GPT-6 Astra OpenAI 1050000 🏔️ Context Cliff
💰 Total Cost Calculation (from Plugin)
Output: $0.100000
Output: $0.100000
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: $10.000000
- Output Cost: $0.100000
- Total Cost: $5.600000
- Cost per 1K tokens: $0.005589 (rounded ~ $0.01)
- Tokens per dollar: 178,929 tokens
- Context Window: 1050000 tokens
Speed & Performance Analysis
With a processing speed of 420 tokens per second and 320ms time to first token:
- Processing Time: 42 minutes, 32.89 seconds
- Latency: 320 milliseconds to first token
- Base Throughput: 420 tokens/second
- Effective Throughput: 393 tokens/second (temperature-adjusted)
Best Use Cases
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| 🏆 |
Gemini 3.5 Flash-Lite
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$0.042500 (rounded ~ $0.04) Best Value | ↓ 99.2% cheaper |
| 🥈 |
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$0.055750 (rounded ~ $0.06) | ↓ 99% cheaper |
| 🥉 |
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$0.105000 (rounded ~ $0.11) | ↓ 98.1% cheaper |
| #4 |
Gemini 3.6 Flash
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|
$0.105000 (rounded ~ $0.11) | ↓ 98.1% cheaper |
| #5 |
Gemini 4 Argon
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|
$0.280000 | ↓ 95% cheaper |
| #6 |
Gemini 2.5 Pro
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|
$0.702500 (rounded ~ $0.70) | ↓ 87.5% cheaper |
| #7 |
GPT-6.1 Sol
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$1.115000 (rounded ~ $1.12) | ↓ 80.1% cheaper |
| #8 |
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$1.115000 (rounded ~ $1.12) | ↓ 80.1% cheaper |
| #9 |
GPT-5.4
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$1.397500 (rounded ~ $1.40) | ↓ 75% cheaper |
| #10 |
GPT-5.4 Thinking
OpenAI
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$1.397500 (rounded ~ $1.40) | ↓ 75% cheaper |
| #11 |
GPT-5.4 Thinking
OpenAI
|
$1.397500 (rounded ~ $1.40) | ↓ 75% cheaper |
Gemini 3.5 Flash-Lite Google
GPT-6 Luna OpenAI
Gemini 3.8 Flash Google
Gemini 3.6 Flash Google
Gemini 4 Argon Google
Gemini 2.5 Pro Google
GPT-6.1 Sol OpenAI
GPT-6 Sol OpenAI
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
GPT-5.4 Thinking OpenAI
Optimizing Internal Knowledge Base Q&A with GPT-6 Astra
For organizations managing extensive internal documentation, the primary challenge is achieving precise, multi-step reasoning across large, unstructured datasets. GPT-6 Astra is designed specifically for high-stakes professional environments where accuracy and the ability to synthesize information from diverse sources are paramount. This model excels in complex agentic workflows where traditional RAG (Retrieval-Augmented Generation) might struggle with nuanced, multi-document dependencies.
When implementing a knowledge base Q&A system for 50-document corpuses—such as internal HR policies, technical manuals, or project archives—the model’s ability to maintain focus over long context windows is a critical advantage. Unlike lighter models that may lose the thread in dense, overlapping documents, GPT-6 Astra demonstrates superior adherence to task boundaries and intent, making it a reliable choice for teams requiring high-fidelity responses without the need for constant human oversight.
For analysts, the choice to deploy this model often hinges on the need for ‘frontier-level’ reasoning capabilities. While throughput and latency are key considerations, the model’s performance on complex, multi-step queries often reduces the total number of follow-up interactions required, effectively optimizing the overall workflow. Teams evaluating this model for internal Q&A should consider the trade-off between the depth of reasoning provided and the specific demands of their document processing pipelines, particularly when handling highly specialized domain knowledge.