DeepSeek's most downloaded model is 19 months old — its newest flagship build is pulled 32 times less often a day

Every model page on Hugging Face carries a 30-day download counter. Read on 25 August 2026, the counter for DeepSeek does not point at anything the lab released this year. The list is led by DeepSeek-R1, published on 20 January 2025 and last modified on 27 March 2025: 5,043,036 downloads in thirty days, roughly 168,000 a day. The lab's current flagship build, DeepSeek-V4-Pro-0813, went online on 13 August 2026 and stands at 63,058. That repository is only twelve days old, so the honest comparison is per day — about 5,300, some 32 times fewer than a model that is nineteen months old. Age is not the only thing the counter measures. The April repository of the same flagship, DeepSeek-V4-Pro, is still pulled about 34,000 times a day: six times more than the newer build it was superseded by. Download counts follow repository names, and repository names sit hard-coded in pipelines, notebooks and container images that nobody rewrites when a lab ships a fresh snapshot. The second gap is about running costs. DeepSeek-V4-Flash-0731, the small fast model, runs at roughly 131,000 downloads a day — twenty-five times the flagship build, and it was published two weeks earlier. The pattern is not DeepSeek's alone. Alibaba's most downloaded model is Qwen3-0.6B, the smallest member of the family and sixteen months old, with 24.3 million pulls in thirty days. Meta's leader is Llama-3.2-1B-Instruct (7.5 million), from September 2024. OpenAI's gpt-oss-20b (7.0 million) is ahead of the six-times-larger gpt-oss-120b (5.0 million) by 40%. Mistral's most downloaded model remains Mistral-7B-Instruct-v0.3, released in May 2024, at 3.4 million. What this ranking is not: a quality table. A download is a copy of the weights fetched to a machine — it counts people who run models themselves, and completely misses everyone who buys the same models through an API. Continuous-integration jobs and mirrors inflate the figures for popular repositories. What it does measure is which weights are cheap enough to run: models that fit on one graphics card get pulled in the millions, while a trillion-parameter flagship is downloaded by the few who own a server rack — and by everyone else it is rented, not fetched. All figures were read from the Hugging Face model API on 25 August 2026; the counters cover the preceding thirty days.