Side-by-side technical and economic comparison between OpenAI's GPT-5.6 Luna and DeepSeek's DeepSeek-V4.1-Reasoner.
| Metric | GPT-5.6 Luna | DeepSeek-V4.1-Reasoner | Advantage |
|---|---|---|---|
| Provider Organization | OpenAI | DeepSeek | — |
| Standard Input / 1M | $0.20 | $0.50 | GPT-5.6 Luna (60% lower) |
| Cached Input / 1M | $0.02 | $0.01 | DeepSeek-V4.1-Reasoner lower |
| Output / 1M | $1.20 | $2.50 | GPT-5.6 Luna lower |
| Context Window | 1.05M | 256k | GPT-5.6 Luna (1.05M) |
| Max Generation Tokens | 16.4k | 384k | DeepSeek-V4.1-Reasoner |
| Tokenizer Family | OpenAI o200k_base (200k vocabulary) | DeepSeek Byte-Level BPE (102k vocabulary) | — |
Opt for GPT-5.6 Luna when your engineering requirements prioritize OpenAI's ecosystem, specific tokenizer efficiencies (Exact BPE (o200k_base)), or when your expected prompt-to-completion ratios favor its $0.2/M input rate.
Opt for DeepSeek-V4.1-Reasoner when looking for DeepSeek's tooling integration, specific context window depth (256k tokens), or when output generation volume favors its $2.5/M rate.