Side-by-side technical and economic comparison between Meta / Together's Llama 4 Scout (17B Active / 109B MoE) — Together AI and Meta / Together's Llama 4 Vivas.
• Llama 4 Scout (17B Active / 109B MoE) — Together AI: Classified as a Legacy Reference model (Deprecated on Together AI (Reference)).
| Metric | Llama 4 Scout (17B Active / 109B MoE) — Together AI | Llama 4 Vivas | Advantage |
|---|---|---|---|
| Provider Organization | Meta / Together | Meta / Together | — |
| Standard Input / 1M | $0.18 | $0.25 | Llama 4 Scout (17B Active / 109B MoE) — Together AI (28% lower) |
| Cached Input / 1M | None | $0.06 | Llama 4 Scout (17B Active / 109B MoE) — Together AI lower |
| Output / 1M | $0.59 | $0.85 | Llama 4 Scout (17B Active / 109B MoE) — Together AI lower |
| Context Window | 328k | 1M | Llama 4 Vivas (1M) |
| Max Generation Tokens | 16.4k | 128k | Llama 4 Vivas |
| Tokenizer Family | Meta Llama 3/4 Tiktoken (128k vocabulary) | Meta Llama 3 Tokenizer (128k vocabulary) | — |
Opt for Llama 4 Scout (17B Active / 109B MoE) — Together AI when your engineering requirements prioritize Meta / Together's ecosystem, specific tokenizer efficiencies (Calibrated Llama 3 Tokenizer (±3%)), or when your expected prompt-to-completion ratios favor its $0.18/M input rate.
Opt for Llama 4 Vivas when looking for Meta / Together's tooling integration, specific context window depth (1M tokens), or when output generation volume favors its $0.85/M rate.