13 results for “DeepSeek V4”
DeepSeek V4 Flash is positioned as a fast, lower-cost open-weight AI model for developers who need long-context reasoning without paying frontier-model. The model is described as a Mixture-of-Experts system with 284 billion total.
DeepSeek's next flagship model, DeepSeek V4, appears to be on track for an official launch on August 3, 2026, according to multiple converging signals. The competitive landscape DeepSeek V4 enters is markedly different from the one.
September 2026 watch through 14 Sep: Daybreak keys passed, DeepSeek V4.1-Flash live with Pro remap, Astra and Agents API surfaces landed — Gemini 4 and Apple China launch timing still open.
DeepSeek’s official page confirms V4.1-Flash is live as deepseek-flash with a 552B MoE encoder–decoder stack. Pro requests remap to Flash rates from 04:00 UTC on September 14 until V4.1-Pro.
Chinese AI startup DeepSeek has released its V4-Flash model at a price point that dramatically undercuts every major competitor, according to benchmark. The emergence of V4-Flash accelerates a structural shift in the AI market.
A sharp commercial divide is opening across Chinese open-weight models: Alibaba and Moonshot have added $50M revenue gates and anti-MaaS restrictions to protect their clouds, while DeepSeek and Zhipu stick to permissive MIT terms.
DeepSeek Flash, Qwen3.8-Flash, and GLM-5.3-Flash still price near $0.15/MTok input — while Opus and Sol sit at flagship multiples. Brocker’s baseline table is refreshed from vendor pages as of 15 September 2026.
NVIDIA’s DSX bet is tokens per megawatt: Lambda reports 24% more HGX B200 throughput at 85% power. Eos + Emerald cut 4 MW to 3 MW for the grid — Flex-pattern proof, not a shipped DSX Flex site.
DeepSeek released DeepSeek Harness (dsh) in developer preview on 13 August 2026, an MIT-licensed agent runtime where models, tools, and UI are all plugins. This design lets teams assemble internal coding agents from swappable parts.
AI model costs are forcing OpenAI, Meta, xAI and other major labs to shift their sales pitch from raw capability toward cheaper intelligence per task. Enterprise AI bills have become a board-level concern.
Tencent open-sourced Hy4 preview, a 770B-parameter MoE model with 49B activated weights and a 1M-token context window. The Apache 2.0 release puts a frontier-class model within reach of self-hosted inference stacks.
Z.ai released GLM-5.2, a 753B open-weight model with MIT licensing that matches Anthropic's Opus 4.8 on agentic benchmarks at roughly one-fifth the token cost. The 1M-context model is already running in Cursor and Claude Code, shifting enterprise leverage toward self-hosted inference.