GPT-6 Astra OpenAI 1050000
💰 Total Cost Calculation (from Plugin)
Output: $0.012500 (rounded ~ $0.01)
Output: $0.012500 (rounded ~ $0.01)
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 200,000 input tokens and 500 output tokens:
- Input Cost: $1.000000
- Output Cost: $0.012500 (rounded ~ $0.01)
- Total Cost: $0.562500 (rounded ~ $0.56)
- Cost per 1K tokens: $0.002805
- Tokens per dollar: 356,444 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: 8 minutes, 30.98 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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← Back to GPT-6 Astra| Rank | AI Model & Provider | Total Cost | vs GPT-6 Astra |
|---|---|---|---|
| 🏆 |
GPT-6 Luna
OpenAI
|
$0.005625 (rounded ~ $0.01) Best Value | ↓ 99% cheaper |
| 🥈 |
GPT-5.6 Luna
OpenAI
|
$0.005650 (rounded ~ $0.01) | ↓ 99% cheaper |
| 🥉 |
Gemini 3.1 Flash Lite
Google
|
$0.007063 (rounded ~ $0.01) | ↓ 98.7% cheaper |
| #4 |
Gemini 3.5 Flash-Lite
Google
|
$0.008563 (rounded ~ $0.01) | ↓ 98.5% cheaper |
| #5 |
Gemini 2.5 Flash
Google
|
$0.008563 (rounded ~ $0.01) | ↓ 98.5% cheaper |
| #6 |
Mistral Large 3
Mistral AI
|
$0.013938 (rounded ~ $0.01) | ↓ 97.5% cheaper |
| #7 |
Gemini 3.8 Flash
Google
|
$0.021094 (rounded ~ $0.02) | ↓ 96.3% cheaper |
| #8 |
Gemini 3.6 Flash
Google
|
$0.021094 (rounded ~ $0.02) | ↓ 96.3% cheaper |
| #9 |
GPT-5.4 mini
OpenAI
|
$0.021188 (rounded ~ $0.02) | ↓ 96.2% cheaper |
| #10 |
Claude Haiku 5.5
Anthropic
|
$0.028125 (rounded ~ $0.03) | ↓ 95% cheaper |
| #11 |
Claude Haiku 4.5
Anthropic
|
$0.028125 (rounded ~ $0.03) | ↓ 95% cheaper |
| #12 |
Gemini 3.1 Flash
Google
|
$0.028250 (rounded ~ $0.03) | ↓ 95% cheaper |
| #13 |
Mistral Large 4
Mistral AI
|
$0.037923 (rounded ~ $0.04) | ↓ 93.3% cheaper |
| #14 |
Claude Sonnet 5.5
Anthropic
|
$0.053750 (rounded ~ $0.05) | ↓ 90.4% cheaper |
| #15 |
Gemini 4 Argon
Google
|
$0.056250 (rounded ~ $0.06) | ↓ 90% cheaper |
| #16 |
Claude Sonnet 5
Anthropic
|
$0.056250 (rounded ~ $0.06) | ↓ 90% cheaper |
| #17 |
GPT-5.6 Terra
OpenAI
|
$0.056500 (rounded ~ $0.06) | ↓ 90% cheaper |
| #18 |
Gemini 2.5 Pro
Google
|
$0.071250 (rounded ~ $0.07) | ↓ 87.3% cheaper |
| #19 |
Claude Sonnet 4.6
Anthropic
|
$0.084375 (rounded ~ $0.08) | ↓ 85% cheaper |
| #20 |
Claude Opus 5.5
Anthropic
|
$0.107500 (rounded ~ $0.11) | ↓ 80.9% cheaper |
| #21 |
Grok 4.3
xAI
|
$0.111000 (rounded ~ $0.11) | ↓ 80.3% cheaper |
| #22 |
Grok 4.20 Beta
xAI
|
$0.111000 (rounded ~ $0.11) | ↓ 80.3% cheaper |
| #23 |
GPT-6.1 Sol
OpenAI
|
$0.112500 (rounded ~ $0.11) | ↓ 80% cheaper |
| #24 |
GPT-6 Sol
OpenAI
|
$0.112500 (rounded ~ $0.11) | ↓ 80% cheaper |
| #25 |
GPT-5.6 Sol
OpenAI
|
$0.112500 (rounded ~ $0.11) | ↓ 80% cheaper |
| #26 |
Gemini 3.1 Pro
Google
|
$0.113000 (rounded ~ $0.11) | ↓ 79.9% cheaper |
| #27 |
Claude Opus 4.7
Anthropic
|
$0.140625 | ↓ 75% cheaper |
| #28 |
Claude Opus 5
Anthropic
|
$0.140625 | ↓ 75% cheaper |
| #29 |
Claude Opus 4.8
Anthropic
|
$0.140625 | ↓ 75% cheaper |
| #30 |
Claude Opus 4.6
Anthropic
|
$0.140625 | ↓ 75% cheaper |
| #31 |
GPT-5.4
OpenAI
|
$0.141250 (rounded ~ $0.14) | ↓ 74.9% cheaper |
