Last verified: April 25, 2026
Current official status: DeepSeek-V4 Preview is now the latest official DeepSeek general model release. It is available through DeepSeek web/app access and the official API, and DeepSeek has also published open weights for the V4 line. The current official API model names are deepseek-v4-flash and deepseek-v4-pro. The older names deepseek-chat and deepseek-reasoner are legacy compatibility names that currently route to deepseek-v4-flash non-thinking and thinking modes, and DeepSeek says they will be retired after July 24, 2026, 15:59 UTC.
DeepSeek research covers the technical work behind DeepSeek model families, official papers, open-weight releases, reasoning systems, Mixture-of-Experts architecture, long-context efficiency, coding models, math reasoning, vision-language models, OCR/document models, infrastructure projects, and the evolution of DeepSeek’s API models. This independent guide organizes the most important official DeepSeek research resources in one place and connects them to practical Chat-Deep.ai guides.
Independent guide disclosure: Chat-Deep.ai is an independent DeepSeek and is not affiliated with DeepSeek.com, Hangzhou DeepSeek Artificial Intelligence Co., Ltd., the official DeepSeek app, or the official DeepSeek API platform.
What Changed Since the Previous Version of This Page?
The earlier version of this page treated DeepSeek-V3.2 as the most recent official general LLM release and described deepseek-chat and deepseek-reasoner as the main current API aliases. That is now outdated. DeepSeek’s official April 24, 2026 release note states that DeepSeek-V4 Preview is officially live and open-sourced, with API access available today.
| Topic | Correct current wording | Why it matters |
|---|---|---|
| Latest general DeepSeek release | DeepSeek-V4 Preview is the latest official general/API release line. | Do not describe DeepSeek-V3.2 as the current flagship API model anymore. |
| Current API model names | deepseek-v4-flash and deepseek-v4-pro. | These are the model IDs developers should use in new production integrations. |
| Legacy names | deepseek-chat and deepseek-reasoner currently route to V4-Flash compatibility modes. | These names should only be mentioned for migration, not as the recommended current API names. |
| Context length | 1M context for the current V4 API models. | Older 128K wording is now outdated for the current API surface. |
| Maximum output | 384K max output for the current V4 API models. | Older 8K/64K output wording should not be used for V4 pages. |
| V3.2 status | DeepSeek-V3.2 is now a previous major V3-line release and still an important research milestone. | Keep V3.2 for history, agent/tool-use research, and migration context, not as the current API default. |
What Is DeepSeek Research?
DeepSeek research is not one single product. It is the research and engineering ecosystem behind DeepSeek’s papers, technical reports, official repositories, model cards, open-weight releases, API updates, and specialized model families. Depending on the user’s intent, “DeepSeek research” may refer to DeepSeek-V4’s one-million-token context intelligence, the agentic and tool-use work behind DeepSeek-V3.2, the reinforcement-learning reasoning line behind DeepSeek-R1, the Mixture-of-Experts architecture behind DeepSeek-V3, or specialized families such as DeepSeek-Coder, DeepSeekMath, DeepSeek-VL, Janus, DeepSeek-OCR, and DeepSeek-Prover.
The most useful way to understand DeepSeek research is to separate it into practical layers:
- Model architecture: how DeepSeek designs large language models, including MoE, MLA, sparse attention, compressed attention, and long-context systems.
- Reasoning research: how DeepSeek trains and improves models for math, coding, logic, multi-step planning, and agentic problem solving.
- API model evolution: how official API model names change over time, including the current move from legacy names to
deepseek-v4-flashanddeepseek-v4-pro. - Open-weight releases: official model checkpoints published through DeepSeek’s GitHub and Hugging Face accounts.
- Specialized model families: research lines for coding, mathematics, theorem proving, vision-language understanding, OCR, and multimodal generation.
- Deployment and infrastructure: systems that help run MoE models efficiently in production or locally, including serving, storage, kernels, and agent integrations.
If you are looking for the broader product overview instead of research details, start with the DeepSeek AI. If you want to compare current API model IDs and open model families, use the DeepSeek models hub. If you are building with the hosted API, use the updated DeepSeek API guide.
