Models

Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response.

Gemini 3.1 Flash Lite Image served from Google Vertex AI. Billed per image at US$0.0336. Call it through /v1/chat/completions with stream set to false; the image is returned in the message content as a base64 data URI.

Gemini 3.1 Flash Lite Image served from Google AI Studio. Billed per image at US$0.0336. Call it through /v1/chat/completions with stream set to false; the image is returned in the message content as a base64 data URI.

Gemini 3.1 Flash Image served from Google Vertex AI. Billed per image at US$0.067. Call it through /v1/chat/completions with stream set to false; the image is returned in the message content as a base64 data URI.

Gemini 3.1 Flash Image served from Google AI Studio. Billed per image at US$0.067. Call it through /v1/chat/completions with stream set to false; the image is returned in the message content as a base64 data URI.

Gemini 3 Pro Image served from Google Vertex AI. Billed per image at US$0.134. Call it through /v1/chat/completions with stream set to false; the image is returned in the message content as a base64 data URI.

Gemini 3 Pro Image served from Google AI Studio. Billed per image at US$0.134. Call it through /v1/chat/completions with stream set to false; the image is returned in the message content as a base64 data URI.

Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.

Gemini 3.8 Flash is Google's most intelligent Flash-class model, delivering significant improvements over Gemini 3.7 Flash across software engineering, agentic workflows, and complex multi-step reasoning. Designed to combine strong capability with Flash-tier efficiency, it is well suited for coding assistants, autonomous agents, and high-throughput production workflows that require responsive performance without sacrificing reasoning quality.

Gemini 3.7 Flash is Google's fast multimodal model designed for agentic workflows, coding, and complex multi-step reasoning. It combines responsive inference with reliable problem-solving capabilities, making it well suited for interactive and production-scale applications. Optimized for speed and dependable multi-step execution, Gemini 3.7 Flash is a strong choice for coding assistants, autonomous agents, and high-throughput workflows that require both low latency and capable reasoning.

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.

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.

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.

Gemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind, featuring 25.2B total parameters with only 3.8B activated per token—delivering near 31B-class quality at a fraction of the compute cost. It supports multimodal inputs including text, images, and video (up to 60s at 1fps). The model includes a 256K token context window, native function calling, configurable thinking/reasoning modes, and structured output support. Released under the Apache 2.0 license, it is well suited for efficient, production-ready multimodal and agentic applications.

Gemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind, featuring 25.2B total parameters with only 3.8B activated per token—delivering near 31B-class quality at a fraction of the compute cost. It supports multimodal inputs including text, images, and video (up to 60s at 1fps). The model includes a 256K token context window, native function calling, configurable thinking/reasoning modes, and structured output support. Released under the Apache 2.0 license, it is well suited for efficient, production-ready multimodal and agentic applications.

Gemma 4 31B Instruct is Google DeepMind's 30.7B dense multimodal model, supporting text and image inputs with text outputs. It features a 256K token context window, configurable thinking/reasoning modes, native function calling, and broad multilingual support across 140+ languages. The model delivers strong performance in coding, reasoning, and document understanding, making it well suited for developer workflows, multilingual applications, and structured knowledge tasks.

Gemma 4 31B Instruct is Google DeepMind's 30.7B dense multimodal model, supporting text and image inputs with text outputs. It features a 256K token context window, configurable thinking/reasoning modes, native function calling, and broad multilingual support across 140+ languages. The model delivers strong performance in coding, reasoning, and document understanding, making it well suited for developer workflows, multilingual applications, and structured knowledge tasks.

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 3.1 Flash Lite Preview is Google's high-efficiency model designed for high-volume and cost-sensitive workloads. It improves overall quality compared to Gemini 2.5 Flash Lite while approaching the performance of Gemini 2.5 Flash across key capabilities. The model delivers enhancements in audio input/ASR, RAG snippet ranking, translation, data extraction, and code completion, and supports configurable thinking levels (minimal, low, medium, high) for flexible cost–performance optimization. With pricing at roughly half the cost of Gemini 3 Flash, it is well suited for large-scale production deployments.

