Meta open architecture with extreme 10 Million token context window and efficient 17B active MoE
| Scenario | Input Tokens | Output Tokens | Uncached Cost | With Prompt Caching |
|---|---|---|---|---|
| Short Chat Query | 1,000 | 500 | $0.00060 | N/A |
| Document Summarization | 10,000 | 2,000 | $0.00420 | N/A |
| Codebase & Context Analysis | 100,000 | 20,000 | $0.0420 | N/A |
| Batch Corpus Processing | 1,000,000 | 100,000 | $0.3600 | N/A |
Ideal for production workloads demanding balanced capabilities, deep context depth (10M tokens), and reliability from Meta / Open. Excellent when predictable tokenomics and prompt caching support are paramount.
If your use-case requires sub-second streaming latency or ultra-high frequency classification at micro-cent pricing, consider lighter budget options such as Gemini Flash-Lite or Claude Haiku. For deep formal logic, consider dedicated reasoning models like o3.
For Llama 4 Scout (109B), 1 million input tokens costs $0.30, while 1 million output tokens costs $0.60. If using prompt caching, repetitive input prefixes are discounted to $0.30 per million.
Llama 4 Scout (109B) features a maximum context window of 10,000,000 tokens (~7,500,000 words), with a maximum output limit of 16,384 tokens per completion.
Llama 4 Scout (109B) utilizes the Meta Llama 3/4 Tiktoken (128k vocabulary). Token counting on TokenMath runs client-side to ensure maximum privacy.
No, Llama 4 Scout (109B) does not currently advertise prompt caching discounts on its standard API tier.