GPT-5.2 vs DeepSeek R1

GPT-5.2 and DeepSeek R1 come from different labs, which means different tokenizers, different API shapes, and a second vendor relationship. The same English text does not produce the same token count on both, so a price-per-million comparison understates the difference — measure your own prompts on each before treating the headline rates as the full story.

GPT-5.2 and DeepSeek R1 were released within 11 months of each other, so they are competing on the same evaluations under roughly the same conditions. That makes a direct benchmark comparison meaningful here in a way it usually is not — neither model has the advantage of being measured on a newer, easier set of tests.

GPT-5.2 carries the larger context window at 400K tokens versus 128K for DeepSeek R1. The gap is real but not decisive — it matters if your prompts routinely run long, and is irrelevant if they sit where most production prompts sit, well under 100K. Bear in mind that filling a large window is also what makes a request expensive.

Latency separates them: GPT-5.2 scores 85/100 against 60/100 for DeepSeek R1, a 25-point gap. That matters for anything a person waits on — chat, autocomplete, interactive tools. For batch and background work it does not, and trading latency for capability there is usually the right call.

One practical asymmetry: GPT-5.2 offers a batch API at 50% off standard rates, and DeepSeek R1 does not. For anything that does not need an answer immediately — nightly enrichment, backfills, evaluation runs — that discount can be worth more than the difference in list price, and it is easy to overlook when comparing headline rates.