Side-by-side technical and economic comparison between DeepSeek's DeepSeek-V4.1-Flash and Meta / Together's Llama 4 Scout (17B Active / 109B MoE) — Together AI.
• Llama 4 Scout (17B Active / 109B MoE) — Together AI: Classified as a Legacy Reference model (Deprecated on Together AI (Reference)).
| Metric | DeepSeek-V4.1-Flash | Llama 4 Scout (17B Active / 109B MoE) — Together AI | Advantage |
|---|---|---|---|
| Provider Organization | DeepSeek | Meta / Together | — |
| Standard Input / 1M | $0.30 | $0.18 | Llama 4 Scout (17B Active / 109B MoE) — Together AI (40% lower) |
| Cached Input / 1M | $0.006 | None | DeepSeek-V4.1-Flash lower |
| Output / 1M | $1.20 | $0.59 | Llama 4 Scout (17B Active / 109B MoE) — Together AI lower |
| Context Window | 1.05M | 328k | DeepSeek-V4.1-Flash (1.05M) |
| Max Generation Tokens | 384k | 16.4k | DeepSeek-V4.1-Flash |
| Tokenizer Family | DeepSeek Byte-Level BPE (100k vocabulary) | Meta Llama 3/4 Tiktoken (128k vocabulary) | — |
Opt for DeepSeek-V4.1-Flash when your engineering requirements prioritize DeepSeek's ecosystem, specific tokenizer efficiencies (Calibrated DeepSeek Tokenizer (±3%)), or when your expected prompt-to-completion ratios favor its $0.3/M input rate.
Opt for Llama 4 Scout (17B Active / 109B MoE) — Together AI when looking for Meta / Together's tooling integration, specific context window depth (328k tokens), or when output generation volume favors its $0.59/M rate.