DeepSeek R1 8B
DeepSeek's 8B reasoning model distilled from Qwen3 (R1-0528) with strong math, code, and logic.
R1 8B is an open-weight text model from DeepSeek. 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
- May 2025
- Pricing
- Free, runs on your hardware
Samples
Generated with R1 8B 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 Application Programming Interface, or API, is a set of rules and tools that allows different software programs to communicate with each other. It's like a secure messaging system where one program can ask another for specific information or ask it to perform a specific task, without needing to know all the details of how the other program works internally.
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.
I recommend against building your own user authentication system. The single strongest argument against this recommendation is the immense time and resource burden it places on a small two-person team. Developing, maintaining, securing, and scaling authentication internally requires deep expertise in complex areas like cryptography, security protocols, compliance, and user experience. This diverts critical development effort away from your core product. Furthermore, established hosted providers constantly update their security measures to counter new threats and vulnerabilities, offering features and compliance that are hard for a small team to replicate or keep current with. By using a provider, you free up your team's valuable time to focus on building your unique business offering, rather than reinventing the authentication wheel.
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.
AI spending in our company is proving difficult to predict accurately, which creates challenges for budgeting and financial planning. Several factors contribute to this uncertainty. Firstly, AI projects are inherently dynamic. They often begin with a small, targeted goal but frequently expand as we learn more or discover unexpected applications. This scope creep means the initial technical requirements and associated costs can change significantly over time, making early estimates unreliable. Secondly, the technology itself evolves rapidly. What we purchase or license today might be outdated or superseded by cheaper, more powerful alternatives tomorrow. The cost-per-result for many AI tasks can decrease as algorithms improve and scale, which isn't typical for most other technologies. This constant innovation makes forecasting the operational costs tricky. Thirdly, the expertise required is scarce and commands premium prices. Costs aren't just for the software or hardware; they heavily depend on the specialized talent needed to build, train, and manage these systems. Finding and retaining this skill is complex, and the associated human effort translates directly into budget line items that fluctuate. To address this, we need to adopt more structured approaches from the start. Define project scope rigorously, consider potential future needs during initial planning, and actively track the value delivered against the costs incurred. Focus on the business outcomes rather than just the tech acquisition.
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 14B reasoning model distilled from Qwen2.5 with exceptional math and coding benchmarks.
DeepSeek's 32B reasoning model distilled from Qwen2.5 for top-tier chain-of-thought reasoning.