Side-by-side technical and economic comparison between Alibaba / Qwen's Qwen 2.5 72B Instruct — Together AI and Meta / Together's Llama 4 Vivas.
• Qwen 2.5 72B Instruct — Together AI: Classified as a Legacy Reference model (Deprecated on Together AI (Reference)).
| Metric | Qwen 2.5 72B Instruct — Together AI | Llama 4 Vivas | Advantage |
|---|---|---|---|
| Provider Organization | Alibaba / Qwen | Meta / Together | — |
| Standard Input / 1M | $0.35 | $0.25 | Llama 4 Vivas (29% lower) |
| Cached Input / 1M | $0.175 | $0.06 | Llama 4 Vivas lower |
| Output / 1M | $0.40 | $0.85 | Qwen 2.5 72B Instruct — Together AI lower |
| Context Window | 128k | 1M | Llama 4 Vivas (1M) |
| Max Generation Tokens | 8.2k | 128k | Llama 4 Vivas |
| Tokenizer Family | Qwen Byte-level BPE (~152k vocabulary) | Meta Llama 3 Tokenizer (128k vocabulary) | — |
Opt for Qwen 2.5 72B Instruct — Together AI when your engineering requirements prioritize Alibaba / Qwen's ecosystem, specific tokenizer efficiencies (Calibrated Qwen Tokenizer (±3%)), or when your expected prompt-to-completion ratios favor its $0.35/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.