DeepSeek Timeline: Model Release Dates and Key Milestones

DeepSeek is a Chinese AI company founded in Hangzhou in 2023. Its first public model family, DeepSeek Coder, arrived on November 2, 2023. The company then expanded into general language models, mathematical reasoning, formal proof, vision, document recognition, and dedicated reasoning models.

DeepSeek-V4.1-Flash-1017 is the latest DeepSeek model update, released on October 17, 2026. It is available in the App, on the web, and through the deepseek-flash API name. The model supports native image understanding and a one-million-token context window.

Between DeepSeek Coder and V4.1 Flash-1017, the company repeatedly folded ideas from its specialist research into its main model line. Its developer tools now include DeepSeek Harness for AI agents and DeepInfer, the inference framework released on October 20, 2026.

Last Updated: October 20, 2026

DeepSeek Release Dates

DeepSeek Release History

Current DeepSeek Model Lineup

DeepSeek-V4.1-Flash-1017 is the current model behind deepseek-flash. It supports image input, a 1M context window, and up to 384K output tokens. The V4.1 Flash family introduced a Causal Encoder-Decoder architecture with 552 billion backbone parameters, with 8 billion active per token during input processing and 16 billion during generation. Both the original September 10 checkpoint and Flash-1017 have downloadable weights under the MIT License.

DeepSeek-V4-Pro-0813 is available through deepseek-v4-pro. DeepSeek canceled its planned September 14 retirement and continued the service with unchanged billing. Pro supports text input, a 1M context window, and up to 384K output tokens.

DeepSeek-V4-Flash-0731 and V4-Flash-Vision-Exp are retired. Their legacy API names, deepseek-v4-flash and deepseek-v4-flash-vision-exp, are temporarily accepted for compatibility, but requests now run on V4.1 Flash-1017 and use Flash pricing.

DeepSeek-V3.2 is an older general model whose open weights remain available. V4 and V4.1 have replaced it on DeepSeek’s main API routes. V3.2 added reasoning during tool use and completed the architecture first tested in V3.2-Exp.

DeepSeek Harness, DeepInfer, Search API, and Pricing

DeepSeek Harness, also called dsh, is an open-source agent harness built on the Cordis meta-framework. Models, tools, skills, sessions, sandboxes, filesystems, agent loops, orchestration, and the user interface are implemented as replaceable plugins.

DeepInfer launched on October 20, 2026. DeepSeek released the inference framework behind its production services under Apache 2.0. The public code supports offline inference for V4.1 Flash on NVIDIA SM100 GPUs and Huawei Ascend 950 NPUs. It includes distributed inference, speculative decoding, and structured outputs. A complete online service still requires a KV cache system, separate vision encoding, prefill, and decode services, and load balancing.

deepseek-recipe accompanied DeepInfer under the MIT License. Its Rust libraries and Python bindings convert Messages, Chat Completions, and Responses requests into DeepSeek prompts or token IDs, then parse model output into the corresponding response format. Developers supply the inference backend, tool execution, and HTTP transport.

DeepSeek adjusted Flash pricing at 12:00 Beijing Time on September 10, 2026. For every one million tokens, V4.1 Flash costs $0.003 for cache-hit input, $0.15 for cache-miss input, and $0.60 for output during off-peak hours. Peak rates are double those amounts. The October 17 model update kept these rates unchanged. Peak hours are 01:00–04:00 and 06:00–10:00 UTC from Monday through Friday, except Chinese public holidays. All other times use off-peak rates.

Search & Fetch launched on October 17, 2026. Search returns structured web results, while Fetch retrieves a page as Markdown for agents and retrieval-augmented generation applications. Each API costs $0.75 per 1,000 successful requests that return results or content. Existing server-side search and fetch tool calls through DeepSeek Harness, Claude Code, and Codex remain free until per-request billing begins at 00:00 Beijing Time on November 1, 2026.

DeepSeek Timeline

2026: DeepSeek-V4, V4.1 Flash, Harness, DeepInfer, and API Changes

2025: R1, Hybrid Reasoning, Agents, and Specialist Models

2024: MoE Research, V2, V3, and the First Reasoning Preview

2023: DeepSeek Begins with Code and General Language Models

DeepSeek Model Families Explained

V Series

The V series is DeepSeek’s main general-purpose model line. V2 introduced the MoE and attention architecture that shaped later releases. V2.5 combined general and coding abilities, V3 increased the scale, V3.1 merged thinking with regular response modes, and V3.2 brought reasoning into tool use. V4 divided the main line into Pro and Flash models. V4.1 Flash introduced a Causal Encoder-Decoder design and native image understanding. Its global KV cache uses 890 bytes per token, about one quarter of V4 Flash’s footprint.

R Series

The R series focuses on reasoning. R1-Lite-Preview was the first hosted preview. R1-Zero tested reinforcement learning without supervised fine-tuning as the first post-training stage, while R1 added cold-start data to improve readability and consistency. The six distilled R1 models transferred parts of that reasoning behavior into smaller Qwen- and Llama-based models.

Coder, Math, and Prover

DeepSeek Coder and Coder-V2 were built for programming. DeepSeekMath concentrated on competition-style mathematical problems expressed in natural language. DeepSeek-Prover targeted formal proofs that a Lean proof assistant could check. These branches influenced the general models even when their names disappeared from the main API.

