DeepSeek
Free
DeepSeek is an open source large model and
DeepSeek — Deep search open source large model and API platform
Core parameters and statistics
DeepSeek’s latest V4 series offers two product lines under the same architecture: the performance-focused Pro version and the cost-efficiency-focused Flash version. The two share the 1M token context window and the 384K token maximum output, but the gradient is spread out in parameter scale, activation density and training data volume, forming a complementary coverage strategy.
| Project | Specifications |
|---|---|
| Model/API name | DeepSeek V4 series |
| Developer | Hangzhou Deep Search Artificial Intelligence Basic Technology Research Co., Ltd. |
| Product Type | AI Model/API |
| Delivery form | API / Web conversation (chat.deepseek.com) / iOS & Android App / Open source model weight |
| Context length | 1M tokens |
| Maximum output | 384K tokens |
| Parameter scale | Pro: 1.6T total parameters (activated ~49B) / Flash: 284B total parameters (activated ~13B) |
| Architecture | Hybrid Expert Model (MoE) + Hybrid Attention (Hybrid Attention) |
| Support modal | Text, code, picture (basic VLM), file (PDF/Word/Excel/PPT/TXT) |
| Supported languages | Chinese, English |
| Security Framework | Open Source + Content Filtering |
| Pricing model | C-side is completely free; API is very low price based on volume |
| Open Source License | MIT |
| TTFT (reference) | V4-Flash ~200-400ms / V4-Pro ~400-800ms (third-party sampling) |
Actual meaning of efficiency improvement: When processing 1M context, V4-Pro's computing power requirements (FLOPs) are only 27% of the previous generation V3.2, and the KV cache occupation is only 10%; the two indicators of V4-Flash are as low as 10% and 7%. This means that under the same hardware conditions, the V4 series can host longer conversations and larger document analysis without hitting the video memory ceiling. For high-frequency API callers, this translates directly into lower cost per unit and fewer timeout retries.
Three levels of reasoning intensity design: Provides three levels of reasoning modes: Non-think (straight out mode), Think High (regular in-depth thinking) and Think Max (maximum in-depth thinking). The straight-out mode is suitable for delay-sensitive tasks such as information retrieval and translation; the deep-thinking mode releases the upper limit of capabilities in scenarios such as mathematical proofs, competition programming, and logical reasoning that require gradual chain reasoning. The output token amount of Think Max is about 3-8 times that of Non-think. The cost impact needs to be evaluated in advance when calling API.
User and market recognition
DeepSeek's market volume presents a dual-track pattern of "C-side topics fluctuating and B-side topics continuing to penetrate".
C-side market: During the Spring Festival of 2025, DeepSeek's daily active users once exceeded 30 million, becoming the fastest AI application in history to reach this milestone, and its downloads surpassed Doubao to top the free list. But then the APP activity dropped, and some users reported that the model had problems with "hallucinatory" output and excessive "AI flavor". The core user profile is biased towards working people aged 25-44 in high-tier cities.
B-side market: According to public information, more than 30,000 companies have connected to the DeepSeek model, covering 12 industry fields such as finance, medical care, industry, and government affairs. State-owned enterprises such as China UnionPay and National Pipeline Network Group have introduced models into core business processes such as marketing, intelligent regulation, and pipeline safety audits. The core driving force for B-side adoption is the cost advantage of API prices that are 10-100 times lower than competing products and the compliance flexibility of open source self-hosting.
Industry Benchmark Performance: Third-party evaluations show that V4-Pro has reached the best level of open source models in the Agent capability dimension. The experience is better than Claude Sonnet 4.5, and the delivery quality is close to Claude Opus 4.6 non-thinking mode. The reasoning performance exceeds all publicly evaluated open source models, and it is comparable to the world's top closed source models in tasks such as mathematics, STEM, and competition code. But it is still slightly inferior to Gemini-3.1-Pro in terms of world knowledge coverage.
Cost advantage
DeepSeek's cost advantage is not a periodic promotion, but a systematic cost reduction achieved through architectural innovation and technical engineering. It is pushing the call price of large models from "tens of dollars per million tokens" to the order of "a few yuan per million tokens".
C side: completely free and unlimited. The web version and official App are free and open to all users, with no limit on the number of times of use or conversation rounds.
