DeepSeek v4
DeepSeek v4 is a 1.6T parameter MoE model featuring a 1M token context window and native multimodal support for text, vision, and video at disruptive prices.
deepseekDeepSeek-V2026-04-23About DeepSeek v4
Learn about DeepSeek v4's capabilities, features, and how it can help you achieve better results.
High-Efficiency trillion-Scale Architecture
DeepSeek v4 represents an evolution in Mixture-of-Experts (MoE) design, scaling to 1.6 trillion total parameters with 49 billion active parameters. The model integrates Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA) to manage its 1-million-token context window. These technologies reduce the KV cache memory footprint by 90% compared to standard architectures, allowing for faster inference and lower hardware requirements for long-context tasks. ### Native Multimodal Integration Unlike models that use separate vision or audio encoders, DeepSeek v4 is natively multimodal from the initial training phase. It processes text, images, audio, and video within a single unified framework. This approach improves cross-modal reasoning, enabling the model to perform complex analysis on raw video files and large-scale document archives without losing granular details. ### Strategic Cost Disruption The model is positioned as a performant open-source alternative to high-tier proprietary models. With pricing at $1.74 per million input tokens, it maintains frontier-level performance in coding and mathematics while significantly reducing operational costs for developers. The inclusion of an optional Thinking Mode allows for deep reasoning for logical proofs and competitive programming.
Use Cases
Discover the different ways you can use DeepSeek v4 to achieve great results.
Large-Scale Codebase Refactoring
Utilizing the 1M context window to ingest entire repositories for global bug detection and architectural improvements.
Native Video Analysis
Processing raw video files directly to perform scene detection, transcript generation, and complex visual reasoning.
Autonomous Software Agents
Deploying the model in agentic workflows to resolve real-world GitHub issues with an 80.6% success rate on SWE-bench.
Multi-Modal Content Creation
Generating structured data and creative content across text, image, and audio formats using a unified model.
High-Tier Mathematical Proofs
Solving Olympiad-level math problems and formal proofs using the specialized Thinking Mode for deep reasoning.
Enterprise Knowledge Retrieval
Analyzing massive document archives in a single prompt to extract facts without the need for complex RAG pipelines.
Strengths
Limitations
API Quick Start
deepseek/deepseek-v4-pro
import OpenAI from 'openai'; const deepseek = new OpenAI({ baseURL: 'https://api.deepseek.com', apiKey: process.env.DEEPSEEK_API_KEY, }); const msg = await deepseek.chat.completions.create({ model: 'deepseek-v4-pro', messages: [{ role: 'user', content: 'Optimize this Rust kernel for memory efficiency.' }], }); console.log(msg.choices[0].message.content);Install the SDK and start making API calls in minutes.
Community Feedback
See what the community thinks about DeepSeek v4
“DeepSeek v4's reasoning mode found a concurrency bug in my Rust code that even Claude Opus missed. Truly insane.”
“The era of cost-effective 1M context is finally here. We can now run full-project refactors for pennies.”
“Seeing the model work through a 1M token codebase without losing the 'needle' is the real turning point for 2026.”
“Anthropic and OpenAI have a serious pricing problem now. DeepSeek just made frontier AI a commodity.”
“It beats GPT-5.4 in coding benchmarks while being open source. This is the biggest release of the year.”
“The memory compression is the real magic. 1T parameters on consumer-ish hardware is finally becoming real.”
Related Videos
Watch tutorials, reviews, and discussions about DeepSeek v4
“The memory efficiency is the real story here, slashing KV cache by 90% changes everything”
“Running a 1T model with this level of speed is a massive architectural win”
“The cost per million tokens makes it impossible for small startups to ignore”
“I've never seen an open source model handle 1 million tokens this cleanly”
“It feels like the gap between open and closed models has officially closed”
“DeepSeek is no longer just competing on price; they are leading in long-context reasoning”
“The native video support is surprisingly robust compared to Gemini 2.0”
“Installing this locally is surprisingly easy if you use SGLang”
“Benchmarks on HumanEval show it is essentially at parity with GPT-5”
“The context window makes RAG pipelines almost redundant for medium projects”
“Performance on coding benchmarks is currently unmatched by any other open-weight model”
“It matches or exceeds top tier closed models in massive codebase refactoring”
“The engram memory implementation is a technical marvel in this space”
“We are seeing 90% logic accuracy in Thinking Mode for Olympiad math”
“This release effectively democratizes trillion-parameter intelligence”
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Pro Tips
Expert tips to help you get the most out of DeepSeek v4 and achieve better results.
Toggle Thinking Modes
Use the standard mode for rapid chat and reserve Thinking Mode specifically for coding and logical proofs.
Leverage Context Caching
Utilize built-in context caching features to reduce costs by up to 90% when using repetitive long-context prompts.
Direct Multimodal Input
Feed raw audio and video files directly into the API to benefit from native architecture rather than pre-transcribing.
System Prompt Optimization
Provide clear JSON schema or tool-use instructions in the system prompt for highly reliable agentic behavior.
Testimonials
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Frequently Asked Questions
Find answers to common questions about DeepSeek v4