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: $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 260 tokens per second and 400ms time to first token:
- Processing Time: 1 hour, 5 minutes, 31.10 seconds
- Latency: 400 milliseconds to first token
- Base Throughput: 260 tokens/second
- Effective Throughput: 255 tokens/second (temperature-adjusted)
Best Use Cases
Want this applied to YOUR actual stack?
This calculator shows the math for Claude Opus 4.7. Your decision needs more — current infrastructure, compliance requirements, actual workload patterns, volume tiers — that change which model is right for you.
Get a $39 personalized AI Architecture Audit. PDF tailored to your stack, delivered in under 60 seconds. 7-day no-questions-asked refund.
Get my instant AI audit — $39 →GPT-5.4 Pro OpenAI 1024000 🏔️ Context Cliff
💰 Total Cost Calculation (from Plugin)
Output: $0.270000
Output: $0.270000
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: $30.000000
- Output Cost: $0.270000
- Total Cost: $16.770000
- Cost per 1K tokens: $0.016737 (rounded ~ $0.02)
- Tokens per dollar: 59,750 tokens
- Context Window: 1024000 tokens
Speed & Performance Analysis
With a processing speed of 350 tokens per second and 250ms time to first token:
- Processing Time: 48 minutes, 40.29 seconds
- Latency: 250 milliseconds to first token
- Base Throughput: 350 tokens/second
- Effective Throughput: 343 tokens/second (temperature-adjusted)
Best Use Cases
Want this applied to YOUR actual stack?
This calculator shows the math for GPT-5.4 Pro. Your decision needs more — current infrastructure, compliance requirements, actual workload patterns, volume tiers — that change which model is right for you.
Get a $39 personalized AI Architecture Audit. PDF tailored to your stack, delivered in under 60 seconds. 7-day no-questions-asked refund.
Get my instant AI audit — $39 →✨ Market Recommendations AI Model Registry
← Back to Claude Opus 4.7| Rank | AI Model & Provider | Total Cost | vs Claude Opus 4.7 | vs GPT-5.4 Pro |
|---|---|---|---|---|
| 🏆 |
Grok 4.20 Beta
xAI
|
$0.278000 (rounded ~ $0.28) Best Value | ↓ 60.3% cheaper | ↓ 98.3% cheaper |
| 🥈 |
Gemini 2.5 Pro
Google
|
$0.702500 (rounded ~ $0.70) | ↑ 0.4% more | ↓ 95.8% cheaper |
| 🥉 |
Gemini 3.1 Pro
Google
|
$1.118000 (rounded ~ $1.12) | ↑ 59.7% more | ↓ 93.3% cheaper |
| #4 |
GPT-5.4
OpenAI
|
$1.397500 (rounded ~ $1.40) | ↑ 99.6% more | ↓ 91.7% cheaper |
| #5 |
GPT-5.4 Thinking
OpenAI
|
$1.397500 (rounded ~ $1.40) | ↑ 99.6% more | ↓ 91.7% cheaper |
| #6 |
GPT-5.4 Thinking
OpenAI
|
$1.397500 (rounded ~ $1.40) | ↑ 99.6% more | ↓ 91.7% cheaper |
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
When designing high-stakes financial RAG systems, the choice between Claude Opus 4.7 and GPT-5.4 Pro often comes down to the specific nature of your analysis. For deep-dive 10-K filings and complex earnings call transcripts, Opus 4.7 stands out for its high-fidelity reasoning and nuanced multi-step instruction following. It consistently delivers cleaner structured outputs, which is vital when you are converting long-form prose into machine-readable financial data for downstream analysis.
Conversely, GPT-5.4 Pro excels in scenarios where your RAG pipeline requires active web research or terminal-based data retrieval to supplement static filings. It shows strong capabilities in information synthesis, making it a powerful choice if your workflow involves aggregating external market signals alongside internal company documentation. While Opus 4.7 holds a slight edge in pure reasoning and structured coding tasks, GPT-5.4 Pro offers a more aggressive tool-orchestration strategy that can speed up research-heavy workflows.
For most production teams, the decision hinges on the ‘agentic’ nature of your pipeline. If you need a reliable ‘analyst’ that adheres strictly to complex taxonomies and extraction rules, Opus 4.7 is the industry standard for precision. If your pipeline is more of a ‘researcher’ that needs to browse, search, and synthesize disparate data sources, GPT-5.4 Pro likely provides the better integration and retrieval performance. Testing both on your specific document corpus is the final step, but these models represent the current frontier for handling 1 million tokens of context with high reliability.