Tongyi DeepResearch 30B A3B

Executes multi-step research tasks over extended reasoning horizons by decomposing complex information-seeking goals into sub-queries, iteratively refining search strategies, and synthesizing findings across multiple sources. Uses an agentic loop architecture where the model decides when to search, what to search for, and when sufficient evidence has been gathered to answer the original query, enabling autonomous deep research without human intervention between steps.

The model autonomously generates search queries based on information gaps identified during reasoning, executes searches, evaluates results, and decides whether to refine the search strategy or proceed to synthesis. This differs from simple retrieval by having the model control the search loop — it determines query reformulation, decides when to pivot search strategy, and identifies when sufficient information has been gathered, implementing a feedback loop between reasoning and information retrieval.

Aggregates information from multiple search results and sources, identifies contradictions or conflicting claims, and synthesizes a coherent answer by reasoning about source credibility, recency, and relevance. The model maintains awareness of source provenance throughout reasoning and explicitly addresses conflicts rather than simply merging results, producing a unified narrative that acknowledges uncertainty where sources disagree.

Maintains coherent reasoning across extended context windows by using a mixture-of-experts (MoE) architecture where only 3 billion of 30 billion parameters activate per token, reducing computational overhead while preserving reasoning depth. This sparse activation pattern allows the model to process longer reasoning chains, maintain state across multiple research iterations, and synthesize information from numerous sources without the latency and memory penalties of dense full-parameter models.

Automatically breaks down complex, multi-faceted research questions into sub-tasks, executes them in a logical sequence, and combines results into a coherent final answer. The model identifies task dependencies, determines optimal execution order, and manages state across sub-tasks without explicit user guidance on decomposition strategy. This enables handling of queries that would normally require manual step-by-step prompting.

Streams research progress and intermediate reasoning steps to the user in real-time, allowing visibility into what searches are being executed, what information gaps are being identified, and how the model is synthesizing results. Rather than waiting for a final answer, users see the research process unfold, including search queries executed, results evaluated, and reasoning about next steps, enabling early intervention if the research direction is incorrect.

Automatically identifies gaps in the current research and generates follow-up questions that would deepen understanding or fill missing information. The model maintains awareness of what has been learned so far and what remains unclear, suggesting natural next questions that build on previous research rather than starting fresh. This enables continuous research refinement without users having to manually think of follow-up questions.

Provides access to the Tongyi DeepResearch model through OpenRouter's unified API interface, enabling integration without direct Alibaba endpoint management. OpenRouter abstracts provider-specific details (authentication, rate limiting, error handling) behind a standard REST API, allowing developers to integrate the model using familiar HTTP patterns and switch providers without code changes. Supports streaming responses, token counting, and standard LLM API conventions.