API pricing in-depth comparison:
| Service Type | Components | DeepSeek-V4-Flash | DeepSeek-V4-Pro (25% off) | GPT-5.5 Pro (reference price) |
|---|---|---|---|---|
| Input | Cache hit | 0.02 yuan/million tokens | 0.025 yuan/million tokens | ~3.4 yuan/million tokens |
| Cache miss | 1 yuan/million tokens | 3 yuan/million tokens | ~210 yuan/million tokens ($30) | |
| Output | — | 2 yuan/million tokens | 6 yuan/million tokens | ~1,260 yuan/million tokens ($180) |
Chain impact of cost advantage: The early V2 training cost was only US$4.6 million, which is an advantage of two orders of magnitude compared to GPT-4 (approximately 78 million) and GPT-4o (approximately 120 million). Low training costs allow DeepSeek to push major version updates every 2-3 months.
Enterprise/privatized deployment cost considerations: License level open source (MIT), infrastructure costs need to be borne by yourself. V4-Flash can run inference on a single card A100-80G, but high-concurrency scenarios still require a multi-card cluster. The total cost of the enterprise should be comprehensively compared with "API annual fee vs three-year TCO of privatized deployment", and the update cost caused by model version iteration should be considered.
Detailed scenario of cost deduction: Taking a SaaS application that processes 2 million input tokens + 800,000 output tokens per day as an example (such as AI customer service, content generation platform), the monthly cost comparison using different models is as follows:
| Scene | DeepSeek V4-Flash | DeepSeek V4-Pro | GPT-5.5 Pro | Claude Sonnet 4.5 |
|---|---|---|---|---|
| Monthly input token amount | 60 million | 60 million | 60 million | 60 million |
| Monthly output token amount | 24 million | 24 million | 24 million | 24 million |
| Monthly API fee (estimated) | ~84 yuan | ~252 yuan | ~$7,200 (about 50,000 yuan) | ~$1,200 (about 8,400 yuan) |
| Annual cost | ~1,008 yuan | ~3,024 yuan | ~600,000 yuan | ~100,000 yuan |
| Relative DeepSeek multiple | 1x | 3x | ~600x | ~100x |
The above are estimates based on public pricing. Actual costs vary based on cache hit rates, volume discounts, and regional differences. This comparison does not take into account differences in output quality - if the application scenario requires GPT-5.5 Pro-level creative quality, using cost comparison alone to make a selection decision may be misleading.
Risk Disclosure: The sustainability of the C-side free model depends on the ability to cross-subsidize advertising, enterprise services and API revenue. If the subsidy strategy is adjusted, it may affect the availability and response quality of C-side services. API frequency control and SLA are not transparent enough, which may pose risks to key business scenarios. Frequency control restrictions and tiered pricing in high-frequency scenarios need to be communicated with the business in advance. In addition, as a Chinese company, DeepSeek's API services may be limited by cross-border network delays and compliance requirements, and overseas developers need to evaluate regional acceleration node availability.
Main functions
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Extra long context (1M tokens): The most differentiated capability of the V4 series. It can process texts as large as the "Three-Body" trilogy at one time, and is suitable for ultra-long document analysis, large-scale code base audits, and overall understanding of complex conversation history. Limited Disclosure: The longer the context, the greater the inference latency and KV cache overhead. It is recommended to use a context window within 128K for daily tasks, and enable 1M mode for scenes that really require cross-chapter correlation. In extreme scenarios with 1M context, the first word delay may reach 3-5 seconds.
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Online Search: Break through the knowledge cutoff limit of static training data and obtain the latest information in real time. Search results are injected as context into the model inference process. The citation accuracy of the V4 series is improved compared to V3, but it is still recommended to conduct a secondary verification of key facts.
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File upload and multi-modal processing: Supports PDF, Word, Excel, PPT, TXT and pictures. Tabular PDFs and scanned documents mainly rely on text understanding after OCR, and there is still room for improvement in the ability to restore complex table structures. Image understanding is suitable for chart interpretation and object recognition, but is not suitable for fine layout restoration.
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Intelligent Agent Capabilities: SOTA level of Agent capabilities in the open source model, supporting Function Calling, multi-step task planning and workflow orchestration. In Agent scenarios, it is recommended to use Think High mode to balance planning quality and response speed.
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Thinking Mode: Three levels of adjustable reasoning depth. Non-think is suitable for deterministic tasks; Think High is suitable for daily reasoning; Think Max is suitable for scenarios such as mathematical proofs and competitive programming that require exhaustive reasoning paths.
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Context Caching: The V4 series supports server-side caching, which greatly reduces latency and cost when repeating the same prefix content. It is suitable for production scenarios where the system prompt is fixed. The hit rate decreases when dynamic content changes frequently.
