DeepSeek-V3
General-purpose LLM from DeepSeek.
DeepSeek 
DeepInfra General-purpose text generation with large context
DeepSeek-V3 is a large language model developed by DeepSeek, a Chinese AI company. It is a general-purpose text generation model designed to handle a wide range of tasks including coding, reasoning, summarization, and open-ended conversation. The model supports a 128,000-token context window and was trained on data through late 2024. It is identified by the model ID deepseek-chat and is available via API.
DeepSeek-V3 uses a Mixture-of-Experts (MoE) architecture with 671 billion total parameters, activating 37 billion per forward pass, which allows it to maintain efficiency at scale. The model was trained using an optimized pipeline that includes multi-token prediction and FP8 mixed-precision training. It is well-suited for tasks that require long-context understanding, instruction following, and multi-step reasoning across technical and general domains.
What DeepSeek-V3 supports
Long Context Window
Processes up to 128,000 tokens in a single request, enabling analysis of long documents, codebases, or extended conversations without truncation.
Fast Inference
Tagged as FAST, the model is optimized for low-latency responses through its MoE architecture, which activates only 37 billion of its 671 billion parameters per forward pass.
Code Generation
Generates, explains, and debugs code across multiple programming languages, with strong performance on coding benchmarks reported in DeepSeek's technical report.
Instruction Following
Responds to structured prompts and multi-step instructions, making it suitable for task automation, content generation, and assistant-style workflows.
Mathematical Reasoning
Handles multi-step mathematical problems using chain-of-thought style reasoning, supported by training on diverse math and science datasets.
Multilingual Text
Supports text generation and comprehension in multiple languages, with particular strength in English and Chinese based on training data composition.
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Get Started FreeBenchmark scores
Scores represent accuracy — the percentage of questions answered correctly on each test.
| Benchmark | What it tests | Score |
|---|---|---|
| MMLU-Pro | Expert knowledge across 14 academic disciplines | 75.2% |
| GPQA Diamond | PhD-level science questions (biology, physics, chemistry) | 55.7% |
| MATH-500 | Undergraduate and competition-level math problems | 88.7% |
| AIME 2024 | American math olympiad problems | 25.3% |
| LiveCodeBench | Real-world coding tasks from recent competitions | 35.9% |
| HLE | Questions that challenge frontier models across many domains | 3.6% |
| SciCode | Scientific research coding and numerical methods | 35.4% |
Common questions about DeepSeek-V3
What is the context window for DeepSeek-V3?
DeepSeek-V3 supports a context window of 128,000 tokens, allowing it to process long documents or extended conversations in a single request.
What is the knowledge cutoff for DeepSeek-V3?
Based on the available metadata, DeepSeek-V3 was trained on data through late 2024.
What model ID is used to access DeepSeek-V3 on MindStudio?
DeepSeek-V3 is accessed using the model ID deepseek-chat within MindStudio.
What type of tasks is DeepSeek-V3 designed for?
DeepSeek-V3 is a general-purpose text generation model suited for coding, reasoning, summarization, instruction following, and multilingual conversation.
What architecture does DeepSeek-V3 use?
DeepSeek-V3 uses a Mixture-of-Experts (MoE) architecture with 671 billion total parameters, activating 37 billion per forward pass. It was trained with FP8 mixed-precision training and multi-token prediction techniques.
What people think about DeepSeek-V3
Community discussions around DeepSeek-V3 are active and largely positive, with users on r/LocalLLaMA frequently sharing model releases and Hugging Face checkpoints for variants like V3.1 and V3.2. The model has attracted significant attention for its open availability and iterative versioning.
Some threads highlight competitive benchmarking discussions, with users comparing DeepSeek-V3 variants against other models in the open-source space. A notable thread in r/singularity raised questions about model identity disclosure after another model reportedly identified itself as DeepSeek-V3, sparking broader conversation about transparency in AI systems.
Sonnet 4.6 states "I am DeepSeek-V3, an AI assistant developed by DeepSeek" when asked "what model are you" by multiple users in Chinese
deepseek-ai/DeepSeek-V3.2 · Hugging Face
deepseek-ai/DeepSeek-V3.1-Base · Hugging Face
Step-3.5-Flash (196b/A11b) outperforms GLM-4.7 and DeepSeek v3.2
DeepSeek-V3.2 released
Parameters & options
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