Qwen: Qwen3 30B A3B Thinking 2507

Implements a dual-stream architecture where internal reasoning processes are explicitly separated from final outputs, allowing the model to perform multi-step logical decomposition before generating responses. The model uses a Mixture-of-Experts (MoE) routing mechanism to allocate computational resources across specialized reasoning pathways, enabling deeper exploration of problem spaces without exposing intermediate scaffolding to users unless explicitly requested.

Implements a sparse MoE architecture where the 30B parameter model dynamically routes tokens to specialized expert sub-networks based on learned routing decisions, reducing per-token computational cost compared to dense models while maintaining reasoning capacity. The routing mechanism learns which experts are optimal for different token types and reasoning phases, enabling efficient allocation of the full parameter capacity without computing all parameters for every token.

Maintains conversation history across multiple turns while preserving reasoning traces and intermediate thinking states, allowing the model to reference prior reasoning steps and build on previous logical decompositions. The architecture manages separate context streams for thinking and response content, enabling coherent multi-turn reasoning where later turns can reference or refine earlier reasoning without losing interpretability.

Automatically decomposes complex problems into sub-problems and reasoning phases, using the MoE architecture to route different problem aspects through specialized reasoning experts. The model learns to identify problem structure (e.g., mathematical vs. logical vs. code-based reasoning) and allocate reasoning capacity accordingly, producing structured reasoning traces that show problem decomposition steps.

Exposes the model through OpenRouter's API with support for streaming responses, token counting, and fine-grained control over thinking vs. response token allocation. Clients can stream thinking traces and responses separately, control maximum thinking tokens, and receive detailed token usage metrics including thinking token costs, enabling precise cost management and real-time response handling.

Analyzes and generates code by leveraging extended reasoning to understand code structure, dependencies, and correctness properties before generating solutions. The model uses reasoning experts to decompose code problems (refactoring, debugging, optimization) into logical steps, producing code with explicit reasoning traces that justify design decisions and correctness claims.

Solves mathematical problems by generating explicit step-by-step reasoning traces that function as proofs or derivations, using specialized mathematical reasoning experts to handle symbolic manipulation, logical inference, and numerical computation. The model produces reasoning traces that show each algebraic step, logical inference, or computational operation, enabling verification of mathematical correctness.