Llama 4 Scout (10M context) Meta AI 10000000
💰 Total Cost Calculation (from Plugin)
Output: $0.300000
Output: $0.300000
Unit: $0.000000
Fees: $0.000000
Detailed Cost Analysis (from Plugin)
For 1,000,000 input tokens and 1,000,000 output tokens:
- Input Cost: $0.080000
- Output Cost: $0.300000
- Total Cost: $0.380000
- Cost per 1K tokens: $0.000190
- Tokens per dollar: 5,263,158 tokens
- Context Window: 10000000 tokens
Speed & Performance Analysis
With a processing speed of 600 tokens per second and 120ms time to first token:
- Processing Time: 59 minutes, 26.85 seconds
- Latency: 120 milliseconds to first token
- Base Throughput: 600 tokens/second
- Effective Throughput: 561 tokens/second (temperature-adjusted)
Best Use Cases
Want this applied to YOUR actual stack?
This calculator shows the math for Llama 4 Scout (10M context). Your decision needs more — current infrastructure, compliance requirements, actual workload patterns, volume tiers — that change which model is right for you.
Get a $39 personalized AI Architecture Audit. PDF tailored to your stack, delivered in under 60 seconds. 7-day no-questions-asked refund.
Get my instant AI audit — $39 →✨ Market Recommendations AI Model Registry
← Back to Llama 4 Scout (10M context)| Rank | AI Model & Provider | Total Cost | vs Llama 4 Scout (10M context) |
|---|---|---|---|
| 🏆 |
Grok 4.20 Beta
xAI
|
$8.000000 Best Value | ↑ 2005.3% more |
| 🥈 |
Gemini 2.5 Pro
Google
|
$17.500000 | ↑ 4505.3% more |
| 🥉 |
Gemini 2.5 Pro
Google
|
$17.500000 | ↑ 4505.3% more |
Grok 4.20 Beta xAI
Gemini 2.5 Pro Google
Gemini 2.5 Pro Google
“
Industrial-Scale Information Ingestion
In 2026, Llama 4 Scout defines the ‘Ultra-Long Context’ market with its massive 10-million token window. Priced at just $0.38 for 1M/1M tokens, it is the most efficient way to process entire technical libraries or the full history of a Slack workspace. It is the industrial-scale scanner of the AI world, identifying needle-in-a-haystack information across gigabytes of text in seconds.
The Economics of Scale
For legal firms conducting massive discovery, Scout allows for the ingestion of tens of thousands of documents in a single query. While its reasoning is ‘Scout’ level (meant for identification and summarization), it is the essential first step in any large-scale data analysis pipeline. Once Scout identifies the critical information, a more powerful model like Maverick or Opus can be used for final analysis.
“