Claude Opus 4.7 Anthropic 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.025000 (rounded ~ $0.03)
Output: $0.025000 (rounded ~ $0.03)
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 1,000,000 input tokens and 4,000 output tokens:
- Input Cost: $1.250000
- Output Cost: $0.025000 (rounded ~ $0.03)
- Total Cost: $0.712500 (rounded ~ $0.71)
- 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: 1 hour, 6 minutes, 17.56 seconds
- Latency: 400 milliseconds to first token
- Base Throughput: 260 tokens/second
- Effective Throughput: 252 tokens/second (temperature-adjusted)
Best Use Cases
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Get my instant AI audit — $39 →GPT-5.4 Pro OpenAI 1024000 🏔️ Context Cliff
💰 Total Cost Calculation (from Plugin)
Output: $0.540000
Output: $0.540000
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 1,000,000 input tokens and 4,000 output tokens:
- Input Cost: $30.000000
- Output Cost: $0.540000
- Total Cost: $17.040000
- Cost per 1K tokens: $0.016972 (rounded ~ $0.02)
- Tokens per dollar: 58,920 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: 49 minutes, 14.81 seconds
- Latency: 250 milliseconds to first token
- Base Throughput: 350 tokens/second
- Effective Throughput: 340 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.
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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 Pro |
|---|---|---|---|---|
| 🏆 |
Gemini 3.5 Flash-Lite
Google
|
$0.043750 (rounded ~ $0.04) Best Value | ↓ 93.9% cheaper | ↓ 99.7% cheaper |
| 🥈 |
Gemini 3.8 Flash
Google
|
$0.106875 (rounded ~ $0.11) | ↓ 85% cheaper | ↓ 99.4% cheaper |
| 🥉 |
Gemini 3.6 Flash
Google
|
$0.213750 (rounded ~ $0.21) | ↓ 70% cheaper | ↓ 98.7% cheaper |
| #4 |
Gemini 2.5 Pro
Google
|
$0.717500 (rounded ~ $0.72) | ↑ 0.7% more | ↓ 95.8% cheaper |
| #5 |
GPT-5.4
OpenAI
|
$1.420000 | ↑ 99.3% more | ↓ 91.7% cheaper |
| #6 |
GPT-5.4 Thinking
OpenAI
|
$1.420000 | ↑ 99.3% more | ↓ 91.7% cheaper |
| #7 |
GPT-6 Astra
OpenAI
|
$5.700000 | ↑ 700% more | ↓ 66.5% cheaper |
| #8 |
GPT-6 Astra
OpenAI
|
$5.700000 | ↑ 700% more | ↓ 66.5% cheaper |
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
Analyzing High-Stakes Legal Discovery
In the realm of legal contract review, context is everything. When evaluating 50-page agreements for multi-clause dependencies, the ability to maintain a coherent internal model across massive datasets is the primary differentiator between standard automation and true enterprise-grade intelligence. Customer support leads deploying these systems must decide between the raw reasoning depth of GPT-5.4 Pro and the nuanced, instruction-following precision of Claude Opus 4.7.
Claude Opus 4.7 is frequently selected for its sophisticated handling of complex, multi-step instructions, making it ideal for extracting subtle legal nuances that require cross-referencing disparate sections of a document. Its output tends to be more concise and aligned with specific formatting requirements, which reduces the need for secondary post-processing. On the other hand, GPT-5.4 Pro offers a robust agentic capability, excelling when the task requires the model to interact with external tools or perform iterative reasoning steps to verify its own findings.
For high-volume RAG pipelines where the goal is to serve legal analysts with high-fidelity summaries and risk assessments, the choice often hinges on the specific complexity of the legal language. While both models handle the million-token threshold, the internal attention mechanisms differ; one prioritizes linguistic fluidity while the other excels at structured logic and tool integration.