Side-by-side technical and economic comparison between Meta / Open's Llama 4 Scout (109B) and Anthropic's Claude Opus 5.
| Metric | Llama 4 Scout (109B) | Claude Opus 5 | Advantage |
|---|---|---|---|
| Provider Organization | Meta / Open | Anthropic | — |
| Standard Input / 1M | $0.10 | $15.00 | Llama 4 Scout (109B) (99% lower) |
| Cached Input / 1M | $0.30 | $0.50 | Llama 4 Scout (109B) lower |
| Output / 1M | $0.32 | $75.00 | Llama 4 Scout (109B) lower |
| Context Window | 10M | 200k | Llama 4 Scout (109B) (10M) |
| Max Generation Tokens | 16.4k | 16.4k | Llama 4 Scout (109B) |
| Tokenizer Family | Meta Llama 3/4 Tiktoken (128k vocabulary) | Anthropic Claude BPE (~65k vocabulary) | — |
Opt for Llama 4 Scout (109B) when your engineering requirements prioritize Meta / Open's ecosystem, specific tokenizer efficiencies (Calibrated Llama 3 Tokenizer (±3%)), or when your expected prompt-to-completion ratios favor its $0.1/M input rate.
Opt for Claude Opus 5 when looking for Anthropic's tooling integration, specific context window depth (200k tokens), or when output generation volume favors its $75/M rate.