Claude Opus 4.7 Anthropic 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.050000
Output: $0.050000
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 1,000,000 input tokens and 8,000 output tokens:
- Input Cost: $1.250000
- Output Cost: $0.050000
- Total Cost: $0.737500 (rounded ~ $0.74)
- Cost per 1K tokens: $0.000732
- Tokens per dollar: 1,366,780 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, 9 minutes, 8.49 seconds
- Latency: 400 milliseconds to first token
- Base Throughput: 260 tokens/second
- Effective Throughput: 243 tokens/second (temperature-adjusted)
Best Use Cases
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💰 Total Cost Calculation (from Plugin)
Output: $0.360000
Output: $0.360000
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 1,000,000 input tokens and 8,000 output tokens:
- Input Cost: $7.500000
- Output Cost: $0.360000
- Total Cost: $7.860000
- Cost per 1K tokens: $0.007798 (rounded ~ $0.01)
- Tokens per dollar: 128,244 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: 52 minutes, 52.42 seconds
- Latency: 260 milliseconds to first token
- Base Throughput: 340 tokens/second
- Effective Throughput: 318 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 →✨ Market Recommendations AI Model Registry
← Back to Claude Opus 4.7| Rank | AI Model & Provider | Total Cost | vs Claude Opus 4.7 | vs GPT-5.5 Pro |
|---|---|---|---|---|
| 🏆 |
Gemini 3.5 Flash-Lite
Google
|
$0.046250 (rounded ~ $0.05) Best Value | ↓ 93.7% cheaper | ↓ 99.4% cheaper |
| 🥈 |
Gemini 3.8 Flash
Google
|
$0.110625 | ↓ 85% cheaper | ↓ 98.6% cheaper |
| 🥉 |
Gemini 3.6 Flash
Google
|
$0.221250 (rounded ~ $0.22) | ↓ 70% cheaper | ↓ 97.2% cheaper |
| #4 |
Gemini 2.5 Pro
Google
|
$0.747500 (rounded ~ $0.75) | ↑ 1.4% more | ↓ 90.5% cheaper |
| #5 |
GPT-5.4
OpenAI
|
$1.465000 (rounded ~ $1.47) | ↑ 98.6% more | ↓ 81.4% cheaper |
| #6 |
GPT-5.4 Thinking
OpenAI
|
$1.465000 (rounded ~ $1.47) | ↑ 98.6% more | ↓ 81.4% cheaper |
| #7 |
GPT-6 Astra
OpenAI
|
$5.900000 | ↑ 700% more | ↓ 24.9% cheaper |
| #8 |
GPT-6 Astra
OpenAI
|
$5.900000 | ↑ 700% more | ↓ 24.9% 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
For EdTech teams producing long-form video scripts, balancing creative flair with structural consistency is paramount. Both Claude Opus 4.7 and GPT-5.5 Pro offer substantial context windows, allowing them to ingest extensive curriculum guidelines, character bibles, and pedagogical frameworks without losing the thread of a lesson plan. Claude Opus 4.7 has earned a reputation for its sophisticated narrative voice, making it particularly effective for scriptwriting where emotional resonance and human-like cadence are critical for student engagement. It excels at maintaining complex, multi-layered storylines across long-form content, reducing the need for manual retakes during the editing phase.
GPT-5.5 Pro, by contrast, brings a robust reasoning-first approach to content production. Its architectural strengths make it highly effective for structured content, such as instructional tutorials or technical scripts where precision and logical flow are non-negotiable. When managing a pipeline of 1 million tokens, the choice often comes down to the desired output style: Claude Opus 4.7 for scripts requiring high narrative nuance and GPT-5.5 Pro for scripts requiring strict adherence to logical instructional frameworks. Both models support high-volume processing, but development teams should consider how their specific prompt engineering workflows interact with these models’ distinct reasoning styles. For teams aiming to scale production while maintaining educational accuracy, testing both against a sample curriculum module is the recommended path before committing to a full deployment.