Current DeepSeek API and Research Snapshot
This snapshot is included because many readers confuse research model names, open-weight model cards, and API model IDs. This research page intentionally does not mirror live API prices because DeepSeek pricing can change. For current public rates, use the official DeepSeek Models & Pricing page before making billing or production decisions.
| Item | Current official value | Notes for researchers and developers |
|---|---|---|
| Current API model IDs | deepseek-v4-flash, deepseek-v4-pro | Use these names in new API calls. |
| Base URL | https://api.deepseek.com | The OpenAI-compatible base URL remains unchanged. |
| Anthropic-compatible base URL | https://api.deepseek.com/anthropic | DeepSeek documents both OpenAI-compatible and Anthropic-compatible access. |
| Thinking mode | Both V4 API models support thinking and non-thinking modes. | Use DeepSeek’s official thinking-mode guidance for current API syntax. |
| Context length | 1M | DeepSeek describes one-million-token context as the V4 standard. |
| Maximum output | 384K | Applies to the current V4 API model table. |
| Supported API features | JSON Output, Tool Calls, Chat Prefix Completion, and FIM Completion in non-thinking mode. | Feature behavior can vary by mode, so implementation details should be checked in the official API guides. |
| Official API pricing | DeepSeek Models & Pricing | Prices are intentionally not mirrored on this research page because official rates can change. Use DeepSeek’s official pricing page for the latest public token rates and billing notes. |
| Legacy names | deepseek-chat, deepseek-reasoner | Compatibility only; scheduled for retirement after July 24, 2026, 15:59 UTC. |
Official DeepSeek Research Resources
The table below maps the main official DeepSeek research resources to the user intent they serve. It also connects each research area to a relevant Chat-Deep.ai guide so readers can move from theory to practical use.
| Resource / Model family | Type | What it is best known for | Official source | Related Chat-Deep guide |
|---|---|---|---|---|
| DeepSeek-V4-Pro | Current V4 Pro general/API/open-weight model | 1.6T total parameters, 49B active parameters, 1M context, long-context intelligence, advanced reasoning, coding, and agentic workflows. | V4 release note / Hugging Face | DeepSeek-V4 |
| DeepSeek-V4-Flash | Current faster/economical V4 API/open-weight model | 284B total parameters, 13B active parameters, 1M context, lower-cost API usage, and strong everyday reasoning performance. | V4 release note / Hugging Face | DeepSeek-V4-Flash |
| DeepSeek-V3.2 | Previous major V3-line reasoning and agent model | DeepSeek Sparse Attention, scalable reinforcement learning, agentic task synthesis, thinking in tool-use, and open weights. | DeepSeek-V3.2 release note / Hugging Face | DeepSeek V3.2 |
| DeepSeek-V3.1 / V3.1-Terminus | Hybrid inference and agent transition releases | One model supporting thinking and non-thinking modes, stronger agent behavior, and improved tool-use performance. | V3.1 release note / V3.1-Terminus note | DeepSeek V3.1 |
| DeepSeek-V3 | Mixture-of-Experts language model | 671B total parameters, 37B activated parameters per token, MLA, DeepSeekMoE, and large-scale pretraining. | DeepSeek-V3 GitHub | DeepSeek V3 |
| DeepSeek-R1 / R1-0528 | Reasoning model family | Reinforcement-learning-driven reasoning, R1-Zero, R1, distilled Qwen/Llama checkpoints, improved R1-0528 behavior, JSON output, and function calling support. | DeepSeek-R1 GitHub / R1-0528 release note | DeepSeek R1 |
| DeepSeek-Coder / DeepSeek-Coder-V2 | Code model family | Code generation, repository-level code understanding, fill-in-the-middle, code/math capability, and software engineering workflows. | DeepSeek-Coder-V2 GitHub | DeepSeek Coder |
| DeepSeekMath | Math reasoning model family | Mathematical pretraining, quantitative reasoning, and competition-style math research. | DeepSeekMath GitHub | DeepSeek models |
| DeepSeek-Prover | Formal theorem proving | Lean-based theorem proving and formal mathematical reasoning research. | DeepSeek-Prover-V2 GitHub | DeepSeek models |
| DeepSeek-VL / DeepSeek-VL2 | Vision-language models | Multimodal understanding, OCR-like visual understanding, charts, documents, visual question answering, and grounding. | DeepSeek-VL2 GitHub | DeepSeek models |
| Janus / Janus-Pro | Unified multimodal understanding and generation | Decoupled visual encoding for image understanding and image generation in a unified architecture. | Janus GitHub | DeepSeek models |
| DeepSeek-OCR / DeepSeek-OCR-2 | Document and OCR model research | Document understanding, visual token compression, PDF/image OCR, markdown conversion, and vLLM/Transformers inference examples. | DeepSeek-OCR-2 GitHub | DeepSeek models |
| DeepEP, DeepGEMM, 3FS, and related projects | Infrastructure and systems research | MoE communication, GPU kernels, distributed storage, training, inference, and production efficiency. | DeepSeek GitHub organization | Run DeepSeek locally |
DeepSeek Research Timeline
This timeline focuses on selected official DeepSeek model and research milestones that are useful for understanding the evolution from early MoE research to reasoning models, agentic systems, document models, and V4’s one-million-token context direction.
