Model Detail
DeepSeek-R1
—DeepSeek-R1 is a code generation model with 342.2B parameters released by DeepSeek. The model is registered under the text-generation pipeline tag on Hugging Face, and supports text->text inputs, distributed under the permissive mit license.
DeepSeek-R1 reports a Chatbot Arena ELO of 1,422 across 18,451 votes. Other benchmark slots are still empty in our dataset, so this single figure is best read as a partial picture rather than a full evaluation.
DeepSeek-R1 is priced at $0.7/M input tokens and $2.4/M output tokens. Operationally the model offers a 164K-token context window, which matters when sizing it for prompt-heavy or latency-sensitive workloads. Pricing in this range is the working middle of the API market — neither the cheapest nor the most expensive option per token, so cost-fit is usually a function of how much output you generate.
DeepSeek-R1 ships with 342.2B parameters. The published knowledge cutoff is 2024-07-31, so newer events will not be reflected in zero-shot answers without retrieval. Total weight footprint is approximately 684.5 GB, which is the relevant figure when planning local-inference VRAM. The mit license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
Downloads of DeepSeek-R1 have moved +5.4% over the trailing seven days. The trend is mildly positive, consistent with a model that is being picked up incrementally rather than going viral. These numbers are signal, not guarantee — week-over-week download counts on Hugging Face also reflect mirror traffic, CI scrapes, and one-off benchmarking runs.
DeepSeek-R1 is best fit for code completion, repository-scale Q&A, and pair-programming integrations. It is a less obvious choice for one-shot generation of security-critical code without review. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
Instruction Finetuning DeepSeek-R1-8B Model Using LoRA and NEFTune
arXiv:2606.10392v1 Announce Type: new Abstract: Financial named-entity recognition (NER) is essential for translating unstructured financial reports and news into structured knowledge graphs. However, general-purpose large language models (LLMs) often misclassify financial entities or ignore domain-
Open-R1: a fully open reproduction of DeepSeek-R1
US orders federal agencies to remove China’s DeepSeek AI from their systems
The U.S. has ordered federal civilian agencies to remove DeepSeek products and third-party services built using its technology, citing national security concerns.
Adapting Tree-Structured Speculative Decoding to DeepSeek-V4 for Efficient Inference
arXiv:2609.24698v1 Announce Type: cross Abstract: Repeated execution of the target model during autoregressive decoding is a major source of LLM inference latency. Unlike linear speculation, which follows a single candidate chain, tree-structured speculation retains multiple branches from shared pre
DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression
arXiv:2609.19969v1 Announce Type: new Abstract: The widespread adoption of long-horizon agents has made model workloads increasingly input-heavy. Although prior work has substantially reduced the cost of long-context computation, prefill remains computationally expensive, and large KV caches continu
Anthropic details distillation campaigns from Alibaba, Moonshot AI, and DeepSeek
A new report released Thursday by Anthropic alleges persistent distillation attacks by China-based AI companies, which have escalated in recent months as competition in the space has intensified.
