Claude Opus 4.7 Anthropic 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.012500 (rounded ~ $0.01)
Output: $0.012500 (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: $1.250000
- Output Cost: $0.012500 (rounded ~ $0.01)
- Total Cost: $1.262500 (rounded ~ $1.26)
- Cost per 1K tokens: $0.001260
- Tokens per dollar: 793,663 tokens
- Context Window: 1000000 tokens
Speed & Performance Analysis
With a processing speed of 260 tokens per second and 400ms time to first token:
- Processing Time: 1 hour, 7 minutes, 26.72 seconds
- Latency: 400 milliseconds to first token
- Base Throughput: 260 tokens/second
- Effective Throughput: 248 tokens/second (temperature-adjusted)
Best Use Cases
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← Back to Claude Opus 4.7| Rank | AI Model & Provider | Total Cost | vs Claude Opus 4.7 |
|---|---|---|---|
| 🏆 |
Llama 4 Scout
Meta AI
|
$0.080600 Best Value | ↓ 93.6% cheaper |
| 🥈 |
Grok 4.20 Beta
xAI
|
$0.503000 (rounded ~ $0.50) | ↓ 60.2% cheaper |
| 🥉 |
Gemini 2.5 Pro
Google
|
$1.265000 (rounded ~ $1.27) | ↑ 0.2% more |
| #4 |
Gemini 3.1 Pro
Google
|
$2.018000 (rounded ~ $2.02) | ↑ 59.8% more |
| #5 |
GPT-5.4
OpenAI
|
$2.522500 (rounded ~ $2.52) | ↑ 99.8% more |
| #6 |
GPT-5.4 Thinking
OpenAI
|
$2.522500 (rounded ~ $2.52) | ↑ 99.8% more |
| #7 |
GPT-5.4 Thinking
OpenAI
|
$2.522500 (rounded ~ $2.52) | ↑ 99.8% more |
Llama 4 Scout Meta AI
Grok 4.20 Beta xAI
Gemini 2.5 Pro Google
Gemini 3.1 Pro Google
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
GPT-5.4 Thinking OpenAI
For healthcare administrators tasked with distilling vast amounts of information from lengthy PDFs, Claude Opus 4.7 offers robust capabilities. Its massive 1 million token context window is particularly well-suited for processing entire medical journals, complex patient histories, or extensive research papers in a single pass. This allows for a holistic understanding of the document’s content, crucial for accurate summarization without losing critical details. When dealing with dense medical terminology or intricate case studies, Claude’s strength lies in its sophisticated reasoning and ability to follow complex instructions, making it a reliable choice for ensuring the fidelity of summaries. The focus here is on deep comprehension and precise output, essential for clinical documentation and decision support systems where accuracy is paramount.