Model and version evolution
V2 series: MoE architecture foundation laying (2024-05 to 2024-12)
- DeepSeek-V2 (2024-05): Officially introduce the MoE architecture
- DeepSeek-V2.5 (2024-09): Integrating Chat and Coder capabilities
- DeepSeek-V2.5-1210 (2024-12): Improved stability and task versatility
V3 and R1 series: Inference enhancement and scale jump (2024-12 to 2025-12)
- DeepSeek-V3 (2024-12-26): 671B MoE base model, multi-token prediction (MTP)
- DeepSeek-R1 (2025-01-20): Inference specialized flagship, RL driver, triggering discussions on pure RL inference routes
- DeepSeek-V3-0324 (2025-03-24): General optimization of reasoning, code and Chinese writing
- DeepSeek-R1-0528 (2025-05-28): AIME 2025 significantly improved, more efficient RL pipeline
- DeepSeek-V3.1 (2025-08-21): Hybrid inference architecture, 128K context
- DeepSeek-V3.1-Terminus (2025-09-22): Language consistency + Agent capability optimization
- DeepSeek-V3.2-Exp (2025-09-29): Sparse attention experimental version
- DeepSeek-V3.2 (2025-12-01): Knowledge base updated to May 2025
V4 series: Architecture reconstruction and ultra-long context (2026-04 to present)
- DeepSeek-V4-Pro Preview (2026-04-24): 1.6T total parameters (activation 49B), hybrid attention architecture
- DeepSeek-V4-Flash Preview (2026-04-24): 284B total parameters (activated 13B), single card A100 can run
API endpoint mapping
| time | deepseek-chat | deepseek-reasoner |
|---|---|---|
| Starting from 2026-04-24 | Point to V4-Flash non-thinking | Point to V4-Flash thinking |
DeepSeek was established in 2023-07 and released its first large model, DeepSeek LLM, in 2024-01.
Technical advantages
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MoE Innovative Routing: V4-Pro has a total parameter of 1.6T but only activates about 49B per inference (~3% activation rate). During reasoning, only a "small number of expert combinations" are used to answer questions, and massive parameters store a wider knowledge distribution. Restricted Disclosure: For long-tail problems that require the intersection of multi-domain knowledge, the routing strategy may miss relevant experts, which is a potential technical reason why the "world knowledge" dimension is slightly inferior to the closed-source model.
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Hybrid Attention Architecture: A core breakthrough in the V4 series. Through the combination of CSA (Compressed Sparse Attention) and HCA (Heavy Compression Attention), the calculation amount and memory usage are reduced to 10%-27% of traditional full attention in a 1M ultra-long context. Very long documents that previously required an 8-card A100 can now be processed on a single card.
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Training efficiency innovation: Multi-token prediction (MTP) training target increases training signal density; FP8 mixed-precision training reduces memory usage and calculation volume by nearly half; GRPO (Group Relative Policy Optimization) simplifies the RL training pipeline and does not rely on the Critic model.
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In-depth adaptation of domestic computing power: Deep collaboration with Huawei’s Ascend NPU has been achieved, and full-process training and inference can be completed on the Ascend platform. It is of great significance to industries such as government affairs, finance, and energy that have localized substitution needs. Restricted Disclosure: The inference throughput and stability on Ascend 910B are still weaker than those of NVIDIA GPUs of the same generation. Enterprises need to conduct stress testing based on actual load when selecting models.
Adaptation boundaries and restrictions
- Recommended usage scenarios: Code generation and review (competition programming, complex algorithms), mathematics and STEM reasoning, Agent application (Function Calling), ultra-long document analysis (1M context), general Q&A with priority on cost-effectiveness.
- Not Recommended Scenarios: Brand copywriting that requires extremely high originality in the output style (the "naturalness" of creative writing is weaker than that of the Claude series); news summary scenarios that require extremely strict freshness of real-time knowledge (requires cooperation with online search); long-tail knowledge Q&A that requires 100% factual accuracy (the world knowledge coverage accuracy is lower than Gemini-3.1-Pro).
- Known limitations: C-side activity has dropped after the outbreak, and product stickiness needs to be verified; API frequency control and SLA transparency are not enough; long-context scene reasoning delays still need to be optimized; C-side free model has no SLA guarantee, and response delays may occur during peak hours.
