.750000
- Output Cost: $0.003750
- Total Cost: $3.078750 (rounded ~ $3.08)
- Cost per 1K tokens: $0.000616
- Tokens per dollar: 1,624,361 tokens
- Context Window: 1000000 tokens
Speed & Performance Analysis
With a processing speed of 450 tokens per second and 200ms time to first token:
- Processing Time: 3 hours, 8 minutes, 55.78 seconds
- Latency: 200 milliseconds to first token
- Base Throughput: 450 tokens/second
- Effective Throughput: 441 tokens/second (temperature-adjusted)
Best Use Cases
Want this applied to YOUR actual stack?
This calculator shows the math for Claude Sonnet 4.6. Your decision needs more — current infrastructure, compliance requirements, actual workload patterns, volume tiers — that change which model is right for you.
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Get my instant AI audit — $39 →✨ Market Recommendations AI Model Registry
← Back to o3 ProClinical Note Generation at Scale
For mid-market healthcare SaaS platforms, transforming doctor-dictated audio or raw transcripts into formalized clinical records is a high-stakes balancing act. The requirement is not just speed; it is clinical accuracy, strict adherence to structured schema, and the ability to handle complex diagnostic logic without hallucination. As you scale to 5M tokens monthly, the choice between reasoning-heavy models and instruction-following powerhouses becomes a primary driver of your unit economics and product reliability.
Strategic Model Selection
o3 Pro is purpose-built for deep reasoning. In clinical contexts, this model excels where diagnostic validation, contradictory evidence detection, and multi-step clinical logic are required. If your product involves automated differential diagnosis support or complex summarization that requires ‘thinking’ through patient histories, o3 Pro provides a safety-first approach to reasoning.
Claude Sonnet 4.6, by contrast, is frequently the preferred choice for high-volume structured data extraction. Its ability to follow complex formatting instructions—such as mapping clinical notes directly to FHIR resources or specific EHR JSON schemas—is industry-leading. For standard clinical note generation where the primary task is parsing and formatting, Claude often achieves higher throughput and lower latency, making it ideal for standardizing the millions of tokens your pipeline processes monthly.
Reducing Vendor Lock-in
Given the regulatory sensitivity of clinical data, relying on a single provider introduces operational risk. A robust architecture often employs both models: using Claude Sonnet 4.6 for routine extraction and o3 Pro as a ‘reasoning layer’ for complex cases requiring high-fidelity verification.