Claude Opus 4.8 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 280 tokens per second and 380ms time to first token:
- Processing Time: 1 hour, 3 minutes, 56.89 seconds
- Latency: 380 milliseconds to first token
- Base Throughput: 280 tokens/second
- Effective Throughput: 262 tokens/second (temperature-adjusted)
Best Use Cases
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← Back to Claude Opus 4.8| Rank | AI Model & Provider | Total Cost | vs Claude Opus 4.8 |
|---|---|---|---|
| 🏆 |
Gemini 3.5 Flash-Lite
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$0.043750 (rounded ~ $0.04) Best Value | ↓ 93.9% cheaper |
| 🥈 |
Gemini 3.6 Flash
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$0.213750 (rounded ~ $0.21) | ↓ 70% cheaper |
| 🥉 |
Gemini 2.5 Pro
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$0.717500 (rounded ~ $0.72) | ↑ 0.7% more |
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GPT-5.4
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$1.420000 | ↑ 99.3% more |
| #5 |
GPT-5.4 Thinking
OpenAI
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$1.420000 | ↑ 99.3% more |
| #6 |
GPT-5.4 Thinking
OpenAI
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$1.420000 | ↑ 99.3% more |
Gemini 3.5 Flash-Lite Google
Gemini 3.6 Flash Google
Gemini 2.5 Pro Google
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
GPT-5.4 Thinking OpenAI
Optimizing Creative Workflows
For a 20-person startup focused on consistent video script production, selecting a model with high reasoning capability is paramount. Claude Opus 4.8 stands out in environments where script nuance, character consistency, and adherence to complex brand voice guidelines are non-negotiable. Unlike models optimized purely for throughput, this architecture excels at maintaining narrative arcs over long-form content, which is critical when you are producing multiple scripts every week.
As a CTO, you must evaluate the risk of model drift. Claude Opus 4.8 provides a stable, high-performance baseline that reduces the need for constant prompt re-engineering. This stability is a hidden efficiency gain; your creative team spends less time debugging model responses and more time refining the actual video content. The 1M token context window also allows you to ingest entire content libraries or show bibles, ensuring the AI maintains context across your entire production pipeline.
While latency can be higher compared to lighter models, for long-form scriptwriting, the time-to-first-token is rarely the bottleneck compared to the human review process. Instead, focus on the quality of output to minimize the ‘editing tax’ paid by your content team. By offloading the heavy lifting of narrative structure to a sophisticated reasoning engine, you align your AI costs with the value of high-quality, production-ready drafts, effectively turning your AI spend into an investment in creative velocity rather than just generation volume.