| Date | Release / Research milestone | Why it matters | Status now |
|---|---|---|---|
| May 2024 | DeepSeek-V2 | Introduced a strong MoE language model with efficient inference, MLA, DeepSeekMoE, and 128K context support in the V2 family. | Historical architecture milestone |
| December 26, 2024 | DeepSeek-V3 | Expanded the V line with 671B total parameters, 37B activated parameters, 14.8T training tokens, and major architecture improvements. | Foundation for later V-line releases |
| January 20, 2025 | DeepSeek-R1 | Released the first major open reasoning family, including R1-Zero, R1, and six distilled checkpoints. | Still important for open reasoning research |
| March 25, 2025 | DeepSeek-V3-0324 | Improved reasoning, front-end development, tool-use capabilities, and MIT-licensed model release posture according to DeepSeek’s release note. | Historical V3 update |
| May 28, 2025 | DeepSeek-R1-0528 | Updated the R1 line with improved benchmark behavior, reduced hallucinations, JSON output, and function calling support. | Still relevant open reasoning checkpoint |
| August 21, 2025 | DeepSeek-V3.1 | Introduced hybrid inference with thinking and non-thinking modes, stronger agent skills, and 128K context for the API aliases at that time. | Key transition toward V4-style dual modes |
| September 22, 2025 | DeepSeek-V3.1-Terminus | Improved language consistency and agent behavior after V3.1 user feedback. | Historical transition release |
| September 29, 2025 | DeepSeek-V3.2-Exp | Introduced DeepSeek Sparse Attention for more efficient training and inference in long-context scenarios. | Precursor to V3.2 |
| December 1, 2025 | DeepSeek-V3.2 and DeepSeek-V3.2-Speciale | Launched V3.2 as the official successor to V3.2-Exp, with reasoning-first agent features, thinking in tool-use, open weights, and a technical report. | Previous major V3-line release |
| January 27, 2026 | DeepSeek-OCR-2 | Extended DeepSeek’s document-model research with Visual Causal Flow and practical vLLM / Transformers inference examples. | Current OCR/document research line |
| April 24, 2026 | DeepSeek-V4 Preview | Introduced V4-Pro and V4-Flash, one-million-token context, current V4 API model IDs, open weights, and new long-context efficiency work. | Current general/API release line |
Key DeepSeek Research Themes
1. V4 and One-Million-Token Context Intelligence
DeepSeek-V4 is now the main current research and product direction for DeepSeek’s general models. DeepSeek describes the V4 series as a preview line with two MoE models: DeepSeek-V4-Pro and DeepSeek-V4-Flash. Both support a one-million-token context length, and both are exposed through the current API model IDs deepseek-v4-pro and deepseek-v4-flash.
For product teams, the 1M context shift matters because long-context use cases are common in real applications: codebase review, document analysis, legal or policy research, support-ticket history, RAG pipelines, multi-file agent work, and long-running planning tasks. For researchers, V4 matters because it combines MoE scaling, compressed/sparse attention ideas, post-training for reasoning, and explicit agentic evaluation.
2. Mixture-of-Experts Efficiency
A central theme in DeepSeek research is Mixture-of-Experts efficiency. In a dense model, most or all parameters are used for each token. In an MoE model, only selected experts are activated for a token. This allows DeepSeek to maintain very large total capacity while reducing the computation used for each step of inference.
DeepSeek-V3 made this approach widely visible with 671B total parameters and 37B activated parameters per token. DeepSeek-V4 extends the same broad MoE direction at a larger scale for V4-Pro and a more efficient scale for V4-Flash. For developers, this matters because model architecture affects latency, memory, serving complexity, and API economics.
