Claude Sonnet 5 Anthropic 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.005000 (rounded ~ $0.01)
Output: $0.005000 (rounded ~ $0.01)
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 1,000,000 input tokens and 2,000 output tokens:
- Input Cost: $0.500000
- Output Cost: $0.005000 (rounded ~ $0.01)
- Total Cost: $0.280000
- Cost per 1K tokens: $0.000279
- Tokens per dollar: 3,578,571 tokens
- Context Window: 1000000 tokens
Speed & Performance Analysis
With a processing speed of 460 tokens per second and 195ms time to first token:
- Processing Time: 37 minutes, 23.79 seconds
- Latency: 195 milliseconds to first token
- Base Throughput: 460 tokens/second
- Effective Throughput: 447 tokens/second (temperature-adjusted)
Best Use Cases
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💰 Total Cost Calculation (from Plugin)
Output: $0.022500 (rounded ~ $0.02)
Output: $0.022500 (rounded ~ $0.02)
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 1,000,000 input tokens and 2,000 output tokens:
- Input Cost: $2.500000
- Output Cost: $0.022500 (rounded ~ $0.02)
- Total Cost: $1.397500 (rounded ~ $1.40)
- Cost per 1K tokens: $0.001395
- Tokens per dollar: 716,995 tokens
- Context Window: 1024000 tokens
Speed & Performance Analysis
With a processing speed of 400 tokens per second and 220ms time to first token:
- Processing Time: 43 minutes, 0.33 seconds
- Latency: 220 milliseconds to first token
- Base Throughput: 400 tokens/second
- Effective Throughput: 388 tokens/second (temperature-adjusted)
Best Use Cases
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This calculator shows the math for GPT-5.4 Thinking. 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 Claude Sonnet 5| Rank | AI Model & Provider | Total Cost | vs Claude Sonnet 5 | vs GPT-5.4 Thinking |
|---|---|---|---|---|
| 🏆 |
Gemini 3.5 Flash-Lite
Google
|
$0.042500 (rounded ~ $0.04) Best Value | ↓ 84.8% cheaper | ↓ 97% cheaper |
| 🥈 |
Gemini 3.8 Flash
Google
|
$0.105000 (rounded ~ $0.11) | ↓ 62.5% cheaper | ↓ 92.5% cheaper |
| 🥉 |
Gemini 3.6 Flash
Google
|
$0.210000 | ↓ 25% cheaper | ↓ 85% cheaper |
| #4 |
Gemini 2.5 Pro
Google
|
$0.702500 (rounded ~ $0.70) | ↑ 150.9% more | ↓ 49.7% cheaper |
| #5 |
GPT-5.4
OpenAI
|
$1.397500 (rounded ~ $1.40) | ↑ 399.1% more | Same price |
| #6 |
GPT-5.4 Thinking
OpenAI
|
$1.397500 (rounded ~ $1.40) | ↑ 399.1% more | Same price |
| #7 |
GPT-6 Astra
OpenAI
|
$5.600000 | ↑ 1900% more | ↑ 300.7% more |
| #8 |
GPT-6 Astra
OpenAI
|
$5.600000 | ↑ 1900% more | ↑ 300.7% more |
Gemini 3.5 Flash-Lite Google
Gemini 3.8 Flash Google
Gemini 3.6 Flash Google
Gemini 2.5 Pro Google
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
GPT-6 Astra OpenAI
GPT-6 Astra OpenAI
Choosing the Right Model for Clinical Note Generation
For translation agencies scaling clinical note generation, the choice between Claude Sonnet 5 and GPT-5.4 Thinking depends largely on your requirement for agentic autonomy versus reasoning depth. Clinical records often contain unstructured, jargon-heavy dictations that require precise normalization into structured formats.
Claude Sonnet 5 excels at long-horizon task execution. In clinical documentation, this translates to maintaining a consistent persona and tone across large patient history files. Its agentic capabilities allow it to process structured templates more reliably, reducing the need for iterative prompting when complex logic is involved.
GPT-5.4 Thinking, by contrast, leverages its reasoning-first architecture to handle ambiguity. If your clinical pipeline involves interpreting handwritten notes or highly irregular transcriptions, this model’s ability to perform deep, persistent reasoning before outputting the final record is a significant advantage. The model’s integration with computer-use tools also enables it to interact directly with internal clinical systems or electronic health record (EHR) interfaces, making it a stronger choice for end-to-end automation workflows.
When processing 10 million tokens monthly, both models offer distinct performance profiles. Agencies prioritizing throughput and structural consistency often favor Claude’s predictable agentic flow, while those dealing with high-variability inputs—where the AI must ‘think’ through potential medical context—should lean toward OpenAI’s reasoning-heavy approach.