| #32 |
GPT-5.4 Thinking
OpenAI
|
$0.141250 (rounded ~ $0.14) | ↓ 74.9% cheaper |
| #33 |
GPT-5.5 Instant
OpenAI
|
$0.141250 (rounded ~ $0.14) | ↓ 74.9% cheaper |
| #34 |
Claude Fable 5.1
Anthropic
|
$0.262500 (rounded ~ $0.26) | ↓ 53.3% cheaper |
| #35 |
Claude Mythos 5.1
Anthropic
|
$0.262500 (rounded ~ $0.26) | ↓ 53.3% cheaper |
| #36 |
Claude Fable 5
Anthropic
|
$0.281250 (rounded ~ $0.28) | ↓ 50% cheaper |
| #37 |
Claude Mythos 5
Anthropic
|
$0.281250 (rounded ~ $0.28) | ↓ 50% cheaper |
| #38 |
GPT-5.5
OpenAI
|
$0.282500 (rounded ~ $0.28) | ↓ 49.8% cheaper |
| #39 |
GPT-5.5
OpenAI
|
$0.282500 (rounded ~ $0.28) | ↓ 49.8% cheaper |
GPT-6 Luna OpenAI
GPT-5.6 Luna OpenAI
Gemini 3.1 Flash Lite Google
Gemini 3.5 Flash-Lite Google
Gemini 2.5 Flash Google
Mistral Large 3 Mistral AI
Gemini 3.8 Flash Google
Gemini 3.6 Flash Google
GPT-5.4 mini OpenAI
Claude Haiku 5.5 Anthropic
Claude Haiku 4.5 Anthropic
Gemini 3.1 Flash Google
Mistral Large 4 Mistral AI
Claude Sonnet 5.5 Anthropic
Gemini 4 Argon Google
Claude Sonnet 5 Anthropic
GPT-5.6 Terra OpenAI
Gemini 2.5 Pro Google
Claude Sonnet 4.6 Anthropic
Claude Opus 5.5 Anthropic
Grok 4.3 xAI
Grok 4.20 Beta xAI
GPT-6.1 Sol OpenAI
GPT-6 Sol OpenAI
GPT-5.6 Sol OpenAI
Gemini 3.1 Pro Google
Claude Opus 4.7 Anthropic
Claude Opus 5 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
Claude Fable 5.1 Anthropic
Claude Mythos 5.1 Anthropic
Claude Fable 5 Anthropic
Claude Mythos 5 Anthropic
GPT-5.5 OpenAI
GPT-5.5 OpenAI
Internal knowledge base Q&A pipelines require models that can balance deep reasoning with high-fidelity context retrieval. For newsletter publishers and research teams managing a repository of 50 or more documents, the ability to synthesize nuanced information without hallucination is paramount. GPT-6 Astra stands out as a top-tier choice for this use case because of its state-of-the-art reasoning capabilities, which are essential when querying complex, interconnected source material. Unlike smaller models that might struggle with the subtle cross-references found in technical documentation or long-form research, this model maintains high accuracy even when the query requires synthesizing multiple sections of the provided context.
From a technical perspective, the long context window is a major advantage for RAG workflows. By keeping the entire knowledge base context directly in the prompt window, you reduce the risk of retrieval errors that often occur when chunks are pulled from disparate files. This model is particularly effective for teams that need to go beyond simple keyword matching and require a system that understands the intent behind employee questions. While it is a premium model, the improvement in accuracy and the reduction in the need for complex, multi-step prompt engineering often result in a net gain for overall project efficiency. For organizations where accuracy is the highest priority—such as when drafting internal policy responses or summarizing deep research—this model represents a reliable and high-performing foundation for your internal Q&A infrastructure.