DeepSeek: DeepSeek V3

Processes natural language instructions and maintains coherent multi-turn conversations by tracking full conversation history within a context window. Uses transformer-based attention mechanisms trained on 15 trillion tokens to understand nuanced user intent, follow complex instructions, and generate contextually appropriate responses. Supports system prompts for role-based behavior customization and instruction refinement.

Generates syntactically correct, functional code across 40+ programming languages by leveraging transformer attention patterns trained on billions of code tokens. Supports code completion from partial snippets, full function generation from docstrings, and code explanation. Uses context-aware token prediction to maintain language-specific syntax rules, indentation, and idioms without explicit grammar constraints.

Generates explicit reasoning chains that decompose complex problems into intermediate steps, enabling transparent problem-solving logic. Uses chain-of-thought prompting patterns to surface reasoning before final answers, allowing verification of logic at each step. Trained to recognize problem structure and apply appropriate reasoning strategies (mathematical derivation, logical deduction, case analysis) based on problem type.

Exposes model inference through REST API endpoints with support for streaming token-by-token responses, enabling real-time output consumption. Implements OpenAI-compatible API schema for drop-in compatibility with existing LLM application frameworks. Supports batch processing for non-real-time workloads and configurable sampling parameters (temperature, top-p, max-tokens) for controlling output diversity and length.

Enables the model to invoke external tools and APIs by generating structured function calls based on JSON schema definitions. Model receives tool schemas, reasons about which tools to use, and generates properly-formatted function calls with arguments. Supports multi-turn tool use where model can call multiple functions sequentially and incorporate results into reasoning. Implements OpenAI-compatible function-calling protocol for framework compatibility.

Processes extended input sequences up to the model's context window limit (typically 4K-8K tokens, expandable to 32K+ with specific configurations), enabling analysis of long documents, code files, and conversation histories without truncation. Uses efficient attention mechanisms to maintain coherence across long sequences while managing computational costs. Supports retrieval-augmented generation patterns where long documents are passed directly rather than requiring external retrieval systems.

Processes and generates text in 100+ languages including English, Chinese, Spanish, French, German, Japanese, Korean, Arabic, and many others. Uses multilingual transformer embeddings trained on diverse language corpora to maintain semantic understanding across language boundaries. Supports code-switching (mixing languages in single response) and language-aware formatting (RTL text, character encoding, punctuation conventions).

Extracts structured data from unstructured text and generates output conforming to specified JSON schemas. Model receives schema definitions and natural language input, then generates valid JSON output matching the schema structure. Supports nested objects, arrays, optional fields, and type constraints. Enables reliable data extraction for downstream processing without manual parsing or validation.

Model acknowledges its knowledge cutoff date and can reason about temporal information, historical events, and time-dependent facts. Trained to distinguish between information from training data (pre-cutoff) and information requiring real-time lookup. Supports relative date reasoning (e.g., 'what happened 3 months ago') and temporal logic for understanding sequences of events. Does not hallucinate future information or claim knowledge of events after training cutoff.

Generates responses that avoid producing harmful, illegal, or unethical content through alignment training and safety filters. Model is trained to refuse requests for illegal activities, violence, hate speech, sexual content involving minors, and other harmful outputs. Implements graceful refusal patterns that explain why requests cannot be fulfilled rather than abruptly blocking users. Supports configurable safety levels for different use cases.