How to use
| How to use | Suitable for people | Features | Cost |
|---|---|---|---|
| Web version | All users | chat.deepseek.com, no need to register to chat directly | Completely free |
| Mobile App | Mobile users | iOS/Android, supports voice input | Totally free |
| API calls | Developers and enterprises | platform.deepseek.com Create API Key | Pay-as-you-go |
| Private deployment | High data compliance requirements | Open source model MIT license, self-hosting | Infrastructure + operation and maintenance |
API Quick Start (OpenAI compatible interface):
from openai import OpenAI
client = OpenAI(api_key="<YOUR_API_KEY>", base_url="https://api.deepseek.com")
response = client.chat.completions.create(
model="deepseek-chat",
messages=[
{"role": "system", "content": "You are a professional code assistant."},
{"role": "user", "content": "Quick sort using Python."}
],
temperature=0.7, max_tokens=4096, stream=True
)
for chunk in response:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="")Key parameters: temperature 0.0-1.0 (code 0.1-0.3, creative 0.7-0.9); max_tokens up to 384K; stream=True reduces first word delay; response_format={"type":"json_object"} forces JSON.
Product Pricing
| Tier | Price | Quota/Content |
|---|---|---|
| C-side free | ¥0 | Unlimited use of web version and App |
| API free quota | ¥0 | 100,000 calls per month (approximately 10 million input tokens) |
| API by volume (V4-Flash) | Cache hit 0.02 yuan/output 2 yuan | per million tokens |
| API by volume (25% off for V4-Pro) | Cache hit 0.025 yuan/output 6 yuan | per million tokens |
| Enterprise Privatization | Hardware + Operations | MIT Open Source, No License Fee |
The monthly fee for using V4-Flash for lightweight applications (average daily input of 1 million tokens/500,000 output tokens) is approximately RMB 60. The monthly rental price of 8×A100-80G server is about 50,000-80,000 yuan.
Application scenarios
- Finance and Risk Control: State-owned enterprises such as China UnionPay have invested in marketing copywriting and intelligent customer service. The R1 series’ advantages in logical reasoning are suitable for compliance reviews – comparison of contract terms and identification of abnormal transaction patterns. Implementation Tips: Financial scenarios have extremely high requirements on output accuracy. The model output is used as an auxiliary reference, and manual review points are required for key judgments.
- Geological Exploration and Energy: National Pipeline Network Group is used in geological report information extraction and remote sensing image interpretation. The information extraction accuracy reaches 83%, and manual sorting time is reduced by more than 60%. Implementation Tips: Geological terms are dense, so it is recommended to inject a domain vocabulary into the prompt.
- Government Affairs and Smart Office: Assistance in writing official documents, automatic generation of proposed opinions, and comparative analysis of policy documents. Domestic computing power adaptation constitutes core competitiveness in government affairs scenarios - full-link deployment can be completed on the Shengteng platform to meet the compliance requirements of Xinchuang.
- Software development and personal productivity: code generation, unit test completion, log exception analysis. 1M context window is suitable for auditing large code bases. Implementation Tips: Think High mode is recommended for code generation scenarios.
Applicable people
- Individual users and technology enthusiasts: Free web version or App. Unsuitable Boundary: Users who have strong personalized requirements for answer styles, or users who have extremely high privacy requirements, need to consider paid APIs or self-deployment.
- Students & Researchers: 1M Context suitable for long literature reviews. Unfit boundary: The citation format of academic papers needs to be manually verified item by item, and the model output citations may be inaccurate.
- Developers and Startup Teams: APIs and open source weights can be integrated at low cost. V4-Flash can be run on a single card to lower the hardware threshold. Recommendation: Verify the product concept with the API first, and then decide whether to switch to self-hosting based on the cost model. Pay attention to frequency control limitations and model version switching compatibility.
- Enterprises and medium and large organizations: Private deployment meets data sovereignty and compliance needs. Purchasing Prerequisites: There should be a clear AI implementation direction and quantifiable efficiency indicators. Before purchasing, confirm the commercial licensing terms, GPU support capabilities, and model version backward compatibility.
