DeepSeek-V4-Flash-0731 (all 284B parameters) running on a single DGX Spark. ⚡ ~17 tok/s generation, 40 tok/s prefill 📦 One 80GB GGUF file, mixed IQ2_XXS/Q8 imatrix quant 🧠 256-expert MoE, 6 routed per token 🆓 MIT licensed frontier model (kinda) on your desk 😎 🤗

DeepSeek v4 Flash is actually pretty good. Is it as good as the benchmarks claim? No. It is benchmaxxed like everything else shipping right now. But it is a 284B parameter model. At that size it has no business performing this well. It beats GPT 5.6 Luna in real use. A model a fraction of the size, from a lab a fraction of the budget. DeepSeek keeps doing more with less than anyone in the world. Very impressed.

DeepSeek V4 Pro costs 69× less than Fable 5 per task according to this chart. That gap may be one of the strongest arguments for open-weight AI. So how does DeepSeek make it so cheap? ↓ V4 Pro has 1.6 trillion parameters, but its MoE architecture activates only 49 billion per token. In other words: it gets the capacity of a gigantic model without running the entire model for every word. Then it cuts the inference bill further: • Sparse, compressed attention reduces long-context compute • FP4/FP8 precision lowers memory and bandwidth requirements • Prefix caching makes repeated inputs dramatically cheaper • Open weights let the entire ecosystem optimize deployment Cached API input is also priced roughly 120× lower than uncached input.

DeepSeek works best in Command Code. two ways to prove it: $1 Go plan with $10 → $40 for DeepSeek V4 pro read this harness engineering deep dive below: on how we fix and repair 50K+ tool calls, saving you cost and improve speed & quality of outputs.

DeepSeek has officially transitioned its highly publicized, limited-time 75% promotional discount into a permanent, market-disrupting price reset. This strategic shift permanently locks in a remarkably low rate of just 88 cents ($0.88) per million output tokens for its flagship V4 Pro model, establishing this rate as the new standard pricing after the initial promotional period concludes on May 31, 2026. This aggressive pricing maneuver is set to redefine the economics of generative AI development, allowing enterprises and developers to scale high-performance applications at a fraction of the cost of traditional market alternatives. DeepSeek V4 Pro commercial pricing metrics, global AI API cost comparison analysis, breaking China AI industry news, DeepSeek vs ChatGPT market positioning, hardware acceleration utilizing Huawei Ascend chips, the evolution of global AI infrastructure in 2026, identifying the cheapest high-performance AI model API, and the industry impact of DeepSeek's permanent discount strategy.

DeepSeek V4 Flash is now live on Flap AI Oracle. A stronger intelligence layer for on-chain applications: • Advanced reasoning capabilities • More stable multi-step decision execution • Better support for complex prompt design Build AI-native tokens and logic directly on-chain — DeepSeek V4 Flash 已接入 Flap AI Oracle 链上智能层不断优化： • 更强的推理能力 • 更稳定的多层决策执行 • 更复杂的 Prompt 设计支持 建设AI 原生的链上逻辑与代币机制 —

DeepSeek v4 works fine, but it’s not the frontier-pressing moment we saw with Kimi 2.6. On Notion eval data, it’s similar performance to GPT 5.2, with understandable failings. Most interesting — it doesn’t scale well. It’s ridiculously slow. On multiple major, trusted, and performant US inference providers we see it 15x slower than GPT 5.2 and 2x slower than Opus 4.7, a problem Kimi never had. Curious if it’s a fundamental issue in architecture, or a matter of time til inference providers make it work. Doesn’t seem urgent either way, if Kimi can outperform. Cheaper maybe, but not groundbreaking.