Gemini 3.1 Pro Preview is Google's frontier reasoning model, delivering enhanced software engineering performance, improved agentic reliability, and more efficient token usage across complex workflows. Built on the multimodal foundation of the Gemini 3 series, it combines high-precision reasoning across text, image, video, audio, and code with a 1M-token context window for large-scale tasks. The 3.1 update introduces measurable gains on SWE benchmarks and real-world coding environments, along with stronger autonomous execution in structured domains such as finance and spreadsheet-based workflows. Designed for advanced development and agentic systems, it improves long-horizon stability and tool orchestration while adding a new medium thinking level to better balance cost, speed, and performance. Gemini 3.1 Pro Preview is well suited for agentic coding, structured planning, multimodal analysis, financial modeling, spreadsheet automation, and high-context enterprise applications.

Gemini 3 Flash Preview is a fast, cost-efficient reasoning model designed for agent workflows, multi-turn chat, and coding assistance. It offers near-Pro level reasoning and tool-use performance with significantly lower latency than larger Gemini models, making it ideal for interactive development and long-running agent loops. It improves on Gemini 2.5 Flash with stronger reasoning, multimodal understanding, and reliability. The model supports a 1M-token context window and multimodal inputs (text, images, audio, video, PDFs) with text outputs. It provides configurable reasoning levels, structured output formats, tool use, and automatic context caching—optimized for users seeking strong agentic reasoning without the cost or latency of full frontier-scale models.

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 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 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 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 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.

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.

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.

Gemini 2.5 Pro is Google's top reasoning model for coding, math, and scientific work. It uses built-in “thinking” to deliver more accurate, context-aware answers and ranks at the top of major benchmarks like LMArena, showing strong alignment and problem-solving ability.

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.

Veo 3 Fast by Google. Use it from Apertis SDKs, provider-compatible SDKs, or direct HTTP requests.

Veo 3 by Google. Use it from Apertis SDKs, provider-compatible SDKs, or direct HTTP requests.

Veo 3 Pro by Google. Use it from Apertis SDKs, provider-compatible SDKs, or direct HTTP requests.

Veo 3 Pro Frames by Google. Use it from Apertis SDKs, provider-compatible SDKs, or direct HTTP requests.

Gemma 3 1B by Google. Use it from Apertis SDKs, provider-compatible SDKs, or direct HTTP requests.

Gemma 3 12B by Google. Use it from Apertis SDKs, provider-compatible SDKs, or direct HTTP requests.

Gemma 3 4B by Google. Use it from Apertis SDKs, provider-compatible SDKs, or direct HTTP requests.

Gemma 3 27B by Google. Use it from Apertis SDKs, provider-compatible SDKs, or direct HTTP requests.

Gemini 2.0 Flash Lite by Google. Use it from Apertis SDKs, provider-compatible SDKs, or direct HTTP requests.

Gemini Flash 2.0 by Google. Use it from Apertis SDKs, provider-compatible SDKs, or direct HTTP requests.

Gemini Flash Lite 2.0 Preview by Google. Use it from Apertis SDKs, provider-compatible SDKs, or direct HTTP requests.

Gemini Pro 2.0 Experimental by Google. Use it from Apertis SDKs, provider-compatible SDKs, or direct HTTP requests.

Gemini 1.5 Pro 002 by Google. Use it from Apertis SDKs, provider-compatible SDKs, or direct HTTP requests.

Gemini 1.5 Flash Experimental 0827 by Google. Use it from Apertis SDKs, provider-compatible SDKs, or direct HTTP requests.

Gemma 2 9B by Google. Use it from Apertis SDKs, provider-compatible SDKs, or direct HTTP requests.

Gemma 2 27B by Google. Use it from Apertis SDKs, provider-compatible SDKs, or direct HTTP requests.

Gemini 1.5 Pro by Google. Use it from Apertis SDKs, provider-compatible SDKs, or direct HTTP requests.