3. Reasoning and Reinforcement Learning
DeepSeek-R1 made reinforcement-learning-driven reasoning a core part of the DeepSeek research story. The official R1 repository explains the distinction between DeepSeek-R1-Zero, which explores large-scale RL directly on the base model, and DeepSeek-R1, which adds cold-start data and a more refined training pipeline. Later releases, including R1-0528, V3.1, V3.2, and V4, connect that reasoning lineage to more practical workflows such as coding, tool use, and agentic problem solving.
For normal chat users, reasoning research matters when a task involves multi-step logic, math, planning, or code debugging. For developers, it matters when choosing between non-thinking and thinking modes or deciding when to spend more output tokens on hard tasks.
4. Long-Context and Attention Efficiency
Long-context efficiency is another major DeepSeek research theme. DeepSeek-V2 used MLA to reduce the KV-cache bottleneck. DeepSeek-V3.2-Exp introduced DeepSeek Sparse Attention for efficient long-context training and inference. DeepSeek-V4 then moved the current API and open model story to 1M context, with DeepSeek describing V4 as focused on highly efficient million-token context intelligence.
This matters for both hosted API use and local deployment. More context can unlock new workflows, but it also increases the need for careful prompt construction, caching, memory planning, output budgeting, and validation.
5. Tool Use and Agentic Behavior
DeepSeek-V3.1 and V3.2 placed more emphasis on tool use and agent-style workflows. DeepSeek-V3.2 was especially important because the official release describes it as integrating thinking directly into tool-use scenarios. DeepSeek-V4 continues this agent direction and is positioned around stronger coding, reasoning, long-context, and agentic capabilities.
For a developer, this is one of the most practical research-to-product bridges. A research improvement in tool-use training can become a real product improvement when a model calls functions more reliably, follows schemas more consistently, searches or codes more effectively, or performs multi-step workflows with fewer failures.
6. Coding and Software Engineering Models
DeepSeek-Coder and DeepSeek-Coder-V2 focus on code intelligence. The original DeepSeek-Coder family was trained on a code-heavy corpus and designed for code completion and infilling. DeepSeek-Coder-V2 extended the coding line using a MoE architecture, broader programming language support, 128K context, and stronger code/math capabilities. More recent general models, including V3.2 and V4, also emphasize agentic coding and software-engineering workflows.
This research area matters for developers building coding assistants, repository search, code repair tools, test generation systems, or internal engineering agents. For Chat-Deep.ai readers, it also helps explain why a specialized coding release is not the same thing as a current hosted API model ID.
7. Math, Theorem Proving, and Formal Reasoning
DeepSeekMath is the research line focused on mathematical reasoning. DeepSeek-Prover extends the math direction into formal theorem proving and Lean-based workflows. These projects help explain why math, proof, and reasoning appear repeatedly across DeepSeek-R1, R1-0528, V3.2-Speciale, and V4 reasoning evaluations.
For users, math research matters when evaluating DeepSeek for quantitative reasoning, proofs, tutoring, competitive math, symbolic manipulation, or technical problem solving. For developers, it matters when deciding whether to use a thinking mode, a local distilled model, or a specialized theorem-proving release.
8. Vision-Language, OCR, and Multimodal Research
DeepSeek-VL and DeepSeek-VL2 move beyond text-only language modeling into image-text understanding. Janus and Janus-Pro extend the multimodal direction by combining visual understanding and image generation research. DeepSeek-OCR and DeepSeek-OCR-2 focus on document understanding, OCR, page images, PDFs, and structured extraction.
This matters when users want to analyze screenshots, diagrams, charts, documents, scans, or images. It also clarifies a common misunderstanding: not every DeepSeek model family is available through the same public API model IDs. Some releases are research or open-weight resources rather than standard hosted chat endpoints.
9. Open Weights, Licensing, and Local Deployment
DeepSeek research is closely connected to open model releases. Many DeepSeek model families are published through official GitHub repositories and Hugging Face model cards. However, “open release” does not mean the same thing for every model. Some repositories use MIT for code while model weights may have separate model licenses. Newer releases such as R1, V3.2, and V4 are documented with MIT licensing at the repository/model-card level, but users should always verify the exact license for the exact model they plan to use.
