Side-by-side technical and economic comparison between Google's Gemini 2.5 Flash-Lite 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 | Gemini 2.5 Flash-Lite | Llama 4 Scout (17B Active / 109B MoE) — Together AI | Advantage |
|---|---|---|---|
| Provider Organization | Meta / Together | — | |
| Standard Input / 1M | $0.10 | $0.18 | Gemini 2.5 Flash-Lite (44% lower) |
| Cached Input / 1M | $0.01 | None | Gemini 2.5 Flash-Lite lower |
| Output / 1M | $0.40 | $0.59 | Gemini 2.5 Flash-Lite lower |
| Context Window | 1.05M | 328k | Gemini 2.5 Flash-Lite (1.05M) |
| Max Generation Tokens | 8.2k | 16.4k | Llama 4 Scout (17B Active / 109B MoE) — Together AI |
| Tokenizer Family | Google Gemini SentencePiece (256k vocabulary) | Meta Llama 3/4 Tiktoken (128k vocabulary) | — |
Opt for Gemini 2.5 Flash-Lite when your engineering requirements prioritize Google's ecosystem, specific tokenizer efficiencies (Calibrated Gemini Tokenizer (±4%)), or when your expected prompt-to-completion ratios favor its $0.1/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.