DeepSeek vs ChatGPT

This comparison crosses a bigger divide than most. ChatGPT is OpenAI's consumer app, powered by the closed GPT-5 family you rent through an API. DeepSeek is best known for its models rather than its app: open-weight releases that anyone can host, which made the company famous when its R1 reasoner matched the closed frontier reasoners of its day.

In practice that difference is the whole story, because a closed model ties you to one operator while an open-weight model is something the market competes to serve. Below: what the apps cost, the model families tier by tier with live pricing, and the question most DeepSeek searches are really about, which is where your data goes.

The GPT-5 models behind ChatGPT are closed: only OpenAI runs them, and every request goes through OpenAI. DeepSeek's models are open weights, meaning the company publishes the model itself and anyone with the hardware can run it, which is why independent providers all over the world serve DeepSeek models.

Unlike the apps, the models are metered per million tokens (a token is a short chunk of text, roughly three quarters of a word). The board below compares the families as you buy them through an API, where open versus closed decides who processes your data and at what price.

DeepSeek's engineering is pointed at one target: frontier reasoning at a cost that makes million-token context practical. DeepSeek V4 Pro is a 1.6 trillion parameter sparse mixture-of-experts model that activates 49 billion parameters per token and runs on roughly a quarter of the previous generation's per-token compute, while V4 Flash pushes the same idea further, around a tenth of the older model's inference compute with the full 1M token window intact. For text-heavy reasoning and agent loops at scale, the cost per solved task is DeepSeek's strongest argument.

The open weights are the other half. Because anyone can host the models, you choose the operator, the region and the retention policy, instead of accepting the model maker's. DeepSeek also ships its own agentic coding harness, DeepSeek Harness, an open coding agent you can point at any of these models.

The GPT family is broader in what it accepts and what it can drive. DeepSeek's models are text-first, while every current GPT tier takes images, and GPT-5.6 Sol leads long-horizon agentic evaluations across terminal work, browsing and computer use. GPT-5.3 Codex holds state-of-the-art results on SWE-Bench Pro and Terminal-Bench, and the tooling ecosystem around the GPT lineup, from structured output to agent frameworks, is deep and battle-tested.

If the deciding factor is polish across modalities and agent depth rather than cost per token, the GPT lineup keeps the edge.

Most DeepSeek vs ChatGPT searches carry an unspoken second question: does using DeepSeek mean sending data to China? Using DeepSeek's own app or first-party API means dealing with the model maker directly, and its terms. Using the open weights does not. On Opper, DeepSeek models are served entirely by independent hosting providers, and DeepSeek the company is not among them. Each model page lists every route with its provider, region, retention posture and training policy, so you can see exactly who would process a request, and where, before you send it.

For the wider compliance picture, one DPA covering every model and route on the gateway, see the AI security and compliance page.

Send DeepSeek the text-heavy reasoning at volume: agent loops, analysis, extraction and long-document work where cost per task compounds and multimodal input isn't needed. Keep the GPT lineup for multimodal pipelines, computer-use agents and the hardest agentic coding. Since both are one model-name change apart on the same API, the cheapest experiment is to run your own evaluation set through both and let the results decide.

The LLM leaderboard shows where the current versions rank on independent benchmarks, and the head-to-head pages below carry the full spec, pricing and privacy detail.