Claude Opus 4.6 Anthropic 1000000 🏔️ Context Cliff
💰 Total Cost Calculation (from Plugin)
Output: $7.500000
Output: $7.500000
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 1,000,000 input tokens and 200,000 output tokens:
- Input Cost: $10.000000
- Output Cost: $7.500000
- Total Cost: $17.500000
- Cost per 1K tokens: $0.014583 (rounded ~ 0.01)
- Tokens per dollar: 68,571 tokens
- Context Window: 1000000 tokens
Speed & Performance Analysis
With a processing speed of 280 tokens per second and 380ms time to first token:
- Processing Time: 1 hour, 15 minutes
- Latency: 380 milliseconds to first token
- Base Throughput: 280 tokens/second
- Effective Throughput: 267 tokens/second (temperature-adjusted)
Best Use Cases
GPT-5.2 Pro OpenAI
💰 Total Cost Calculation (from Plugin)
Output: $33.600000
Output: $33.600000
Unit: $0.000000
Fees: $0.000000
Detailed Cost Analysis (from Plugin)
For 1,000,000 input tokens and 200,000 output tokens:
- Input Cost: $21.000000
- Output Cost: $33.600000
- Total Cost: $54.600000
- Cost per 1K tokens: $0.045500 (rounded ~ 0.05)
- Tokens per dollar: 21,978 tokens
- Context Window: 128000 tokens
Speed & Performance Analysis
With a processing speed of 350 tokens per second and 250ms time to first token:
- Processing Time: 1 hour
- Latency: 250 milliseconds to first token
- Base Throughput: 350 tokens/second
- Effective Throughput: 333 tokens/second (temperature-adjusted)
Best Use Cases
✨ Market Recommendations AI Model Registry
← Back to Claude Opus 4.6| Rank | AI Model & Provider | Total Cost | vs Claude Opus 4.6 | vs GPT-5.2 Pro |
|---|---|---|---|---|
| 🏆 |
Grok 5
xAI
|
$6.000000 Best Value | ↓ 65.7% cheaper | ↓ 89% cheaper |
| 🥈 |
Grok 5
xAI
|
$6.000000 | ↓ 65.7% cheaper | ↓ 89% cheaper |
Grok 5 xAI
Grok 5 xAI
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Frontier Intelligence Comparison
As of February 2026, scientific researchers are choosing Claude 4.6 Opus over GPT-5.2 for complex data synthesis. While GPT-5.2 excels in raw logic, Claude 4.6 Opus ($5.00/$25.00 per 1M tokens) provides a superior ‘needle-in-a-haystack’ retrieval rate in its 1M context window. This makes it ideal for synthesizing hundreds of academic papers simultaneously to identify hidden correlations in biomedical data.
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