GPT-5.5 OpenAI 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.030000
Output: $0.030000
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 100,000 input tokens and 2,000 output tokens:
- Input Cost: $0.250000
- Output Cost: $0.030000
- Total Cost: $0.100000
- Cost per 1K tokens: $0.000980
- Tokens per dollar: 1,020,000 tokens
- Context Window: 1000000 tokens
Speed & Performance Analysis
With a processing speed of 420 tokens per second and 210ms time to first token:
- Processing Time: 4 minutes, 7.89 seconds
- Latency: 210 milliseconds to first token
- Base Throughput: 420 tokens/second
- Effective Throughput: 412 tokens/second (temperature-adjusted)
Best Use Cases
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← Back to GPT-5.5| Rank | AI Model & Provider | Total Cost | vs GPT-5.5 |
|---|---|---|---|
| 🏆 |
Mistral Small 3
Mistral AI
|
$0.000850 Best Value | ↓ 99.2% cheaper |
| 🥈 |
Grok Code Fast 1
xAI
|
$0.002150 | ↓ 97.9% cheaper |
| 🥉 |
Gemini 3.1 Flash Lite
Google
|
$0.002500 | ↓ 97.5% cheaper |
| #4 |
Gemini 2.5 Flash
Google
|
$0.003350 | ↓ 96.7% cheaper |
| #5 |
Mistral Large 3
Mistral AI
|
$0.004250 | ↓ 95.8% cheaper |
| #6 |
GPT-5.4 mini
OpenAI
|
$0.007500 (rounded ~ $0.01) | ↓ 92.5% cheaper |
| #7 |
o4-mini Deep Research
OpenAI
|
$0.009000 (rounded ~ $0.01) | ↓ 91% cheaper |
| #8 |
Claude Haiku 4.5
Anthropic
|
$0.009500 | ↓ 90.5% cheaper |
| #9 |
o4-mini
OpenAI
|
$0.009900 | ↓ 90.1% cheaper |
| #10 |
Grok 4.3
xAI
|
$0.010000 | ↓ 90% cheaper |
| #11 |
Gemini 3.1 Flash
Google
|
$0.010000 | ↓ 90% cheaper |
| #12 |
Gemini 3.5 Flash
Google
|
$0.015000 (rounded ~ $0.02) | ↓ 85% cheaper |
| #13 |
Grok 4.20 Beta
xAI
|
$0.017000 (rounded ~ $0.02) | ↓ 83% cheaper |
| #14 |
GPT-5.3 Codex Spark
OpenAI
|
$0.019250 | ↓ 80.8% cheaper |
| #15 |
GPT-5.3 Instant
OpenAI
|
$0.019250 | ↓ 80.8% cheaper |
| #16 |
Gemini 2.5 Pro
Google
|
$0.027500 (rounded ~ $0.03) | ↓ 72.5% cheaper |
| #17 |
Claude Sonnet 4.6
Anthropic
|
$0.028500 (rounded ~ $0.03) | ↓ 71.5% cheaper |
| #18 |
Gemini 3.1 Pro
Google
|
$0.040000 | ↓ 60% cheaper |
| #19 |
Claude Opus 4.7
Anthropic
|
$0.047500 (rounded ~ $0.05) | ↓ 52.5% cheaper |
| #20 |
Claude Opus 4.8
Anthropic
|
$0.047500 (rounded ~ $0.05) | ↓ 52.5% cheaper |
| #21 |
Claude Opus 4.6
Anthropic
|
$0.047500 (rounded ~ $0.05) | ↓ 52.5% cheaper |
| #22 |
GPT-5.4
OpenAI
|
$0.050000 | ↓ 50% cheaper |
| #23 |
GPT-5.4 Thinking
OpenAI
|
$0.050000 | ↓ 50% cheaper |
| #24 |
GPT-5.5 Instant
OpenAI
|
$0.050000 | ↓ 50% cheaper |
| #25 |
o3 Deep Research
OpenAI
|
$0.090000 | ↓ 10% cheaper |
| #26 |
o3 Pro
OpenAI
|
$0.180000 | ↑ 80% more |
| #27 |
GPT-5.2 Pro
OpenAI
|
$0.231000 | ↑ 131% more |
| #28 |
GPT-5.2 Pro
OpenAI
|
$0.231000 | ↑ 131% more |
Mistral Small 3 Mistral AI
Grok Code Fast 1 xAI
Gemini 3.1 Flash Lite Google
Gemini 2.5 Flash Google
Mistral Large 3 Mistral AI
GPT-5.4 mini OpenAI
o4-mini Deep Research OpenAI
Claude Haiku 4.5 Anthropic
o4-mini OpenAI
Grok 4.3 xAI
Gemini 3.1 Flash Google
Gemini 3.5 Flash Google
Grok 4.20 Beta xAI
GPT-5.3 Codex Spark OpenAI
GPT-5.3 Instant OpenAI
Gemini 2.5 Pro Google
Claude Sonnet 4.6 Anthropic
Gemini 3.1 Pro Google
Claude Opus 4.7 Anthropic
Claude Opus 4.8 Anthropic
Claude Opus 4.6 Anthropic
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
GPT-5.5 Instant OpenAI
o3 Deep Research OpenAI
o3 Pro OpenAI
GPT-5.2 Pro OpenAI
GPT-5.2 Pro OpenAI
Scaling Intelligent Knowledge Retrieval
For real estate agencies and educational platforms managing vast libraries of property disclosures, CMA reports, or tutoring curriculums, the ability to perform high-fidelity retrieval-augmented generation (RAG) is critical. GPT-5.5 stands out as a primary candidate for enterprise-scale pipelines where depth of context and reasoning are paramount. When processing hundreds of millions of tokens monthly, the consistency of the model’s output determines the success of automated client responses.
The shift to a 100,000-token per-call input model allows platforms to ingest entire document sets—such as multi-year market analysis or comprehensive student progress histories—without fragmenting the source material. This holistic view enables the agent to draw connections across disparate files that smaller models might miss, reducing hallucination in complex technical or legal domains.
Enterprise architects often find that this model’s reasoning capabilities streamline agentic workflows, particularly when the system needs to orchestrate multiple tools to verify property facts or update tutoring schedules. While the token volume is high, the architectural advantage lies in the model’s ability to maintain logical coherence over long-form tasks. Teams evaluating this model for production should prioritize latency and integration maturity within their existing stack, ensuring that the heavy reasoning load does not bottleneck the overall user experience. For teams already deep in the OpenAI ecosystem, the transition to this tier offers a reliable path to scaling knowledge operations without sacrificing the nuance required for high-stakes client communication.