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 500,000 input tokens and 2,000 output tokens:
- Input Cost: $0.625000 (rounded ~ $0.63)
- Output Cost: $0.012500 (rounded ~ $0.01)
- Total Cost: $0.356250 (rounded ~ $0.36)
- Cost per 1K tokens: $0.000710
- Tokens per dollar: 1,409,123 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: 32 minutes, 49.56 seconds
- Latency: 400 milliseconds to first token
- Base Throughput: 260 tokens/second
- Effective Throughput: 255 tokens/second (temperature-adjusted)
Best Use Cases
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💰 Total Cost Calculation (from Plugin)
Output: $0.022500 (rounded ~ $0.02)
Output: $0.022500 (rounded ~ $0.02)
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 500,000 input tokens and 2,000 output tokens:
- Input Cost: $1.250000
- Output Cost: $0.022500 (rounded ~ $0.02)
- Total Cost: $0.710000
- Cost per 1K tokens: $0.001414
- Tokens per dollar: 707,042 tokens
- Context Window: 1024000 tokens
Speed & Performance Analysis
With a processing speed of 400 tokens per second and 220ms time to first token:
- Processing Time: 21 minutes, 20.28 seconds
- Latency: 220 milliseconds to first token
- Base Throughput: 400 tokens/second
- Effective Throughput: 392 tokens/second (temperature-adjusted)
Best Use Cases
Want this applied to YOUR actual stack?
This calculator shows the math for GPT-5.4 Thinking. 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 4.7| Rank | AI Model & Provider | Total Cost | vs Claude Opus 4.7 | vs GPT-5.4 Thinking |
|---|---|---|---|---|
| 🏆 |
Gemini 3.1 Flash Lite
Google
|
$0.017938 (rounded ~ $0.02) Best Value | ↓ 95% cheaper | ↓ 97.5% cheaper |
| 🥈 |
Gemini 3.5 Flash-Lite
Google
|
$0.021875 (rounded ~ $0.02) | ↓ 93.9% cheaper | ↓ 96.9% cheaper |
| 🥉 |
Gemini 2.5 Flash
Google
|
$0.021875 (rounded ~ $0.02) | ↓ 93.9% cheaper | ↓ 96.9% cheaper |
| #4 |
Gemini 3.8 Flash
Google
|
$0.053438 (rounded ~ $0.05) | ↓ 85% cheaper | ↓ 92.5% cheaper |
| #5 |
GPT-5.6 Luna
OpenAI
|
$0.071750 (rounded ~ $0.07) | ↓ 79.9% cheaper | ↓ 89.9% cheaper |
| #6 |
Gemini 3.6 Flash
Google
|
$0.106875 (rounded ~ $0.11) | ↓ 70% cheaper | ↓ 84.9% cheaper |
| #7 |
Gemini 3.5 Flash
Google
|
$0.107625 (rounded ~ $0.11) | ↓ 69.8% cheaper | ↓ 84.8% cheaper |
| #8 |
Claude Sonnet 5
Anthropic
|
$0.142500 (rounded ~ $0.14) | ↓ 60% cheaper | ↓ 79.9% cheaper |
| #9 |
Gemini 3.1 Flash
Google
|
$0.143500 (rounded ~ $0.14) | ↓ 59.7% cheaper | ↓ 79.8% cheaper |
| #10 |
GPT-5.6 Terra
OpenAI
|
$0.179375 | ↓ 49.6% cheaper | ↓ 74.7% cheaper |
| #11 |
Claude Sonnet 4.6
Anthropic
|
$0.213750 (rounded ~ $0.21) | ↓ 40% cheaper | ↓ 69.9% cheaper |
| #12 |
Claude Opus 5
Anthropic
|
$0.356250 (rounded ~ $0.36) | Same price | ↓ 49.8% cheaper |
| #13 |
Claude Opus 4.8
Anthropic
|
$0.356250 (rounded ~ $0.36) | Same price | ↓ 49.8% cheaper |
| #14 |
Claude Opus 4.6
Anthropic
|
$0.356250 (rounded ~ $0.36) | Same price | ↓ 49.8% cheaper |
| #15 |
Gemini 2.5 Pro
Google
|
$0.358750 (rounded ~ $0.36) | ↑ 0.7% more | ↓ 49.5% cheaper |
| #16 |
GPT-5.6 Sol
OpenAI
|
$0.358750 (rounded ~ $0.36) | ↑ 0.7% more | ↓ 49.5% cheaper |
| #17 |
Grok 4.3
xAI
|
$0.558000 (rounded ~ $0.56) | ↑ 56.6% more | ↓ 21.4% cheaper |
| #18 |
Grok 4.20 Beta
xAI
|
$0.558000 (rounded ~ $0.56) | ↑ 56.6% more | ↓ 21.4% cheaper |
