V4 Pro
DeepSeek's high-capability V4 model (MIT) with a 1M-token context, dual thinking modes, and stronger agentic performance than V4 Flash.
From DeepSeek, V4 Pro is a cloud text model. It takes a text prompt and replies with generated text. Its context window handles up to 1M input tokens and up to 384K output tokens, and it supports adjustable reasoning effort for harder problems. Generation can be tuned with system instructions, temperature, and top-p sampling. It runs through Runware using your own API key, from $0.961 per million input tokens.
- Pricing
- $0.961 / 1M in · $1.922 / 1M out
- Context window
- 1M in · 384K out
- Reasoning
- Adjustable effort
- Controls
- System prompt, Temperature, Top-p
Examples
Generated with V4 Pro via Runware. 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 set of rules that lets one piece of software ask another for data or actions. Think of it like a universal handshake between programs, allowing your weather app to request today’s forecast directly from a remote weather service’s computer.
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.
Use a hosted provider. For a two-person startup, your time and focus are everything. Building and maintaining secure authentication is a massive distraction that will slow down shipping your actual product. A hosted service handles the complexity of password hashing, multi-factor authentication, session management, and compliance instantly, getting you to market in a day instead of weeks. Your tiny team cannot possibly match the security expertise and 24/7 monitoring of a dedicated provider, and the cost of a breach in both reputation and legal liability will far exceed any subscription fee. The single strongest argument against this is the risk of vendor lock-in. If your provider raises prices, suffers a prolonged outage, or goes out of business, your entire user base and login system is held hostage. A sudden forced migration off a proprietary system is a terrifying engineering crisis that can stall your business, whereas an in-house system built on standard libraries will always be under your control.
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.
You asked why AI costs are hard to forecast and what to do about it. The difficulty comes from three distinct forces, none of which behave like traditional software expenses. First, the unit economics are slippery because the product itself is probabilistic. A single customer query can trigger a cheap, one-sentence answer or an expensive, multi-step reasoning chain behind the scenes. Your cloud bill is determined by an ongoing negotiation between the user’s ambiguity and the model’s thoroughness, making spend closer to a utility bill for pure thought than a fixed license fee per seat. Second, the underlying model market is a pricing war with no floor. Frontier labs cut API costs by eighty percent overnight or release a new model that is ten times more capable, immediately rendering your previous performance-to-cost assumptions obsolete. You are planning a budget on a graph that looks like a cliff. Third, waste is silent and architectural. As engineers chain together multiple AI calls to handle a task, small failure loops can spin out into costly retries without a visible error message. The meter keeps running invisibly. What you can do is shift the conversation from a fixed budget to a unit cost culture. Tie every dollar to a business metric like successfully resolved customer tickets rather than raw API consumption. Mandate that teams set hard guardrails on the maximum reasoning spend per interaction. Finally, treat model selection as a financial decision, not just a technical one, routinely swapping to cheaper models unless a premium tier demonstrably lifts revenue.
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 ↗