Side-by-side technical and economic comparison between Meta / Together's Llama 4 Scout (17B Active / 109B MoE) — Together AI and OpenAI's OpenAI o5-mini.
• 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 | OpenAI o5-mini | Advantage |
|---|---|---|---|
| Provider Organization | Meta / Together | OpenAI | — |
| Standard Input / 1M | $0.18 | $0.80 | Llama 4 Scout (17B Active / 109B MoE) — Together AI (78% lower) |
| Cached Input / 1M | None | $0.20 | Llama 4 Scout (17B Active / 109B MoE) — Together AI lower |
| Output / 1M | $0.59 | $3.20 | Llama 4 Scout (17B Active / 109B MoE) — Together AI lower |
| Context Window | 328k | 200k | Llama 4 Scout (17B Active / 109B MoE) — Together AI (328k) |
| Max Generation Tokens | 16.4k | 100k | OpenAI o5-mini |
| Tokenizer Family | Meta Llama 3/4 Tiktoken (128k vocabulary) | OpenAI o200k_base (200k 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 OpenAI o5-mini when looking for OpenAI's tooling integration, specific context window depth (200k tokens), or when output generation volume favors its $3.2/M rate.