DeepSeek: DeepSeek V3.1

DeepSeek-V3.1 implements a two-phase reasoning architecture where users can explicitly trigger an internal 'thinking' phase via prompt templates before generating responses. The model allocates computational budget to chain-of-thought reasoning within a hidden thinking token stream, then produces final outputs based on that reasoning. This is distinct from implicit reasoning — thinking is user-controlled and can be toggled on/off per request, enabling cost-performance tradeoffs.

DeepSeek-V3.1 implements a two-phase long-context architecture that processes extended input sequences (likely 128K+ tokens) by first compressing or summarizing context in phase one, then performing reasoning/generation in phase two. This reduces memory pressure and enables handling of very long documents, codebases, or conversation histories without proportional latency increases. The architecture is optimized for the 671B parameter model with 37B active parameters.

DeepSeek-V3.1 is available through OpenRouter, a multi-model abstraction layer that provides a unified REST API for accessing multiple LLMs (DeepSeek, OpenAI, Anthropic, etc.). OpenRouter handles model routing, fallback logic, and unified pricing, allowing developers to switch between models or implement cost-optimized routing without changing application code. The API is compatible with OpenAI's format, reducing migration friction.

DeepSeek-V3.1 maintains conversation state across multiple turns, allowing users to build multi-turn dialogues where the model retains context from previous exchanges. The implementation uses a message history buffer that tracks roles (user/assistant) and content, enabling coherent follow-up questions, clarifications, and context-dependent reasoning. Context is managed at the API level — users pass full conversation history with each request, and the model processes it through the two-phase architecture.

DeepSeek-V3.1 generates and analyzes code by combining its 671B parameter capacity with explicit reasoning mode, enabling it to understand complex code structures, suggest refactorings, identify bugs, and generate multi-file solutions. The model can process entire codebases as context (via long-context capability) and reason about architectural patterns, dependencies, and correctness. Code generation is informed by both the thinking phase (for complex logic) and the full codebase context.

DeepSeek-V3.1 solves mathematical problems by leveraging its reasoning mode to decompose problems into steps, verify intermediate results, and produce final answers with justification. The thinking phase allows the model to explore multiple solution approaches, check for errors, and select the most reliable path. This is particularly effective for algebra, calculus, discrete math, and logic problems where step-by-step verification is critical.

DeepSeek-V3.1 is accessed via REST API (through OpenRouter or direct endpoint) with support for streaming responses, allowing real-time token-by-token output. The API accepts JSON payloads with messages, system prompts, and generation parameters (temperature, max_tokens, top_p), and returns either streamed Server-Sent Events (SSE) or complete responses. This enables building responsive chat interfaces and real-time applications without waiting for full response generation.

DeepSeek-V3.1 accepts a system prompt parameter that defines the model's behavior, tone, and constraints for a conversation. The system prompt is processed at the beginning of each request and influences all subsequent responses in that conversation turn. This enables building specialized assistants (e.g., code reviewer, math tutor, creative writer) by injecting role-specific instructions without fine-tuning.

DeepSeek-V3.1 exposes fine-grained control over generation parameters including temperature (0.0-2.0 for randomness), top_p (nucleus sampling for diversity), and max_tokens (output length limit). These parameters are passed per-request via the API, allowing users to tune the model's behavior from deterministic (temperature=0) to highly creative (temperature=2.0) without retraining. This enables building applications with different generation strategies for different use cases.

DeepSeek-V3.1 API responses include detailed token usage information (prompt tokens, completion tokens, total tokens), enabling developers to track costs and optimize token consumption. The API returns usage data in the response metadata, allowing real-time cost calculation based on published pricing. This enables building cost-aware applications that can make decisions about when to use reasoning mode, compress context, or batch requests.

DeepSeek-V3.1 API implements standard HTTP error codes and rate limiting to manage request volume and prevent abuse. The API returns appropriate status codes (400 for bad requests, 401 for auth failures, 429 for rate limits, 500 for server errors) and includes rate limit headers indicating remaining quota. Developers must implement retry logic with exponential backoff to handle transient failures and rate limit responses.