VL, Janus, and OCR

DeepSeek-VL and VL2 handle vision-language understanding. Janus combines image understanding with image generation, but uses different visual encoding paths for the two jobs. DeepSeek-OCR treats document images as compressed carriers of text and layout. V4-Flash-Vision-Exp brought image input to the main API family as an experiment in August 2026. V4.1 Flash replaced that temporary model with native visual understanding in the general Flash model.

What the Model Suffixes Mean

Five Turning Points in DeepSeek’s History

1. The First Releases Were Specialist Models

DeepSeek did not begin with a consumer chatbot. It released a code model first, then a general language model, followed by separate research families for MoE routing, mathematics, and vision. Each branch tested an idea that could later move into a larger general model.

The specialist work also fed later releases. DeepSeekMath explored reinforcement-learning methods used in R1. DeepSeekMoE supplied the sparse expert design behind V2 and V3. Coder and Math data helped train Coder-V2 and the general models that followed.

2. V2 Established the Efficiency Architecture

V2 was the first general DeepSeek model built around the architecture now associated with the company. Its mixture-of-experts system activated only part of the full parameter set for each token. Multi-head Latent Attention reduced the memory required for the key-value cache during inference.

Those choices carried into later releases. V3 used a much larger MoE design, R1 inherited the V3 base architecture, and V4 continued the effort to lower the cost of long-context inference. V2 is the architectural starting point for the later flagships.

3. V3 Became the General Foundation

V3 expanded the MoE design to 671 billion total parameters while activating 37 billion for each token. It handled general writing, coding, mathematics, and other text tasks through one model. The DeepSeek App used V3 at launch, and the official deepseek-chat API identifier pointed to V3 at the time.

V3 also became the base for R1. R1 was not a separate architecture built from the ground up. It applied a reasoning-focused post-training process to the capabilities and efficiency of the V3 foundation.

4. R1 Took DeepSeek Beyond Its Developer Audience

R1 made DeepSeek widely known in January 2025. The release combined a large reasoning model, an experimental R1-Zero checkpoint, and six smaller distilled models. Developers could inspect and run the weights, while ordinary users could access reasoning through the DeepSeek service.

5. V3.1, V4, and V4.1 Expanded the Main Model Line

V3.1 reduced the separation between a normal chat model and a reasoning model. One model could answer in thinking or non-thinking mode, and the API identifiers selected the mode. V3.2 extended that design by allowing the model to reason while using tools.

V4 kept both modes and gave Pro and Flash a 1M context window. The July 2026 Flash update strengthened multi-step agent work and added native Responses API support for Codex clients. V4-Flash-Vision-Exp added image input in August, and V4.1 Flash made visual understanding native to the main Flash model on September 10.

Read More: How to use DeepSeek v4.1 Flash in Codex or Claude Code

DeepSeek Timeline FAQs

When was DeepSeek founded?

DeepSeek was founded in Hangzhou, China, in 2023 by Liang Wenfeng. High-Flyer, the quantitative investment firm he co-founded, provided financial backing. No single founding day is consistently documented because the research lab’s creation and its later corporate separation occurred at different stages.

What was the first DeepSeek model?

DeepSeek Coder was the first public DeepSeek model family. It was released on November 2, 2023, with models for code completion and instruction-based programming tasks. DeepSeek LLM followed on November 29 as the company’s first general-purpose language model family.

When was DeepSeek-R1 released?

DeepSeek-R1 was released on January 20, 2025. The release included DeepSeek-R1-Zero, the main R1 model, and six distilled models based on Qwen and Llama architectures. DeepSeek updated the reasoning family with R1-0528 on May 28, 2025.

Did the DeepSeek App launch with R1?

No. The official DeepSeek App launched on January 15, 2025, and was initially powered by DeepSeek-V3. R1 arrived on January 20 and was then made available through DeepSeek’s web product, App, and API. The two events happened in the same week but were separate releases.

What is the latest DeepSeek model?

DeepSeek-V4.1-Flash-1017 is the latest update, released on October 17, 2026. The App, web service, and deepseek-flash API use this version. V4.1 Flash first launched on September 10, 2026.

Does DeepSeek-V4.1-Flash support images?

Yes. V4.1 Flash accepts JPEG, PNG, GIF, and WebP images alongside text. Images can be supplied as base64 data, an external URL, or a Files API file_id through Chat Completions, Messages, or Responses. The API converts each image into billed input tokens, with an upper limit of 1,024 tokens per image after resizing.

What is the difference between DeepSeek V models and R models?

The V series is DeepSeek’s main general-purpose line. It covers ordinary chat, writing, coding, tool use, and other text tasks. The R series was created for deliberate reasoning, especially mathematics, coding, and multi-step problems. Starting with V3.1, the V series also gained thinking modes, so the boundary is less rigid than it was when R1 launched.

Is DeepSeek open source?

Most DeepSeek models have downloadable weights, technical reports, and permissive licenses. R1, V4, and V4.1 Flash, including Flash-1017, use the MIT License. These are open weight releases; the licenses do not imply that all training data or the complete production service is public. DeepInfer uses Apache 2.0, while deepseek-recipe uses MIT.

What do deepseek-chat and deepseek-reasoner mean?

deepseek-chat and deepseek-reasoner are legacy API compatibility identifiers whose underlying models changed over time. They most recently selected V4-Flash’s non-thinking and thinking modes.

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