| #19 |
Gemini 3.1 Pro
Google
|
$0.568000 (rounded ~ $0.57) | ↑ 59.4% more | ↓ 20% cheaper |
| #20 |
Claude Fable 5.1
Anthropic
|
$0.665625 (rounded ~ $0.67) | ↑ 86.8% more | ↓ 6.2% cheaper |
| #21 |
Claude Mythos 5.1
Anthropic
|
$0.665625 (rounded ~ $0.67) | ↑ 86.8% more | ↓ 6.2% cheaper |
| #22 |
GPT-5.4
OpenAI
|
$0.710000 | ↑ 99.3% more | Same price |
| #23 |
GPT-5.4 Thinking
OpenAI
|
$0.710000 | ↑ 99.3% more | Same price |
| #24 |
Claude Fable 5
Anthropic
|
$0.712500 (rounded ~ $0.71) | ↑ 100% more | ↑ 0.4% more |
| #25 |
Claude Mythos 5
Anthropic
|
$0.712500 (rounded ~ $0.71) | ↑ 100% more | ↑ 0.4% more |
| #26 |
GPT-5.5
OpenAI
|
$1.420000 | ↑ 298.6% more | ↑ 100% more |
| #27 |
GPT-6 Astra
OpenAI
|
$2.850000 | ↑ 700% more | ↑ 301.4% more |
| #28 |
GPT-6 Astra
OpenAI
|
$2.850000 | ↑ 700% more | ↑ 301.4% more |
Gemini 3.1 Flash Lite Google
Gemini 3.5 Flash-Lite Google
Gemini 2.5 Flash Google
Gemini 3.8 Flash Google
GPT-5.6 Luna OpenAI
Gemini 3.6 Flash Google
Gemini 3.5 Flash Google
Claude Sonnet 5 Anthropic
Gemini 3.1 Flash Google
GPT-5.6 Terra OpenAI
Claude Sonnet 4.6 Anthropic
Claude Opus 5 Anthropic
Claude Opus 4.8 Anthropic
Claude Opus 4.6 Anthropic
Gemini 2.5 Pro Google
GPT-5.6 Sol OpenAI
Grok 4.3 xAI
Grok 4.20 Beta xAI
Gemini 3.1 Pro Google
Claude Fable 5.1 Anthropic
Claude Mythos 5.1 Anthropic
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
Claude Fable 5 Anthropic
Claude Mythos 5 Anthropic
GPT-5.5 OpenAI
GPT-6 Astra OpenAI
GPT-6 Astra OpenAI
For enterprise architects building financial RAG (Retrieval-Augmented Generation) pipelines, the choice between Claude Opus 4.7 and GPT-5.4 Thinking often comes down to the specific nature of the reasoning task. Financial earnings analysis demands a high degree of precision when parsing unstructured 10-K filings, earnings transcripts, and supplemental disclosures. Claude Opus 4.7 is widely favored in the financial services sector for its measured, professional prose and its ability to maintain context over extremely long, multi-document sequences. It excels in tasks that require synthesis of complex, contradictory signals across disparate sources, making it a robust choice for qualitative due diligence and competitive benchmarking.
Conversely, GPT-5.4 Thinking represents the frontier of model-native reasoning. Its architecture is specifically optimized for multi-step logical deduction, which is particularly beneficial when the financial analysis requires calculating metrics that are not explicitly stated in the reports—such as deriving implicit debt-to-equity ratios or normalizing non-GAAP financial measures. While Claude often provides a more consistent narrative structure, the Thinking-enabled GPT model can often solve for complex inter-dependencies in financial statements with higher reliability on its first pass. When designing your pipeline, consider whether your primary workflow is retrieval-heavy, where Opus’s context fidelity shines, or logic-heavy, where GPT’s reasoning capabilities provide a distinct advantage. Both models support massive context windows, making them suitable for ingesting entire 10-K annual reports in a single session without losing track of the document’s hierarchy or specific line-item data points.