Claude Opus 5 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: $0.700000
- Cost per 1K tokens: $0.000699
- Tokens per dollar: 1,431,429 tokens
- Context Window: 1000000 tokens
Speed & Performance Analysis
With a processing speed of 300 tokens per second and 300ms time to first token:
- Processing Time: 59 minutes, 33.98 seconds
- Latency: 300 milliseconds to first token
- Base Throughput: 300 tokens/second
- Effective Throughput: 280 tokens/second (temperature-adjusted)
Best Use Cases
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This calculator shows the math for Claude Opus 5. 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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💰 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 1,000,000 input tokens and 2,000 output tokens:
- Input Cost: $0.375000 (rounded ~ $0.38)
- Output Cost: $0.003750
- Total Cost: $0.210000
- Cost per 1K tokens: $0.000210
- Tokens per dollar: 4,771,429 tokens
- Context Window: 1048576 tokens
Speed & Performance Analysis
With a processing speed of 304 tokens per second and 120ms time to first token:
- Processing Time: 58 minutes, 46.96 seconds
- Latency: 120 milliseconds to first token
- Base Throughput: 304 tokens/second
- Effective Throughput: 284 tokens/second (temperature-adjusted)
Best Use Cases
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This calculator shows the math for Gemini 3.6 Flash. 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 Opus 5| Rank | AI Model & Provider | Total Cost | vs Claude Opus 5 | vs Gemini 3.6 Flash |
|---|---|---|---|---|
| 🏆 |
Gemini 3.5 Flash-Lite
Google
|
$0.042500 (rounded ~ $0.04) Best Value | ↓ 93.9% cheaper | ↓ 79.8% cheaper |
| 🥈 |
Gemini 3.8 Flash
Google
|
$0.105000 (rounded ~ $0.11) | ↓ 85% cheaper | ↓ 50% cheaper |
| 🥉 |
Gemini 3.6 Flash
Google
|
$0.210000 | ↓ 70% cheaper | Same price |
| #4 |
Gemini 2.5 Pro
Google
|
$0.702500 (rounded ~ $0.70) | ↑ 0.4% more | ↑ 234.5% more |
| #5 |
GPT-5.4
OpenAI
|
$1.397500 (rounded ~ $1.40) | ↑ 99.6% more | ↑ 565.5% more |
| #6 |
GPT-5.4 Thinking
OpenAI
|
$1.397500 (rounded ~ $1.40) | ↑ 99.6% more | ↑ 565.5% more |
| #7 |
GPT-6 Astra
OpenAI
|
$5.600000 | ↑ 700% more | ↑ 2566.7% more |
| #8 |
GPT-6 Astra
OpenAI
|
$5.600000 | ↑ 700% more | ↑ 2566.7% 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 Between Reasoning-Heavy and Throughput-Optimized Architectures
For technical writers and engineers managing large-scale document pipelines, selecting the right model often comes down to the trade-off between complex reasoning and raw processing throughput. When analyzing documents exceeding 500,000 tokens, the decision architecture shifts significantly.
Claude Opus 5 is designed for high-fidelity extraction and reasoning. Its architecture excels in multi-step analysis where identifying subtle contradictions or synthesizing disparate sections of a lengthy document is critical. If your document summarization workflow requires deep understanding—such as extracting specific legal clauses or validating compliance across hundreds of pages—the reasoning capabilities inherent in this model provide superior reliability. It acts as a cognitive partner, effectively reducing the need for iterative prompting.
Conversely, Gemini 3.6 Flash is engineered for velocity and high-volume operations. In scenarios where you need to process thousands of files per hour, the latency advantages and efficient throughput of this model are unmatched. It is particularly effective for tasks like bulk metadata extraction, standard summarization, or indexing content where a ‘good enough’ accuracy threshold allows for higher concurrency. The architectural design prioritizes speed, making it the more suitable choice for rapid-fire document ingestion pipelines where operational cost-efficiency and system responsiveness are the primary KPIs.
Ultimately, your choice should be driven by the complexity of the extraction logic. If your requirement is precision at scale, the reasoning-focused model is the clear winner. If your goal is high-throughput document processing with strict latency requirements, the flash-optimized model provides the better technical fit.