GPT-5.5 Pro OpenAI 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.090000
Output: $0.090000
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: $7.500000
- Output Cost: $0.090000
- Total Cost: $7.590000
- Cost per 1K tokens: $0.007575 (rounded ~ $0.01)
- Tokens per dollar: 132,016 tokens
- Context Window: 1000000 tokens
Speed & Performance Analysis
With a processing speed of 340 tokens per second and 260ms time to first token:
- Processing Time: 50 minutes, 6.18 seconds
- Latency: 260 milliseconds to first token
- Base Throughput: 340 tokens/second
- Effective Throughput: 333 tokens/second (temperature-adjusted)
Best Use Cases
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This calculator shows the math for GPT-5.5 Pro. 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 →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.
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Get my instant AI audit — $39 →✨ Market Recommendations AI Model Registry
← Back to GPT-5.5 Pro| Rank | AI Model & Provider | Total Cost | vs GPT-5.5 Pro | vs Claude Opus 4.7 |
|---|---|---|---|---|
| 🏆 |
Gemini 3.5 Flash-Lite
Google
|
$0.042500 (rounded ~ $0.04) Best Value | ↓ 99.4% cheaper | ↓ 93.9% cheaper |
| 🥈 |
Gemini 3.8 Flash
Google
|
$0.105000 (rounded ~ $0.11) | ↓ 98.6% cheaper | ↓ 85% cheaper |
| 🥉 |
Gemini 3.6 Flash
Google
|
$0.210000 | ↓ 97.2% cheaper | ↓ 70% cheaper |
| #4 |
Gemini 2.5 Pro
Google
|
$0.702500 (rounded ~ $0.70) | ↓ 90.7% cheaper | ↑ 0.4% more |
| #5 |
GPT-5.4
OpenAI
|
$1.397500 (rounded ~ $1.40) | ↓ 81.6% cheaper | ↑ 99.6% more |
| #6 |
GPT-5.4 Thinking
OpenAI
|
$1.397500 (rounded ~ $1.40) | ↓ 81.6% cheaper | ↑ 99.6% more |
| #7 |
GPT-6 Astra
OpenAI
|
$5.600000 | ↓ 26.2% cheaper | ↑ 700% more |
| #8 |
GPT-6 Astra
OpenAI
|
$5.600000 | ↓ 26.2% cheaper | ↑ 700% 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
For enterprise teams processing massive volumes of legal documentation, the choice of model hinges on balancing deep reasoning with the ability to handle extensive context. Reviewing 50-page contracts at a scale of 1 billion tokens monthly demands models that can maintain state across long, complex documents without succumbing to ‘context rot’—the tendency for models to lose accuracy when retrieving information from the middle of a massive context window.
Both GPT-5.5 Pro and Claude Opus 4.7 are industry leaders for this workload. GPT-5.5 Pro is often favored for its highly reliable structured extraction, making it exceptionally strong at converting raw PDF text into clean, machine-readable JSON for downstream compliance systems. Its reasoning capabilities ensure that nuanced clauses, such as indemnity or liability caps, are not just identified but correctly interpreted within the context of the broader agreement.
Claude Opus 4.7, conversely, is frequently cited for its superior nuance in legal drafting and its ability to handle extremely dense, non-standard contractual language with high fidelity. For legal operations teams, the decision often comes down to the specific ‘flavor’ of reasoning required: GPT-5.5 Pro for rigorous extraction and automated classification, or Claude Opus 4.7 when the workflow requires a more sophisticated grasp of contract intent and adversarial edge cases. Both models support high-capacity context windows, essential for keeping entire multi-document M&A dossiers in memory for holistic risk assessment, reducing the need for fragmented, multi-step RAG pipelines.