by Jina AI
DeepSearch combines search, reading, and reasoning capabilities to pursue the best possible answer. It's fully compatible with OpenAI's Chat API format—just replace api.openai.com with aihubmix.com to get started. The stream will return the thinking process.
DeepSearch combines search, reading, and reasoning capabilities to pursue the best possible answer. It's fully compatible with OpenAI's Chat API format—just replace api.openai.com with aihubmix.com to get started. The stream will return the thinking process.
jina-deepsearch-v1 has a 1,000,000 token context window.
On AIHubMix, jina-deepsearch-v1 costs $0.05 per million input tokens and $0.05 per million output tokens.
jina-deepsearch-v1 accepts text and image input.
jina-deepsearch-v1 supports thinking, web and deepsearch. Per-protocol parameter support is listed in the capability table on this page.
jina-deepsearch-v1 is available through the AIHubMix unified API. The API is OpenAI-compatible: point your OpenAI SDK at https://aihubmix.com/v1, use your AIHubMix API key, and set the model name to jina-deepsearch-v1 — no other code changes needed.
jina-deepsearch-v1 is developed by Jina AI. AIHubMix aggregates it alongside models from other providers behind one API and one bill.
jina-reranker-v3.5 is a 0.6B-parameter multilingual listwise document reranker and a…
A 3.8-billion-parameter general vector model (embedding model) for state-of-the-art…
A 3.8-billion-parameter general vector (embedding) model providing state-of-the-art…
A general-purpose vector model with 3.8 billion parameters, used for multimodal and…
Multimodal multilingual document reranker, 131K context, 0.6B parameters, for visual…
Multi-modal Embeddings Model, multilingual, 1024-dimensional, 865M parameters.
Use jina-deepsearch-v1 via the AIHubMix unified API — one interface for every major LLM.