# DeepSeek V4 Ships: Liang Wenfeng's Reported AGI-First Bet

DeepSeek V4 launched mid-July with two MIT-licensed variants at commodity pricing. A circulated investor transcript reportedly reveals founder Liang Wenfeng's AGI-first strategy: open source forever, profit later.

By Neomanex. Updated 2026-10-07.

**Verdict: DeepSeek just did two things at once — shipped a near-frontier model at commodity pricing, and published what may be the most strategically significant Chinese AI founder statement of 2026. The transcript remains unconfirmed by DeepSeek.**

In mid-July, [DeepSeek V4](/models/deepseek-v4-pro)'s official release shipped with two variants: Pro (1.6T total, 49B active) and Flash (284B total, 13B active), both MIT-licensed with a 1M-token context window. Pro hits SWE-bench Verified 80.6% and 3,206 Elo on Codeforces at $0.435/$0.87 per million tokens — roughly 1/57th of Fable 5's output cost. Flash delivers 79.0% SWE-bench Verified at $0.14/$0.28.

The same week, a nearly 4-hour investor transcript from founder Liang Wenfeng — transcribed by Tencent Tech and analyzed by HelloChinaTech — reportedly laid out DeepSeek's strategy with unusual directness: AGI is the only goal, open source is permanent, and China's AI gap with the US collapses to a single variable. **DeepSeek has not confirmed the record.**

## What DeepSeek V4 shipped

**The launch.** DeepSeek V4's official release shipped in mid-July in two variants. Pro activates 49B of 1.6T parameters per token; Flash activates 13B of 284B. Both use a hybrid attention architecture — Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA) — that cuts single-token inference FLOPs to 27% of V3.2's cost and shrinks KV cache to 10% ([DeepSeek V4 Pro Model Card](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro), 2026). The model is the strongest open-weight coding release available.

**Pricing.** DeepSeek's official API rates: V4 Pro at $0.435/M input (cache miss) and $0.87/M output; V4 Flash at $0.14/M input and $0.28/M output ([DeepSeek API Docs](https://api-docs.deepseek.com/quick_start/pricing/), 2026). Cache-hit input drops to $0.003625/M for Pro and $0.0028/M for Flash. Both offer a 1M-token context, 384K max output, thinking and non-thinking modes, and OpenAI-compatible endpoints.

| Model | Output price / 1M tokens |
|---|---|
| DeepSeek V4 Flash | $0.28 |
| DeepSeek V4 Pro | $0.87 |
| GPT-5.6 Sol | $30 |
| [Claude Fable 5](/models/fable-5) | $50 |

**Legacy endpoint retirement.** The `deepseek-chat` and `deepseek-reasoner` endpoints permanently shut down today, July 24 at 15:59 UTC ([DeepSeek V4 Preview Release Notice](https://api-docs.deepseek.com/news/news260424/), 2026). Update your `model` field to `deepseek-v4-flash` (or `deepseek-v4-pro` for heavier reasoning). Your `base_url` stays the same — only the model identifier changes.

## The Liang Wenfeng transcript

In a circulated 118-item investor transcript published by Tencent Tech and analyzed by HelloChinaTech, DeepSeek's founder covered AGI strategy, chip supply, pricing, team retention, and the company's $7.4B fundraise with unusual candor. **DeepSeek has not confirmed the record.**

**On the US–China gap:** According to the transcript, Liang reportedly said all differences — talent, model capability, applications — "can be attributed to differences in compute resources" ([HelloChinaTech](https://hellochinatech.com/p/deepseek-liang-wenfeng-transcript), 2026). He estimated China trails the US by 6 to 18 months, with the entire deficit collapsing to usable compute — not talent, not ambition, not strategy.

**On scaling:** According to the transcript: "We believe in scaling. Bigger is always better. What stops us from scaling is compute, not desire. We train a model at this size not because it is enough, but because that is all our resources allow."

