GPT-6 Astra OpenAI 1050000
💰 Total Cost Calculation (from Plugin)
Output: $0.375000 (rounded ~ $0.38)
Output: $0.375000 (rounded ~ $0.38)
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 50,000 input tokens and 15,000 output tokens:
- Input Cost: $0.250000
- Output Cost: $0.375000 (rounded ~ $0.38)
- Total Cost: $0.512500 (rounded ~ $0.51)
- Cost per 1K tokens: $0.007885 (rounded ~ $0.01)
- Tokens per dollar: 126,829 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: 2 minutes, 45.78 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 |
|---|---|---|---|
| 🏆 |
Claude Haiku 5.5
Anthropic
|
$0.005125 (rounded ~ $0.01) Best Value | ↓ 99% cheaper |
| 🥈 |
GPT-6 Luna
OpenAI
|
$0.005125 (rounded ~ $0.01) | ↓ 99% cheaper |
| 🥉 |
GPT-5.6 Luna
OpenAI
|
$0.005875 (rounded ~ $0.01) | ↓ 98.9% cheaper |
| #4 |
Gemini 3.1 Flash Lite
Google
|
$0.007344 (rounded ~ $0.01) | ↓ 98.6% cheaper |
| #5 |
Mistral Large 3
Mistral AI
|
$0.009063 | ↓ 98.2% cheaper |
| #6 |
Gemini 3.5 Flash-Lite
Google
|
$0.011438 (rounded ~ $0.01) | ↓ 97.8% cheaper |
| #7 |
Gemini 2.5 Flash
Google
|
$0.011438 (rounded ~ $0.01) | ↓ 97.8% cheaper |
| #8 |
Gemini 3.8 Flash
Google
|
$0.019219 | ↓ 96.3% cheaper |
| #9 |
Gemini 3.6 Flash
Google
|
$0.019219 | ↓ 96.3% cheaper |
| #10 |
GPT-5.4 mini
OpenAI
|
$0.022031 (rounded ~ $0.02) | ↓ 95.7% cheaper |
| #11 |
Mistral Large 4
Mistral AI
|
$0.025025 (rounded ~ $0.03) | ↓ 95.1% cheaper |
| #12 |
Claude Haiku 4.5
Anthropic
|
$0.025625 (rounded ~ $0.03) | ↓ 95% cheaper |
| #13 |
Gemini 3.1 Flash
Google
|
$0.029375 | ↓ 94.3% cheaper |
| #14 |
Claude Sonnet 5.5
Anthropic
|
$0.050625 | ↓ 90.1% cheaper |
| #15 |
Gemini 4 Argon
Google
|
$0.051250 (rounded ~ $0.05) | ↓ 90% cheaper |
| #16 |
Claude Sonnet 5
Anthropic
|
$0.051250 (rounded ~ $0.05) | ↓ 90% cheaper |
| #17 |
Grok 4.3
xAI
|
$0.057500 (rounded ~ $0.06) | ↓ 88.8% cheaper |
| #18 |
Grok 4.20 Beta
xAI
|
$0.057500 (rounded ~ $0.06) | ↓ 88.8% cheaper |
| #19 |
GPT-5.6 Terra
OpenAI
|
$0.058750 (rounded ~ $0.06) | ↓ 88.5% cheaper |
| #20 |
GPT-5.3 Codex Spark
OpenAI
|
$0.064531 (rounded ~ $0.06) | ↓ 87.4% cheaper |
| #21 |
GPT-5.3 Instant
OpenAI
|
$0.064531 (rounded ~ $0.06) | ↓ 87.4% cheaper |
| #22 |
Claude Sonnet 4.6
Anthropic
|
$0.076875 (rounded ~ $0.08) | ↓ 85% cheaper |
| #23 |
Gemini 2.5 Pro
Google
|
$0.092188 (rounded ~ $0.09) | ↓ 82% cheaper |
| #24 |
Claude Opus 5.5
Anthropic
|
$0.101250 (rounded ~ $0.10) | ↓ 80.2% cheaper |
| #25 |
GPT-6.1 Sol
OpenAI
|
$0.102500 (rounded ~ $0.10) | ↓ 80% cheaper |
| #26 |
GPT-6 Sol
OpenAI
|
$0.102500 (rounded ~ $0.10) | ↓ 80% cheaper |
| #27 |
GPT-5.6 Sol
OpenAI
|
$0.102500 (rounded ~ $0.10) | ↓ 80% cheaper |
| #28 |
Gemini 3.1 Pro
Google
|
$0.117500 (rounded ~ $0.12) | ↓ 77.1% cheaper |
| #29 |
Claude Opus 4.7
Anthropic
|
$0.128125 (rounded ~ $0.13) | ↓ 75% cheaper |
| #30 |
Claude Opus 5
Anthropic
|
$0.128125 (rounded ~ $0.13) | ↓ 75% cheaper |
| #31 |
Claude Opus 4.8
Anthropic
|
$0.128125 (rounded ~ $0.13) | ↓ 75% cheaper |
| #32 |
Claude Opus 4.6
Anthropic
|
$0.128125 (rounded ~ $0.13) | ↓ 75% cheaper |
| #33 |
GPT-5.4