If your goal is to run DeepSeek locally, start with model size, quantization, hardware, context length, and runtime. A small distilled R1 checkpoint can be practical on a personal machine, while full-scale frontier models such as V3.2 or V4 may require server-class hardware, multi-GPU infrastructure, or specialized serving systems. For practical setup, use the DeepSeek local installation guide.
DeepSeek Papers and Technical Reports Explained
The official DeepSeek papers and technical reports can be difficult to compare because each one answers a different question. The summaries below explain the practical meaning of the main research resources.
| Paper / report | Main idea | Why it matters | Best for | Official source | Chat-Deep internal guide |
|---|---|---|---|---|---|
| DeepSeek-V4 Technical Report | Explains V4’s MoE models, 1M context direction, compressed/sparse attention work, post-training, reasoning modes, and agentic evaluations. | It is the current core research report for DeepSeek’s general/API model line. | API developers, model evaluators, long-context teams, agent builders. | DeepSeek-V4 PDF | DeepSeek-V4 |
| DeepSeek-V3.2 Technical Report | Explains DeepSeek Sparse Attention, scalable RL, and agentic task synthesis. | It connects DeepSeek’s long-context efficiency work with reasoning and tool-use behavior before V4. | Agent developers, API users, long-context product teams. | DeepSeek-V3.2 Hugging Face | DeepSeek V3.2 |
| DeepSeek-V3 Technical Report | Explains the V3 MoE architecture, MLA, DeepSeekMoE, training scale, and efficiency design. | It is the foundation for understanding the V line that later connects to V3.1, V3.2, and V4. | Researchers, model evaluators, infrastructure teams. | arXiv: DeepSeek-V3 | DeepSeek V3 |
| DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning | Explains R1-Zero, R1, RL-driven reasoning, cold-start data, and distillation. | It is the core research resource for understanding DeepSeek’s first major reasoning family. | Reasoning research, math/coding tasks, local R1 deployment. | arXiv: DeepSeek-R1 | DeepSeek R1 |
| Native Sparse Attention | Research on sparse attention for hardware-aligned and natively trainable long-context modeling. | It helps explain why sparse attention matters for long-context training and inference efficiency. | ML researchers, long-context engineers, serving teams. | arXiv: Native Sparse Attention | DeepSeek models |
| DeepSeek-Coder and DeepSeek-Coder-V2 papers | Research on code-specialized pretraining, repository-level completion, FIM, and code/math capability. | It explains DeepSeek’s dedicated coding path apart from general chat models. | Software engineers, coding assistant builders, code agent teams. | DeepSeek-Coder-V2 GitHub | DeepSeek Coder |
| DeepSeekMath | Math-focused pretraining and reasoning research built from a code-model foundation. | It shows how math capability became a major part of the DeepSeek research direction. | Math reasoning, tutoring, evaluation, research benchmarks. | DeepSeekMath GitHub | DeepSeek models |
| DeepSeek-VL and DeepSeek-VL2 | Vision-language models for real-world visual understanding tasks. | They broaden DeepSeek research beyond text into OCR, charts, documents, and image-text reasoning. | Document AI, visual QA, OCR, chart understanding. | DeepSeek-VL2 GitHub | DeepSeek models |
| Janus-Pro | A unified multimodal understanding and generation framework with decoupled visual encoding. | It explains DeepSeek’s approach to multimodal understanding and image generation research. | Multimodal researchers, image understanding, generation experiments. | Janus GitHub | DeepSeek models |
| DeepSeek-OCR and DeepSeek-OCR-2 | Document/OCR research for converting images and documents into structured text or markdown. | It is useful for document intelligence, PDF workflows, OCR pipelines, and visual token research. | Document AI teams, OCR users, local model experimenters. | DeepSeek-OCR-2 GitHub | DeepSeek models |
DeepSeek Research vs DeepSeek Chat vs DeepSeek API
Many users search for “DeepSeek research” when they actually need a paper, a chatbot, an API model name, or a local model checkpoint. The table below separates those intents.
