veo3.1Veo 3.1 is a state-of-the-art generative AI video model developed by Google DeepMind (part of the broader Gemini/Flow ecosystem). It builds on the earlier Veo models to make AI-generated video creation more realistic, expressive, and controllable.
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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="veo3.1", 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="veo3.1",# messages=[{"role": "user", "content": "Hello!"}],# extra_body={"compression": {"enabled": True, "model": "gpt-4.1-mini"}}# )modelpromptsizedurationresponse_formatseedwatermarkidcontentremixUse these namespaced identifiers in Cursor IDE to avoid conflicts with built-in models.
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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.
Gemini 3.1 Flash TTS Preview is Google's next-generation text-to-speech model, delivering a major upgrade over Gemini 2.5 Flash TTS. It converts text into natural audio across 70+ languages, with significantly expanded language coverage and improved quality. The model introduces 200+ inline audio control tags (e.g., [whispers], [laughs], [excited]) for fine-grained control over emotion, tone, and pacing, along with support for two speakers with independent voice and style settings. It outputs 24 kHz / 16-bit PCM audio, includes SynthID watermarking, and supports a 32K token context window. Designed for expressive and controllable voice generation, it is well suited for dialogue systems, storytelling, character-driven content, and advanced audio production workflows.
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Gemini 2.5 Flash Image (“Nano Banana”) is now generally available. It’s a state-of-the-art image generation model with strong contextual understanding, supporting image creation, editing, and multi-turn conversational workflows around visuals.
Gemini Embedding 2 is Google's advanced text embedding model designed for high-accuracy semantic representation across large-scale retrieval and understanding tasks. It converts text into dense vector embeddings optimized for semantic search, retrieval-augmented generation (RAG), clustering, classification, and recommendation systems. Built for production use, it offers strong multilingual support, improved semantic similarity accuracy, and efficient embedding generation, making it well suited for large knowledge indexing pipelines and enterprise-scale retrieval applications.
Gemini 2.5 Flash-Lite is a lightweight, low-latency model focused on speed and cost efficiency. It generates tokens quickly and outperforms earlier Flash models on common benchmarks. “Thinking” (multi-pass reasoning) is off by default for maximum speed, but can be turned on through the Reasoning API when deeper reasoning is needed.
Gemini 2.5 Flash-Lite is a smaller, low-latency model focused on speed and cost efficiency. It delivers faster generation and better benchmark performance than earlier Flash models. Thinking mode is off by default for maximum speed, but developers can enable it when they want deeper reasoning at a higher cost.
Initialized observational baseline with no recorded failures