tongyi-deepresearch-30b-a3bTongyi DeepResearch is a 30B-parameter agentic model (3B active per token) built for long-horizon, deep research and information-seeking tasks. It achieves state-of-the-art results on major agentic search and reasoning benchmarks, outperforming prior models in complex multi-step problem solving. Trained with a fully automated synthetic data pipeline and advanced on-policy RL, it supports ReAct workflows and a high-performance “Heavy” mode for test-time scaling, making it well suited for advanced research agents, tool use, and intensive inference workloads.
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from openai import OpenAI client = OpenAI( api_key="YOUR_API_KEY", base_url="https://api.apertis.ai/v1") response = client.chat.completions.create( model="tongyi-deepresearch-30b-a3b", messages=[ {"role": "user", "content": "Hello!"} ], max_tokens=1024, temperature=0.7) print(response.choices[0].message.content) # Optional: Enable context compression to reduce token usage# response = client.chat.completions.create(# model="tongyi-deepresearch-30b-a3b",# messages=[{"role": "user", "content": "Hello!"}],# extra_body={"compression": {"enabled": True, "model": "gpt-4.1-mini"}}# )modelmessagesmax_tokenstemperaturetop_pstreamtoolsreasoning_effortstream_optionsthinkingextra_bodyUse these namespaced identifiers in Cursor IDE to avoid conflicts with built-in models.
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See how this model compares to others from the same provider.
Qwen3.7 Flash is Alibaba's vision-language reasoning model, designed for multimodal agents, visual coding, search, and computer interaction. It combines fast inference with strong visual understanding, including object recognition, spatial reasoning, and real-world scene perception. Optimized for interactive and agentic workflows, Qwen3.7 Flash is well suited for GUI understanding, visual question answering, multimodal search, and computer-use applications that require responsive reasoning across text and images.
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