| Term | What it means | Best for | Where to go |
|---|---|---|---|
| DeepSeek Research | Official papers, technical reports, model releases, architecture notes, repositories, and model cards. | Understanding how DeepSeek models are designed, trained, released, and evaluated. | This research guide and official sources linked above. |
| DeepSeek Chat | The official web chatbot experience from DeepSeek. | Everyday chat, writing, reasoning, coding help, and general use. | DeepSeek AI guide |
| DeepSeek API | Developer access through official API endpoints and current model IDs such as deepseek-v4-flash and deepseek-v4-pro. | Building apps, automations, agents, chatbots, coding tools, and business workflows. | DeepSeek API guide |
| DeepSeek open models | Model weights and code published through official repositories or model hubs. | Research, self-hosting, evaluation, fine-tuning where permitted, and local experiments. | DeepSeek models |
| DeepSeek local deployment | Running a compatible DeepSeek model or distilled checkpoint on your own hardware. | Privacy, offline experimentation, local coding assistants, and self-hosted workflows. | Run DeepSeek locally |
A key current distinction: deepseek-v4-flash and deepseek-v4-pro are the current official API model names. deepseek-chat and deepseek-reasoner should be described only as legacy compatibility names during the V4 migration window, not as the recommended model IDs for new integrations.
Model-to-Use-Case Mapping
Use this table when you want the practical answer: which DeepSeek research line matters for which task?
| Use case | Most relevant DeepSeek research line | Recommended starting point | Why |
|---|---|---|---|
| General API applications | DeepSeek-V4-Flash | DeepSeek API | It is the lower-cost current V4 API model and the best default for many chat, extraction, summarization, and app workflows. |
| Hard reasoning, coding, long-context, and agents | DeepSeek-V4-Pro | DeepSeek models | It is the stronger current V4 model for complex tasks, deeper reasoning, and more demanding agentic workflows. |
| Historical agent/tool-use research | DeepSeek-V3.2 | DeepSeek V3.2 | V3.2 is no longer the current API default, but it remains a key research milestone for thinking in tool-use. |
| Open reasoning research and local reasoning | DeepSeek-R1 and R1 distilled checkpoints | DeepSeek R1 | R1 explains the reasoning lineage, and distilled checkpoints are more practical for smaller local deployments. |
| Code generation and coding agents | DeepSeek-Coder / Coder-V2 / V4 coding and agent capabilities | DeepSeek Coder | Coder models explain the specialized code path; V4 is the current general hosted line for coding and agent workflows. |
| Document OCR and PDF extraction | DeepSeek-OCR / OCR-2 / VL2 | DeepSeek models | OCR and VL research families are better suited to visual documents than text-only chat models. |
| Long-context applications | V2, V3.2-Exp, V3.2, and V4 | DeepSeek models | MLA, sparse attention, compressed attention, caching, and V4’s 1M context are central to long-context performance. |
| Cost planning for production API usage | Official API pricing and context caching | Official DeepSeek Models & Pricing | Research improvements matter, but production cost depends on token usage, cache behavior, output length, model choice, and request volume. Use the official pricing page for current rates. |
How DeepSeek Research Connects to Chat-Deep.ai Guides
This research page is designed as a hub. Use the links below to move from research concepts to practical actions:
- DeepSeek AI — start here for the main overview of DeepSeek chat, app, API, models, pricing, and official links.
- DeepSeek models — compare current API model IDs, open releases, legacy names, and specialized model lines.
- DeepSeek API — learn the current API base URL, V4 model IDs, chat completions, JSON output, tool calls, and thinking mode.
- Official DeepSeek Models & Pricing — verify the latest public API rates directly from DeepSeek.
- DeepSeek cost calculator — estimate API spend from your own token volumes after checking the official DeepSeek pricing page.
- DeepSeek R1 — understand R1, R1-Zero, R1-0528, and official distilled checkpoints.
- Run DeepSeek locally — install and run compatible DeepSeek models with local tools and realistic hardware expectations.
- DeepSeek V4 release tracker — follow the latest Chat-Deep.ai summary of V4 changes and migration notes.
Who Should Use This Page?
This DeepSeek research guide is useful for several types of readers:
- Researchers who want a clean map of official DeepSeek papers, reports, repositories, and model families.
- Developers who need to connect research names such as V4, V3.2, or R1 to real API model IDs.
- AI enthusiasts who want to understand why DeepSeek model releases receive attention.
- Product teams deciding whether DeepSeek is useful for chat, coding, agents, long-context workflows, or document processing.
- Businesses evaluating whether to use the DeepSeek API, run local open models, or compare costs before deployment.
- Local deployment users trying to understand which official open releases can realistically run on their hardware.
