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
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💰 Total Cost Calculation (from Plugin)
Output: $0.018000 (rounded ~ $0.02)
Output: $0.018000 (rounded ~ $0.02)
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: $2.000000
- Output Cost: $0.018000 (rounded ~ $0.02)
- Total Cost: $1.118000 (rounded ~ $1.12)
- Cost per 1K tokens: $0.001116
- Tokens per dollar: 896,243 tokens
- Context Window: 1000000 tokens
Speed & Performance Analysis
With a processing speed of 400 tokens per second and 220ms time to first token:
- Processing Time: 42 minutes, 35.28 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 Gemini 3.1 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 →✨ Market Recommendations AI Model Registry
← Back to Claude Opus 4.7| Rank | AI Model & Provider | Total Cost | vs Claude Opus 4.7 | vs Gemini 3.1 Pro |
|---|---|---|---|---|
| 🏆 |
Gemini 3.5 Flash-Lite
Google
|
$0.042500 (rounded ~ $0.04) Best Value | ↓ 93.9% cheaper | ↓ 96.2% cheaper |
| 🥈 |
Gemini 3.8 Flash
Google
|
$0.105000 (rounded ~ $0.11) | ↓ 85% cheaper | ↓ 90.6% cheaper |
| 🥉 |
Gemini 3.6 Flash
Google
|
$0.210000 | ↓ 70% cheaper | ↓ 81.2% cheaper |
| #4 |
Gemini 2.5 Pro
Google
|
$0.702500 (rounded ~ $0.70) | ↑ 0.4% more | ↓ 37.2% cheaper |
| #5 |
GPT-5.4
OpenAI
|
$1.397500 (rounded ~ $1.40) | ↑ 99.6% more | ↑ 25% more |
| #6 |
GPT-5.4 Thinking
OpenAI
|
$1.397500 (rounded ~ $1.40) | ↑ 99.6% more | ↑ 25% more |
| #7 |
GPT-6 Astra
OpenAI
|
$5.600000 | ↑ 700% more | ↑ 400.9% more |
| #8 |
GPT-6 Astra
OpenAI
|
$5.600000 | ↑ 700% more | ↑ 400.9% 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
When building legal-tech pipelines, the choice between Claude Opus 4.7 and Gemini 3.1 Pro often hinges on the specific nature of your contract review workload. For legal teams, the core challenge is maintaining high-accuracy extraction across massive, unstructured document repositories.
Claude Opus 4.7 is frequently selected for its nuanced reasoning and instruction-following capabilities. In contract review, where specific clause definitions—such as limitation of liability or indemnification—must be isolated from dense, 50-page agreements, Opus often demonstrates superior precision in distinguishing between standard boilerplate and critical negotiated terms. It is particularly effective for workflows that require multi-step reasoning to interpret how various clauses interact across different sections of a document.
Conversely, Gemini 3.1 Pro offers distinct advantages in document processing pipelines that require multimodal inputs. If your legal review involves OCR-heavy workflows—such as analyzing scanned PDFs, handwritten signatures, or image-embedded appendices—Gemini’s integrated vision capabilities reduce the need for upstream pre-processing. For engineers managing 1M-token context windows, Gemini’s native architecture provides efficient handling of long-context retrieval, making it a strong candidate for large-scale due diligence platforms where speed and broad document support are paramount.
Deciding between these two requires balancing the depth of legal reasoning required versus the breadth of document modalities you need to ingest. While Opus excels in pure logic and clause extraction accuracy, Gemini’s ability to streamline multimodal document pipelines can significantly reduce architectural complexity for high-volume, multi-format legal discovery projects.