DeepSeek-V3.1

DeepSeek-V3.1 is a cutting-edge open-source large language model featuring 685 billion parameters, delivering state-of-the-art performance in coding, AI agent deployment, mathematical reasoning, and general intelligence tasks. It outperforms many proprietary models in benchmarks while being fully accessible for fine-tuning and local deployment. The model also offers a highly affordable API service, enabling seamless integration for production applications at a fraction of competitors' costs.

FREEMIUM

4.8(0 reviews)

open-sourcemixture-of-experts671B-parameterscoding-specialistmath-reasoningmultilinguallong-contextefficient-inferenceApache-2.0frontier-modelChinese-Englishzero-shotfew-shot-learninghigh-throughputMoE-architecturedeepseek-aistate-of-the-artbenchmark-leaderscalable-trainingcost-effective

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About DeepSeek-V3.1

DeepSeek-V3.1 represents a major advancement in open-source AI, with its massive 685 billion parameters enabling superior capabilities in code generation, debugging, and software engineering workflows, as well as deploying autonomous AI agents for complex tasks. Its reasoning prowess shines in logical puzzles, multi-step problem-solving, and scientific simulations, often rivaling or surpassing models like GPT-4o and Claude 3.5 Sonnet on key benchmarks such as HumanEval, MATH, and GPQA. Released under an permissive license, the model weights are freely available on Hugging Face, allowing researchers and developers worldwide to customize and run it on high-end hardware. Complementing the open-source release, DeepSeek provides a low-cost API with enterprise-grade reliability, supporting high-throughput inference for startups and businesses seeking cost-effective scaling without vendor lock-in.

Key Features

Mixture-of-Experts (MoE) architecture with 671B total parameters and 37B active parameters

Supports 128K context length for long-form conversations and document processing

Trained on 14.8 trillion high-quality tokens for broad knowledge coverage

State-of-the-art performance on benchmarks like MMLU (88.5%), GPQA (59.1%), and MATH (73.8%)

Open-source under MIT license with full weights and training code available

Multilingual capabilities supporting over 100 languages

Efficient inference optimized with FP8 quantization and GQA (Grouped Query Attention)

DeepSeek-V3-Chat variant fine-tuned for instruction-following and role-playing

RMSNorm normalization and SwiGLU activations for stable training

Native support for tool use and function calling

High throughput on multi-GPU setups (up to 60 tokens/s on H100)

Seamless integration with frameworks like vLLM and Hugging Face Transformers

Advanced reasoning capabilities rivaling GPT-4o and Claude 3.5 Sonnet

Pros

Exceptional cost-efficiency: trained for ~$5.6M, far cheaper than competitors
Top-ranked open model on LMSYS Arena leaderboard
Fully open-source enabling customization and research
Superior performance in coding (HumanEval: 89.0%) and math tasks
Low inference latency due to MoE sparsity
Runs quantized versions on consumer GPUs (e.g., 24GB VRAM for 4-bit)
Strong multilingual reasoning without degradation
Rapid iteration with frequent updates from DeepSeek team
Versatile across general, technical, and creative domains

Cons

Requires substantial hardware for full-precision inference (hundreds of GB VRAM)
Occasional inconsistencies in long-context retrieval
Potential cultural biases from training data skewed toward English/Chinese
Slower cold-start inference compared to dense models
Limited fine-tuning examples for niche domains

Use Cases

Advanced code generation and debugging for software engineersMathematical problem-solving and theorem provingScientific research assistance and hypothesis generationMultilingual translation and localization servicesBuilding intelligent chatbots and virtual assistantsData analysis and visualization scriptingContent creation for blogs, reports, and marketingEducational tutoring systems for STEM subjectsEnterprise knowledge base querying and summarizationTool-augmented agents for web browsing and APIsLocal AI deployment for privacy-sensitive applications

Pricing

Model:Pay-per-token

Price:$0.27 per million input tokens / $1.10 per million output tokens (for 685B model)

Quick Info

API Available:Yes

Popularity:92/100

Official Website

Integrations

Hugging Face TransformersvLLMSGLangTensorRT-LLMOllamaLM StudioJan.aiGroqFireworks AITogether AIDeepInfraReplicate

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