DeepSeek-V3
DeepSeek-V3 is a text generation model from DeepSeek with a 128,000-token context window, released in December 2024.
DeepSeek
DeepInfraLarge-scale mixture-of-experts text generation
DeepSeek-V3 is a large language model developed by DeepSeek and released in December 2024. It uses a Mixture-of-Experts (MoE) architecture, activating a subset of its total parameters per forward pass, which allows it to handle a wide range of text generation tasks efficiently. The model supports a context window of 128,000 tokens, making it suitable for long-document processing, extended conversations, and complex multi-step tasks.
DeepSeek-V3 is designed for general-purpose text generation, including coding assistance, reasoning, summarization, and instruction following. It is served on MindStudio via DeepInfra and is tagged as a fast model, indicating optimized inference throughput. The maximum response size is 8,000 tokens, and the model accepts standard text input, making it straightforward to integrate into a wide variety of AI-powered workflows.
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 conversation histories.
Fast Inference
Tagged as a fast model, DeepSeek-V3 is optimized for low-latency responses suitable for interactive and real-time applications.
Code Generation
Generates, explains, and debugs code across multiple programming languages, a well-documented strength of the DeepSeek-V3 model family.
Instruction Following
Responds to structured prompts and multi-step instructions, supporting chat-style interactions through its llm_chat model type.
Mixture-of-Experts Architecture
Uses an MoE design that activates only a portion of total parameters per inference pass, enabling large model capacity with efficient compute usage.
Text Summarization
Condenses long-form content into concise summaries, leveraging its 128,000-token context to handle full documents in a single pass.
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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 size 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 maximum response length?
The maximum response size for DeepSeek-V3 is 8,000 tokens per generation.
Does DeepSeek-V3 support image or video inputs?
No. Based on the available metadata, DeepSeek-V3 does not support image or video analysis. It accepts standard text input only.
When was DeepSeek-V3 released?
DeepSeek-V3 was released in December 2024 by DeepSeek.
What is the architecture behind DeepSeek-V3?
DeepSeek-V3 uses a Mixture-of-Experts (MoE) architecture, which activates only a subset of its parameters during each forward pass, enabling large model capacity with more efficient inference.
Who provides DeepSeek-V3 on MindStudio?
DeepSeek-V3 is served on MindStudio through DeepInfra as the infrastructure provider.
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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