OpenAI
|
$0.146875 (rounded ~ $0.15) | ↓ 71.3% cheaper |
| #34 |
GPT-5.4 Thinking
OpenAI
|
$0.146875 (rounded ~ $0.15) | ↓ 71.3% cheaper |
| #35 |
GPT-5.5 Instant
OpenAI
|
$0.146875 (rounded ~ $0.15) | ↓ 71.3% cheaper |
| #36 |
Claude Fable 5.1
Anthropic
|
$0.251563 (rounded ~ $0.25) | ↓ 50.9% cheaper |
| #37 |
Claude Mythos 5.1
Anthropic
|
$0.251563 (rounded ~ $0.25) | ↓ 50.9% cheaper |
| #38 |
Claude Fable 5
Anthropic
|
$0.256250 (rounded ~ $0.26) | ↓ 50% cheaper |
| #39 |
Claude Mythos 5
Anthropic
|
$0.256250 (rounded ~ $0.26) | ↓ 50% cheaper |
| #40 |
GPT-5.5
OpenAI
|
$0.293750 (rounded ~ $0.29) | ↓ 42.7% cheaper |
| #41 |
GPT-5.2 Pro
OpenAI
|
$0.774375 (rounded ~ $0.77) | ↑ 51.1% more |
| #42 |
GPT-5.2 Pro
OpenAI
|
$0.774375 (rounded ~ $0.77) | ↑ 51.1% more |
Claude Haiku 5.5 Anthropic
GPT-6 Luna OpenAI
GPT-5.6 Luna OpenAI
Gemini 3.1 Flash Lite Google
Mistral Large 3 Mistral AI
Gemini 3.5 Flash-Lite Google
Gemini 2.5 Flash Google
Gemini 3.8 Flash Google
Gemini 3.6 Flash Google
GPT-5.4 mini OpenAI
Mistral Large 4 Mistral AI
Claude Haiku 4.5 Anthropic
Gemini 3.1 Flash Google
Claude Sonnet 5.5 Anthropic
Gemini 4 Argon Google
Claude Sonnet 5 Anthropic
Grok 4.3 xAI
Grok 4.20 Beta xAI
GPT-5.6 Terra OpenAI
GPT-5.3 Codex Spark OpenAI
GPT-5.3 Instant OpenAI
Claude Sonnet 4.6 Anthropic
Gemini 2.5 Pro Google
Claude Opus 5.5 Anthropic
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-5.2 Pro OpenAI
GPT-5.2 Pro OpenAI
For game studios integrating automated research assistants into their production pipelines, GPT-6 Astra presents a compelling case for drafting complex technical documentation and literature reviews. When processing extensive document sets—such as the 65K-token requirements for deep-dive literature reviews—the model’s reasoning capabilities allow for high-fidelity synthesis that maintains narrative consistency across lengthy outputs.
The primary advantage for enterprise teams operating at a 100M-token monthly scale is the model’s ability to handle intricate instruction sets without losing track of nuanced source material. Unlike smaller, more generic models, this architecture is optimized for sustained engagement with dense, academic-style information, making it suitable for generating structured paper drafts or summarizing vast quantities of internal research.
However, architects should evaluate the trade-off between reasoning depth and latency. While the model excels at producing high-quality, nuanced content that requires minimal human revision, the computational resources required for its reasoning engine may introduce slight delays compared to lighter, flash-variant models. For teams where throughput is the paramount metric, balancing this model with lighter alternatives for initial summarization tasks can create a cost-effective, high-performance pipeline. The ideal implementation involves using this model for the final drafting and synthesis stages where accuracy and clarity are non-negotiable, while delegating simpler, bulk-processing tasks to more lightweight counterparts within the same architecture family.