DeepSeek R1 14B
DeepSeek's 14B reasoning model distilled from Qwen2.5 with exceptional math and coding benchmarks.
DeepSeek's R1 14B is an open-weight text model. It takes a text prompt and replies with generated text. It runs entirely on your own hardware through Ollama: no API key and no per-use fees.
- Released
- Jan 2025
- Pricing
- Free, runs on your hardware
Samples
Generated with R1 14B via Ollama. The same three prompts run against every text model in the catalog, shown verbatim, so the only thing that changes between two models’ answers is the model.
Explain what an API is to someone who has never written code. Do not use a restaurant or waiter analogy. Plain prose only — no headings, bullet points, or markdown formatting. Maximum 50 words.
An API is a way for different systems or apps to communicate and share data or functionality without needing to understand each other's inner workings. It's like a bridge that lets them work together smoothly.
A two-person startup is deciding whether to build their own user authentication or use a hosted provider. Give them a clear recommendation, then the single strongest argument against your own recommendation. Plain prose only — no headings, bullet points, or markdown formatting. Maximum 150 words.
The two-person startup should use a hosted provider for user authentication. This approach saves time, leverages expertise, and avoids scalability and security challenges. However, the strongest argument against this recommendation is the risk of vendor lock-in, which could limit flexibility and increase dependency on a third-party service.
Write a briefing for a non-technical executive explaining why their company's spending on AI is hard to predict, and what they can do about it. Cover at least three distinct causes. Plain prose only — no headings, bullet points, or markdown formatting. Write exactly 205 words.
Predicting your company’s AI spending is challenging because AI projects often evolve unpredictably, and their costs depend on factors that are hard to forecast. Three main reasons stand out: First, the scope of AI use cases isn’t always clear at the start. Early efforts might reveal new opportunities or problems that require additional tools or expertise, leading to unexpected expenses. Second, AI is a long-term investment. Implementing AI systems can take months or years to deliver measurable results, making it hard to predict when—or if—the payoff will outweigh the costs. Third, the AI landscape changes rapidly. New tools, algorithms, and frameworks emerge frequently, which can make existing investments obsolete or require costly upgrades to stay competitive. To manage this uncertainty, consider these steps: - **Define clear use cases early** to set boundaries and prioritize projects that align with your goals. - **Set flexible budgets** that allow for adjustments as new opportunities or challenges arise. - **Invest in monitoring and evaluation** to track the ROI of AI initiatives and identify when course corrections are needed. By adopting these strategies, you can better navigate the unpredictable nature of AI spending and maximize your returns.
DeepSeek
DeepSeek is a Chinese AI lab whose open-weight models — from the R1 reasoning distills to the V4 mixture-of-experts family — bring frontier-grade reasoning under permissive (MIT) licensing.
www.deepseek.com ↗DeepSeek's fast mixture-of-experts model and the first on its Causal Encoder-Decoder architecture, with image input, a 1M-token context and native tool calling.
DeepSeek's high-capability V4 model (MIT) with a 1M-token context, dual thinking modes, and stronger agentic performance than V4 Flash.
DeepSeek's compact 1.5B reasoning model distilled from Qwen2.5 — chain-of-thought on-device.
DeepSeek's 8B reasoning model distilled from Qwen3 (R1-0528) with strong math, code, and logic.
DeepSeek's 32B reasoning model distilled from Qwen2.5 for top-tier chain-of-thought reasoning.