Claude Opus 5.5 Anthropic 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.010000
Output: $0.010000
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: $1.000000
- Output Cost: $0.010000
- Total Cost: $0.535000 (rounded ~ $0.54)
- Cost per 1K tokens: $0.000534
- Tokens per dollar: 1,872,897 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, 1 minute, 26.11 seconds
- Latency: 380 milliseconds to first token
- Base Throughput: 280 tokens/second
- Effective Throughput: 272 tokens/second (temperature-adjusted)
Best Use Cases
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← Back to Claude Opus 5.5| Rank | AI Model & Provider | Total Cost | vs Claude Opus 5.5 |
|---|---|---|---|
| 🏆 |
Gemini 3.5 Flash-Lite
Google
|
$0.042500 (rounded ~ $0.04) Best Value | ↓ 92.1% cheaper |
| 🥈 |
GPT-6 Luna
OpenAI
|
$0.055750 (rounded ~ $0.06) | ↓ 89.6% cheaper |
| 🥉 |
Gemini 3.8 Flash
Google
|
$0.105000 (rounded ~ $0.11) | ↓ 80.4% cheaper |
| #4 |
Gemini 3.6 Flash
Google
|
$0.105000 (rounded ~ $0.11) | ↓ 80.4% cheaper |
| #5 |
Gemini 4 Argon
Google
|
$0.280000 | ↓ 47.7% cheaper |
| #6 |
Gemini 2.5 Pro
Google
|
$0.702500 (rounded ~ $0.70) | ↑ 31.3% more |
| #7 |
GPT-6.1 Sol
OpenAI
|
$1.115000 (rounded ~ $1.12) | ↑ 108.4% more |
| #8 |
GPT-6 Sol
OpenAI
|
$1.115000 (rounded ~ $1.12) | ↑ 108.4% more |
| #9 |
GPT-5.4
OpenAI
|
$1.397500 (rounded ~ $1.40) | ↑ 161.2% more |
| #10 |
GPT-5.4 Thinking
OpenAI
|
$1.397500 (rounded ~ $1.40) | ↑ 161.2% more |
| #11 |
GPT-6 Astra
OpenAI
|
$5.600000 | ↑ 946.7% more |
| #12 |
GPT-6 Astra
OpenAI
|
$5.600000 | ↑ 946.7% more |
Gemini 3.5 Flash-Lite Google
GPT-6 Luna OpenAI
Gemini 3.8 Flash Google
Gemini 3.6 Flash Google
Gemini 4 Argon Google
Gemini 2.5 Pro Google
GPT-6.1 Sol OpenAI
GPT-6 Sol OpenAI
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
GPT-6 Astra OpenAI
GPT-6 Astra OpenAI
For financial analysts and researchers dealing with 100-page 10-K filings, the ability to maintain coherence across massive document structures is paramount. Claude Opus 5.5 stands out in this domain due to its refined reasoning capabilities, which are essential for extracting nuanced financial data from dense, unstructured text. When your pipeline involves parsing dozens of lengthy reports simultaneously, Opus provides a level of reliability that minimizes the need for iterative prompting or error correction.
Unlike smaller, more generic models, Opus excels at identifying subtle discrepancies in balance sheets or complex management discussion sections. Social media managers or analysts looking to automate their quarterly earnings workflows will find that the model’s high-context window allows for the ingestion of multiple related SEC filings in a single pass. This reduces the fragmentation often seen when breaking down documents into smaller chunks.
While the performance is top-tier, evaluators should consider the latency trade-offs inherent in such a powerful model. If your primary goal is rapid, real-time extraction for live financial dashboards, you may find that Opus acts as a powerful backend for deep-dive analysis rather than a front-end for high-frequency trading alerts. For teams where accuracy is the non-negotiable metric for audit-grade reporting, this model serves as the gold standard for maintaining structural integrity across large-scale document pipelines.