Comparison of competing products
| Comparative dimensions | DeepSeek V4 Pro | GPT-5.5 Pro | Claude Sonnet 4.5 | Gemini 3.1 Pro |
|---|---|---|---|---|
| Developer | Deep Search | OpenAI | Anthropic | |
| Context length | 1M | 256K | 200K | 2M |
| Parameter scale (activation) | 1.6T (49B) | Undisclosed | Undisclosed | Undisclosed |
| Multimodal | ✅ Basic VLM | ✅ | ❌ | ✅ |
| Agent capabilities (open source model) | SOTA | N/A (closed source) | N/A (closed source) | N/A (closed source) |
| Depth of Reasoning (Math/Code) | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ |
| Creative Writing Quality | ⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ |
| World Knowledge Coverage | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ |
| API Pricing | ⭐⭐⭐⭐⭐ (Very Low) | ⭐⭐ (High) | ⭐⭐⭐ (Medium) | ⭐⭐⭐⭐ (Low) |
| Open Source | ✅ MIT | ❌ | ❌ | ❌ |
| Domestic computing power adaptation | ✅ Shengteng | ❌ | ❌ | ❌ |
Selection Suggestions: For scenarios where the budget is extremely sensitive or self-hosted/domestic computing power is required, DeepSeek is the best choice; for extreme creative writing quality, give priority to evaluating Claude; for the largest context window (2M), Gemini is the only choice; for the most mature ecology and tool chain, the GPT series is still a safe brand.
Summary and Outlook
DeepSeek has established a unique competitive position in the field of large AI models in China through a combination strategy of "architectural innovation driving cost reduction, open source strategy to expand ecological coverage, and dual versions to meet hierarchical needs" - it is not the model with the most parameters, but it is likely to be the model with the highest output per unit of computing power.
Core Advantages: The V4 series has reached the best level of open source models in terms of reasoning, coding and agent capabilities; the API price is 10-100 times lower than international competing products; the all-free strategy on the C-side reduces the cost of market education; 30,000+ enterprises on the B-side have accessed to verify production availability; domestic computing power adaptation provides irreplaceable compliance value for government and enterprise market penetration.
Current limitations: The world knowledge coverage accuracy is still lower than the top closed-source model; C-side activity has dropped after the outbreak, and product stickiness needs to be verified; API frequency control and SLA transparency are not enough; long-context scene reasoning delay still needs to be optimized.
Follow-up observation points: After the official version of V4 is released, can it get closer to closed-source competing products in terms of knowledge accuracy and creative capabilities; whether the API service will introduce more detailed frequency control classification and regional acceleration nodes; whether the open source community ecosystem (fine-tuning framework, deployment tools, and Agent framework integration) can form a positive flywheel.
Procurement/Adoption Risk Assessment: Individuals and developers can try and make mistakes at zero cost without substantial risk. Enterprises recommend verifying capabilities in non-critical processes first and then gradually expand. Before privatization deployment in compliance-sensitive industries, the performance benchmarking test of the Shengteng platform should be completed, and the model version update clause should be written into the procurement contract to avoid the risk of business interruption caused by API model switching.
Related tools: DeepSeek, 
ChatGPT
Version Info
- DeepSeek-V4-Pro Preview ：V4 preview flagship model, with 1.6T total parameters (approximately 49B activated), represents the upper limit of the capabilities of the new generation basic model.
- DeepSeek-V4-Flash Preview ：V4 preview version is a cost-effective model with 284B total parameters (approximately 13B activated), emphasizing cost-effectiveness and throughput.
- DeepSeek-V3.2 ：General capabilities are further enhanced, performance is benchmarked against cutting-edge models, and the knowledge base is updated to May 2025.
- DeepSeek-V3.2-Exp ：Experimental iteration, focusing on verifying the new sparse attention mechanism.
- DeepSeek-V3.1-Terminus ：Focus on optimizing language consistency (mitigating the confusion of Chinese and English) and Agent capabilities.
- DeepSeek-V3.1 ：Introducing a hybrid reasoning architecture to support fast response and deep thinking mode switching, and the context is expanded to 128K.
- DeepSeek-R1-0528 ：R1 has been significantly upgraded, its reasoning capabilities have been significantly improved, and its performance on mathematical benchmarks (such as AIME 2025) has been significantly enhanced.
- DeepSeek-V3-0324 ：V3 is a slightly enhanced version that improves reasoning, code generation and Chinese writing capabilities.
- DeepSeek-R1 ：The flagship version of reasoning specialization, driven by reinforcement learning, has outstanding mathematics, programming and logical reasoning capabilities.
- DeepSeek-V3 ：The generation speed of the 671B MoE base model is significantly improved compared to V2, laying the foundation for subsequent series.
- DeepSeek-V2.5-1210 ：The subsequent improved version of the V2.5 series integrates Chat and Coder capabilities and continues to optimize.
- DeepSeek-V2.5 ：The V2 series fusion version integrates V2 Chat and Coder V2 capabilities.
- DeepSeek-V2 ：An early key milestone is the formal introduction of the MoE architecture, laying a technical roadmap for subsequent version evolutions.
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