veo3.1-fastVeo 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-fast", 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-fast",# 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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Nano Banana Pro is Google's most advanced image-generation and editing model, built on Gemini 3 Pro. It delivers stronger multimodal reasoning, real-world grounding, and highly detailed visuals, producing everything from diagrams and infographics to cinematic scenes. It excels at text-in-image rendering, identity consistency, and multi-image blending, while supporting precise creative controls (localized edits, lighting, camera shifts) plus 2K/4K output and flexible aspect ratios — making it suitable for professional design and complex visual composition.
Gemini 2.5 Flash Preview (May 2025) is Google's high-performance general model built for advanced reasoning, coding, math, and science. It includes built-in “thinking” features to deliver more accurate, context-aware answers.
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 2.5 Flash is Google's main high-performance model for complex reasoning, coding, math, and scientific tasks. It has built-in “thinking” features that help it produce more accurate, context-aware answers.
Initialized observational baseline with no recorded failures