DeepSeek V3.2 matches or beats GPT-4o on math and coding benchmarks and costs about 9x less at the API level. The model still trails GPT-5.5 on complex reasoning and lacks the multimodal features ChatGPT has built up over two years. Whether that tradeoff works for you depends mostly on your use case and whether open-source deployment matters to your workflow.

What DeepSeek is in 2026

DeepSeek started as a Chinese AI lab that shocked the market in early 2025 with R1 — an open-source reasoning model that matched GPT-4 performance at a fraction of the cost. By 2026 the lab has shipped V3.2 (a chat model) and V4 (1M token context, strong on SWE-bench), while R2 remains unreleased publicly.

DeepSeek V4 scores 83.7% on SWE-bench Verified, ahead of V3's 73.1% and close to GPT-5.5's results. DeepSeek R1 scores 97.3% on MATH-500 compared to GPT-4o's 60.3%. On math benchmarks specifically, DeepSeek is genuinely world-class.

The user base has grown fast: approximately 125 million monthly active users and 170,000+ GitHub stars as of March 2026, making it the most popular open-source AI project by that measure.

Head-to-head by task

Math and quantitative reasoning: DeepSeek wins. The R1 model's MATH-500 score of 97.3% versus GPT-4o's 60.3% isn't close. For students, researchers, or anyone working with quantitative problems, DeepSeek's math capability is exceptional.

Coding: DeepSeek V4 at 83.7% on SWE-bench Verified is competitive but still trails Claude Opus 4.8 (88.6%) and is roughly comparable to GPT-5.5. For standard coding tasks it works well; for the hardest multi-file engineering problems, Claude Code still has the edge.

General conversation and reasoning: ChatGPT with GPT-5.5 is better here. DeepSeek V3.2 is a strong general model, but the reasoning depth on complex multi-step problems doesn't match the latest OpenAI frontier model.

Multimodal tasks: ChatGPT has Sora for video, DALL-E for images, and Advanced Voice mode. DeepSeek doesn't compete on this dimension.

API cost: processing 100 million input tokens through GPT-4o costs $250. The same through DeepSeek V3 costs $27 — a 9.3x difference. At scale, this is a big deal.

The open-source advantage

DeepSeek's models are fully open-weight, which means you can download them and run them on your own infrastructure. This matters in specific situations: companies with strict data residency requirements, teams that need to fine-tune on proprietary data without sending it to an external API, and applications where the variable cost of external API calls needs to be eliminated.

For most individual users this doesn't come into play. But for developers and teams, it's a meaningful differentiator that no closed model can match.

What ChatGPT still does better

GPT-5.5 is a better general reasoning model for complex, multi-step tasks. The Sora integration for video and DALL-E for image generation have no DeepSeek equivalent. ChatGPT's Custom GPT ecosystem, the Assistants API, and the code interpreter are mature features that took years to build.

If your workflow depends on image input/output, video generation, or the broader OpenAI plugin/tool ecosystem, ChatGPT is the right choice and there's no DeepSeek equivalent to switch to.

The honest recommendation

Use DeepSeek if math and quantitative reasoning are central to your work, you need self-hosted deployment for data privacy, API cost at scale is a real constraint, or you want a free alternative to ChatGPT for general use.

Use ChatGPT if you need multimodal capabilities, general reasoning and writing quality matter more than math benchmarks, you rely on the OpenAI ecosystem tools, or you want access to the latest GPT-5.5 capabilities.

For teams using AI at scale, the combination often makes practical sense: DeepSeek for high-volume math, data processing, and self-hosted deployments; ChatGPT for customer-facing or reasoning-intensive tasks. The cost gap is wide enough to warrant building workflows around the right model for each job.

Privacy note

DeepSeek is based in China, which raises legitimate data privacy questions for some organizations. The company's privacy policy is subject to Chinese law. For consumer use this is a personal risk assessment; for enterprise deployments it warrants legal and compliance review before production deployment.