Editorial Accuracy Rules for This Page
DeepSeek moves quickly, so this page follows four rules to avoid the most common mistakes:
- Do not call V3.2 the current API model line anymore. It is now a previous major V3-line release.
- Do not recommend
deepseek-chatordeepseek-reasonerfor new production code. They are legacy compatibility names during the V4 migration period. - Do not assume every open DeepSeek model has the same license. Check the exact model card or repository before commercial use.
- Do not treat web/app/API/local models as one identical product. Official DeepSeek services, hosted API models, and open-weight checkpoints can differ.
DeepSeek Research FAQ
What is DeepSeek Research?
DeepSeek Research refers to the official technical work behind DeepSeek models, including papers, model reports, open-weight releases, architecture research, reasoning systems, code models, math models, vision-language models, OCR releases, infrastructure projects, and API model evolution.
What is the latest official DeepSeek general model release?
As of April 25, 2026, DeepSeek-V4 Preview is the latest official DeepSeek general/API release line. It includes DeepSeek-V4-Flash and DeepSeek-V4-Pro, supports 1M context, and is available through the official API using deepseek-v4-flash and deepseek-v4-pro.
Is DeepSeek-V3.2 still the current API model?
No. DeepSeek-V3.2 is now a previous major V3-line release. It remains important for understanding DeepSeek Sparse Attention, scalable RL, thinking in tool-use, and agentic training, but the current official API model IDs are deepseek-v4-flash and deepseek-v4-pro.
What happened to deepseek-chat and deepseek-reasoner?
They are now legacy compatibility names. DeepSeek says deepseek-chat currently routes to the non-thinking mode of deepseek-v4-flash, and deepseek-reasoner routes to the thinking mode of deepseek-v4-flash. They are scheduled to be retired after July 24, 2026, 15:59 UTC.
Where can I find official DeepSeek papers?
Official DeepSeek papers and reports are usually linked from DeepSeek’s GitHub repositories, Hugging Face model cards, API release notes, and arXiv pages. This guide links to the most relevant official sources for each major research line.
Is DeepSeek Research open source?
Many DeepSeek releases include open code, open model weights, or public technical reports, but licensing varies by release. Check the official repository or model card for each model before using it commercially, modifying it, or deploying it in production.
What is the difference between DeepSeek R1 and DeepSeek V4 research?
DeepSeek-R1 is mainly known as a reasoning-focused model family built around reinforcement learning, R1-Zero, R1, and distilled checkpoints. DeepSeek-V4 is the current general/API model line focused on one-million-token context, stronger reasoning, coding, and agentic workflows through V4-Flash and V4-Pro.
Does DeepSeek Research power DeepSeek Chat?
DeepSeek research releases influence the model families behind DeepSeek products, but a research model name is not always the same as a live chat or API endpoint. Always check official DeepSeek product and API documentation for the current model mapping.
Can I run DeepSeek research models locally?
Some official DeepSeek open releases and distilled checkpoints can be run locally, depending on the model size, quantization, context length, runtime, and your hardware. Smaller distilled models are more realistic for personal computers, while full V3.2 or V4-scale models require much more serious infrastructure.
How is DeepSeek research different from the DeepSeek API?
DeepSeek research explains the models, architecture, training methods, papers, and open releases. The DeepSeek API is the developer product used to access supported hosted models programmatically, currently through model IDs such as deepseek-v4-flash and deepseek-v4-pro.
Is Chat-Deep.ai the official DeepSeek research website?
No. Chat-Deep.ai is an independent DeepSeek guide. It is not affiliated with DeepSeek.com or the official DeepSeek API platform. This page links to official DeepSeek sources wherever official verification is needed.
Final Takeaway
DeepSeek research is best understood as a connected ecosystem: V-series architecture work, V4 one-million-token context intelligence, R-series reasoning, Coder and Math specialization, VL and Janus multimodal research, OCR document models, and infrastructure projects that support efficient training and inference. The current general/API story is now V4-first: use deepseek-v4-flash or deepseek-v4-pro for new API integrations, keep V3.2 as an important previous research milestone, and treat deepseek-chat and deepseek-reasoner as legacy migration names only.
To continue, compare DeepSeek models, read the DeepSeek API guide, estimate usage with the DeepSeek cost calculator, or follow the DeepSeek local installation guide.