OpenAI vs DeepSeek

DeepSeek's portfolio focuses exclusively on text-based reasoning models (10 of 11 support reasoning), reflecting a deliberate specialization strategy rather than broad capability coverage. This contrasts sharply with OpenAI's multimodal approach where 65.6% of models support vision, including their top performer GPT-5.4 (67/100 score), making DeepSeek unsuitable for any computer vision, image analysis, or visual AI applications.

OpenAI's lower pricing floor comes from offering 2 free models and leveraging economies of scale across 64 models, while DeepSeek's all-paid portfolio of 11 models maintains a narrower $0.290-$2.50 range. The price gap is particularly striking given DeepSeek's top model (V3.2 Exp) scores only 46/100 versus OpenAI's GPT-5.4 at 67/100, making OpenAI both cheaper at the low end and significantly more capable at the high end.

DeepSeek's open-source-only approach (11 of 11 models) inherently limits performance potential compared to OpenAI's mix of 5 open and 59 proprietary models, where closed development enables optimizations like the 1.1M token context in GPT-5.4 versus DeepSeek's 164K maximum. Additionally, OpenAI's 57 of 64 models with function calling versus DeepSeek's 8 of 11 suggests more sophisticated architectural investments that translate to the 49/100 average score versus DeepSeek's 42/100.

DeepSeek achieves 91% reasoning coverage (10 of 11 models) versus OpenAI's 53% (34 of 64), but this specialization comes at a cost: DeepSeek's best reasoning model still scores 46/100 versus OpenAI's 67/100 top performer. The $0.290 minimum for DeepSeek versus $0.110 for OpenAI means you're paying 2.6x more for inferior reasoning performance, making DeepSeek's narrow focus a poor value proposition even within its specialty.