CURATED TIMELINE · EDITORIAL EDITION

DeepSeek Before the Breakout

The January 2025 sensation did not appear overnight. Trace the open-weight code models, sparse experts, GRPO, MTP, V2 efficiency work, R1 preview, V3, the free app, and the App Store shock — while separating reported training-run cost from total R&D and open weights from full open source.

Timeline overview

At a glance

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2023.112025.01

11.02DeepSeek Coder 01.11DeepSeekMoE 02.05DeepSeekMath / GRPO 05.07DeepSeek-V2 11.20R1-Lite-Preview 12.26DeepSeek-V3 01.27DeepSeek goes mainstream

10 nodes2023.11—2025.01 · Editorially curated · Updated 2026-07-15

Editorial thread

The timeline

Reviewed event briefs and original editorial context, ordered to show how the story changed over time.

01
2024-01-11Paper & weights release
DeepSeek publishes the sparse architecture behind its later models
DeepSeekMoE uses fine-grained experts and shared expert isolation to improve sparse-model efficiency, with 16B weights and training code released.
Editorial context
Efficiency became a research program. DeepSeekMoE split experts more finely and isolated shared knowledge so only a small slice of a large model had to run for each token. That line later fed directly into V2 and V3.

Export controls are part of the operating context, but public evidence does not prove that each architectural idea was caused by them. The defensible claim is that efficiency became central under constrained hardware conditions.
**Efficiency became a research program.** DeepSeekMoE split experts more finely and isolated shared knowledge so only a small slice of a large model had to run for each token. That line later fed directly into V2 and V3. Export controls are part of the operating context, but public evidence does not prove that each architectural idea was caused by them. The defensible claim is that efficiency became central under constrained hardware conditions.
Read event
02
2023-11-02Open-source release
DeepSeek starts with an open-weight code model
DeepSeek releases code models from 1.3B to 33B parameters, making open weights and code part of its product strategy from the outset.
Editorial context
The origin story is developer credibility, not an overnight consumer hit. High-Flyer supplied capital and early compute, but DeepSeek operated as a dedicated AI research effort. Calling it a quant fund's casual side project understates the sustained work that followed.

Its first model set the distribution strategy: release weights, code, and technical material to developers before building a mass-market chat product.
**The origin story is developer credibility, not an overnight consumer hit.** High-Flyer supplied capital and early compute, but DeepSeek operated as a dedicated AI research effort. Calling it a quant fund's casual side project understates the sustained work that followed. Its first model set the distribution strategy: release weights, code, and technical material to developers before building a mass-market chat product.
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DeepSeek publishes the sparse architecture behind its later models

DeepSeek starts with an open-weight code model