**On Huawei:** According to the transcript, Liang claimed the Ascend 950 "super-node can fully replace Nvidia GB200 and GB300 in performance and price," asserting a 4:1 chip ratio. He disclosed that DeepSeek already runs on a custom compiler stack called TileLang: "We already barely depend on Nvidia's ecosystem" ([HelloChinaTech](https://hellochinatech.com/p/deepseek-liang-wenfeng-transcript), 2026).

**On open source:** According to the transcript: "Our strongest model will probably be open-sourced too. I can't see what good closed-source does." He cited ByteDance: "Their model is closed-source. What is the benefit? I see none."

**On pricing:** According to the transcript, Liang described a simple formula — buy equipment, recover the cost in 10 months. "I could raise the price by half, or double it, and token consumption would barely change" ([HelloChinaTech](https://hellochinatech.com/p/deepseek-liang-wenfeng-transcript), 2026). The company chooses not to.

**On AGI vs. profit:** According to the transcript: "Restraint is a strategy. Sometimes you give up some things to gain more of others." Liang positioned DeepSeek's narrower commercial ambition as a deliberate choice to maximize the probability of reaching AGI. He also disclosed the fundraise "substantially relieved" retention risk by funding large option grants for researchers.

## Why it matters

The V4 launch and Liang's transcript land in a week where Kimi K3 open weights are available, [Claude Opus 5](/models/claude-opus-5) is leaking across providers, and Washington is actively weighing sanctions on Chinese AI models. If confirmed, Liang's message is a direct rebuttal to the sanctions narrative: China's AI capability is real, it's open-weight, it's cheap, and the compute gap is the only bottleneck.

For developers, the economics shift the entire cost model for high-throughput agent pipelines. V4 Pro at $0.87/M makes batch inference, agentic coding, and RAG pipelines dramatically cheaper than any Western frontier API. The MIT license means self-hosting is legal and practical.

For the industry, what may be the most strategically significant Chinese AI founder statement of 2026 — if confirmed — is now circulating. It will be cited in policy debates, investment memos, and competitive strategy decks for months.

## What changes for you

**1. Migrate your API calls immediately.** The legacy `deepseek-chat` and `deepseek-reasoner` endpoints retire today, July 24 at 15:59 UTC. Update the `model` field in your API calls to `deepseek-v4-flash` (or `deepseek-v4-pro` for heavy reasoning). Your `base_url` stays the same.

**2. Evaluate Pro vs. Flash for your workload.** Flash delivers near-Pro quality at roughly one-third the cost for high-throughput tasks — chat, classification, simple code completion. Pro is built for depth: tasks requiring more than ~2,000 tokens of reasoning before generating an answer. Test both on your own workload; the CoT style shift (GA uses "I'm"/"I'll" instead of "Let me") confirms you're on the new version.

**3. Consider self-hosting.** Both models are MIT-licensed with weights on Hugging Face. For regulated workloads or data-residency-sensitive deployments, self-hosting eliminates per-token costs and keeps data off DeepSeek's China-hosted API.

## FAQ

**When do the legacy endpoints stop working?** July 24, 2026 at 15:59 UTC — today. After that, `deepseek-chat` and `deepseek-reasoner` return errors. Switch to `deepseek-v4-flash` or `deepseek-v4-pro` now.

**Should I use V4 Pro or V4 Flash?** Flash for high-throughput, low-latency tasks (chat, classification, simple code completion) at $0.28/M output. Pro for heavy reasoning — complex coding, multi-step agent tasks, math — at $0.87/M output. If your task needs more than ~2,000 reasoning tokens before answering, start with Pro.

**Is DeepSeek V4 open source?** Yes — both variants are MIT-licensed with weights on Hugging Face. Download, modify, and self-host without restriction. The API is pay-per-token at flat rates.

Canonical: https://neomanex.com/news/deepseek-v4-ga-liang-wenfeng-agi-first