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gemini-embedding-001Gemini-Embedding-001 is Google's high-quality text embedding model designed for semantic understanding and retrieval tasks. It converts text into dense vector representations optimized for semantic search, retrieval-augmented generation (RAG), clustering, classification, and recommendation systems. The model emphasizes strong multilingual performance, high semantic accuracy, and efficient embedding generation, making it well suited for large-scale knowledge indexing and production retrieval pipelines.
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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="gemini-embedding-001", 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="gemini-embedding-001",# messages=[{"role": "user", "content": "Hello!"}],# extra_body={"compression": {"enabled": True, "model": "gpt-4.1-mini"}}# )modelinputencoding_formatdimensionsuserUse these namespaced identifiers in Cursor IDE to avoid conflicts with built-in models.
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Gemini 3.6 Flash is Google's high-efficiency model for coding, agentic workflows, and web and application development. It is optimized to produce polished, production-ready outputs with fewer unnecessary revisions, less hedging, and more direct task execution. By reducing both token usage and the number of model calls required to complete complex tasks, Gemini 3.6 Flash is well suited for high-throughput development, scalable agent systems, and cost-sensitive production workflows.
Gemini 3.5 Flash-Lite is Google's high-efficiency model with enhanced agentic capabilities, optimized for fast, cost-effective inference. It is designed to handle focused tasks with low latency while maintaining strong reasoning and execution quality. Well suited for subagents in complex multi-agent systems, Gemini 3.5 Flash-Lite excels at executing specialized tasks within larger workflows, making it ideal for scalable agent orchestration and high-throughput production environments.
Nano Banana 2 Lite (Gemini 3.1 Flash Lite Image) is Google's fastest and most cost-efficient multimodal image generation model, designed for high-throughput visual workflows and real-time applications. It supports text-to-image generation, image editing, and multi-image composition through a unified API, while also producing text outputs alongside images. Delivering image generation in approximately 4 seconds, it combines fast inference with strong character consistency, precise editing, and real-world knowledge. The model generates 1K-resolution images across 14 aspect ratios and embeds an invisible SynthID watermark in all outputs. Optimized for the best balance of quality, speed, and cost, Nano Banana 2 Lite is ideal for prototyping, developer pipelines, and large-scale visual content generation.
Gemini 3.5 Flash is Google's high-efficiency multimodal model, delivering near-Pro level performance in coding and reasoning at Flash-tier speed and cost. It supports text, image, video, audio, and PDF inputs, making it well suited for diverse multimodal workflows. Optimized for coding proficiency and parallel agentic execution, the model defaults to medium thinking effort for faster, cost-efficient responses while supporting configurable thinking levels (minimal, low, medium, high) for fine-grained cost–performance control.
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Gemini 3.6 Flash is Google's high-efficiency model for coding, agentic workflows, and web and application development. It is optimized to produce polished, production-ready outputs with fewer unnecessary revisions, less hedging, and more direct task execution. By reducing both token usage and the number of model calls required to complete complex tasks, Gemini 3.6 Flash is well suited for high-throughput development, scalable agent systems, and cost-sensitive production workflows.
Gemini 3.5 Flash-Lite is Google's high-efficiency model with enhanced agentic capabilities, optimized for fast, cost-effective inference. It is designed to handle focused tasks with low latency while maintaining strong reasoning and execution quality. Well suited for subagents in complex multi-agent systems, Gemini 3.5 Flash-Lite excels at executing specialized tasks within larger workflows, making it ideal for scalable agent orchestration and high-throughput production environments.
Nano Banana 2 Lite (Gemini 3.1 Flash Lite Image) is Google's fastest and most cost-efficient multimodal image generation model, designed for high-throughput visual workflows and real-time applications. It supports text-to-image generation, image editing, and multi-image composition through a unified API, while also producing text outputs alongside images. Delivering image generation in approximately 4 seconds, it combines fast inference with strong character consistency, precise editing, and real-world knowledge. The model generates 1K-resolution images across 14 aspect ratios and embeds an invisible SynthID watermark in all outputs. Optimized for the best balance of quality, speed, and cost, Nano Banana 2 Lite is ideal for prototyping, developer pipelines, and large-scale visual content generation.
Gemini 3.5 Flash is Google's high-efficiency multimodal model, delivering near-Pro level performance in coding and reasoning at Flash-tier speed and cost. It supports text, image, video, audio, and PDF inputs, making it well suited for diverse multimodal workflows. Optimized for coding proficiency and parallel agentic execution, the model defaults to medium thinking effort for faster, cost-efficient responses while supporting configurable thinking levels (minimal, low, medium, high) for fine-grained cost–performance control.
No observed failures in the current observation window