# DeepSeek V4 Flash officially launches at $0.14 per million input tokens

> DeepSeek has made its V4 Flash model official, and the pricing is the headline: 14 cents per million input tokens on a cache miss, dropping to a fraction of a cent on a cache hit, with output at 28 cents. For a capable agentic model, that is aggressive. The technical trick is efficiency. V4 Flash activates just 13 billion of its 284 billion parameters, yet DeepSeek claims it beats the heavier 49-billion-active V4 Pro preview across all nine of its agentic benchmarks, including 82.7 percent on Terminal Bench 2.1. The gains came from redoing post-training with a focus on agent performance rather than changing the architecture. It ships with a 1M-token context window and MIT-licensed open weights. The pattern is familiar by now: a Chinese lab releasing open weights that undercut Western pricing while chasing frontier capability. For developers weighing cost against performance, cheap open models like this keep tightening the squeeze on closed APIs, and they keep doing it out in the open.

_Section: [Daily AI Updates](https://www.wortins.com/daily-ai) · Source: Hugging Face · Published Sunday, August 9, 2026_

## Wortins' read

DeepSeek has made its V4 Flash model official, and the pricing is the headline: 14 cents per million input tokens on a cache miss, dropping to a fraction of a cent on a cache hit, with output at 28 cents. For a capable agentic model, that is aggressive. The technical trick is efficiency. V4 Flash activates just 13 billion of its 284 billion parameters, yet DeepSeek claims it beats the heavier 49-billion-active V4 Pro preview across all nine of its agentic benchmarks, including 82.7 percent on Terminal Bench 2.1. The gains came from redoing post-training with a focus on agent performance rather than changing the architecture. It ships with a 1M-token context window and MIT-licensed open weights. The pattern is familiar by now: a Chinese lab releasing open weights that undercut Western pricing while chasing frontier capability. For developers weighing cost against performance, cheap open models like this keep tightening the squeeze on closed APIs, and they keep doing it out in the open.

## Source

[Read the full story at Hugging Face](https://huggingface.co/blog/ResterChed/deepseek-v4-flash-official-release)

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_Curated and written by [Wortins](https://www.wortins.com) — The daily AI briefing. Every story links to its original source; the "Wortins read" on each is our own original analysis. [About Wortins & our editorial approach](https://www.wortins.com/about)._