DeepSeek V4.1 Flash
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 V4.1 Flash is a cloud text model. It is multimodal: alongside a text prompt it accepts images, then replies with generated text. Its context window handles up to 1.05M 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, OpenRouter, and DeepSeek using your own API key, from $0.15 per million input tokens.
- Released
- Sep 2026
- Pricing
- $0.15 / 1M in · $0.60 / 1M out
- Inputs
- Text, Images
- Context window
- 1.05M in · 384K out
- Reasoning
- Adjustable effort
- Controls
- System prompt, Temperature, Top-p
Examples
Generated with V4.1 Flash 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 two computer programs talk to each other. It defines what requests one program can make and what answers it will get back, so software can share features and data without knowing internal details.
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 authentication provider. A two-person team should spend its limited engineering time on the product's core differentiator, not on password reset flows, MFA, session management, compliance, and the endless security patches that authentication demands. Hosted providers give you battle-tested security and faster time to market at low upfront cost. The single strongest argument against this recommendation is vendor lock-in: migrating user identities, password hashes, MFA enrollments, and integrations away from a provider can be painful and expensive, and the provider can change pricing or terms once you depend on it. That risk is real, but for most early startups the speed and security gains outweigh it. You can mitigate it by keeping a thin abstraction layer and exporting user data regularly.
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.
Your company's AI spending is difficult to forecast for three main reasons. First, the technology changes monthly. New models, tools, and providers appear constantly, and prices for computing power and APIs can fall or spike unpredictably. A plan built today may be obsolete next quarter. Second, data readiness is uncertain. Cleaning, labeling, integrating, and securing data often takes longer and costs more than expected, and hidden quality problems emerge only during implementation. Third, adoption and usage are unpredictable. If employees use AI features heavily, cloud and API bills scale with every query, while productivity gains arrive unevenly. Talent costs and change management also add variance. To manage this, stop treating AI as a fixed annual line item. Instead, create a flexible budget with clear spending gates, fund small pilots before scaling, and track value through specific business metrics like hours saved or error reduction. Negotiate contracts with usage caps and exit clauses. Build a cross-functional review board to reassess costs quarterly. Most importantly, accept that uncertainty is inherent. By funding options rather than a single monolithic plan, you can capture AI's upside without exposing the company to runaway costs or stalled experiments. Review these assumptions every quarter with finance and engineering leaders and adjust accordingly.
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 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 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.