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: 52 minutes, 33.53 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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← Back to GPT-5.5 Pro| Rank | AI Model & Provider | Total Cost | vs GPT-5.5 Pro |
|---|---|---|---|
| 🏆 |
Gemini 3.5 Flash-Lite
Google
|
$0.062750 (rounded ~ $0.06) Best Value | ↓ 99.2% cheaper |
| 🥈 |
Gemini 3.8 Flash
Google
|
$0.155625 (rounded ~ $0.16) | ↓ 97.9% cheaper |
| 🥉 |
Gemini 3.6 Flash
Google
|
$0.311250 (rounded ~ $0.31) | ↓ 95.9% cheaper |
| #4 |
Gemini 2.5 Pro
Google
|
$1.040000 | ↓ 86.3% cheaper |
| #5 |
GPT-5.4
OpenAI
|
$2.072500 (rounded ~ $2.07) | ↓ 72.7% cheaper |
| #6 |
GPT-5.4 Thinking
OpenAI
|
$2.072500 (rounded ~ $2.07) | ↓ 72.7% cheaper |
| #7 |
GPT-6 Astra
OpenAI
|
$8.300000 | ↑ 9.4% more |
| #8 |
GPT-6 Astra
OpenAI
|
$8.300000 | ↑ 9.4% 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 EdTech product managers managing high-volume marketing copy A/B testing, the primary challenge is balancing creative nuance with brand consistency. You need a model capable of generating dozens of high-quality variations for different student demographics—from K-12 parents to university students—while adhering to strict educational guidelines and brand tone. GPT-5.5 Pro excels in this environment because of its advanced reasoning capabilities, which allow it to iterate on complex marketing briefs with greater fidelity than previous iterations. When you are pushing 100 million tokens monthly, the consistency of the model becomes the deciding factor in your workflow stability. Unlike smaller models that might require excessive prompt engineering or manual oversight to maintain voice, this model maintains high instruction adherence across diverse, large-scale batches. It is particularly effective for generating structured ad copy, landing page headlines, and email subject lines where the difference between a high and low conversion rate is often found in the subtlety of the language. Beyond the raw output, its agentic capabilities allow for better integration into automated marketing pipelines, where the model can not only draft copy but also evaluate its own variations against provided personas. For teams focused on rapid experimentation, the ability to generate reliable, on-brand copy at enterprise scale without constant hand-holding makes this the preferred choice for sophisticated marketing automation stacks where quality, speed, and consistency are non-negotiable.