Google Vertex Provider

Gemini Omni Flash (Preview) is a multimodal model designed for video, image, and text tasks. It is optimized for video generation, offering video output alongside text responses in a single model

Gemini Omni 1.1 Flash (Preview) is a multimodal model designed for video, image, and text tasks. It is optimized for video generation, offering video output alongside text responses in a single model.

Gemini Embedding 2 is Google's first multimodal embedding model. We currently support mapping text and images into a unified vector space for semantic search and retrieval-augmented generation (RAG). It supports input context up to 8,192 tokens and flexible output dimensions from 128 to 3,072 (recommended: 768, 1536, or 3,072). Designed for cross-modal similarity — you can embed a text query and retrieve the most relevant images, or vice versa — making it well-suited for multimodal search, recommendation, and document understanding pipelines.

Claude Opus 4.1 is an updated version of Anthropic’s flagship model, offering improved performance in coding, reasoning, and agentic tasks. It achieves 74.5% on SWE-bench Verified and shows notable gains in multi-file code refactoring, debugging precision, and detail-oriented reasoning. The model supports extended thinking up to 64K tokens and is optimized for tasks involving research, data analysis, and tool-assisted reasoning.

Opus 4.7 is the next generation of Anthropic's Opus family, built for long-running, asynchronous agents. Building on the coding and agentic strengths of Opus 4.6, it delivers stronger performance on complex, multi-step tasks and more reliable agentic execution across extended workflows. It is especially effective for asynchronous agent pipelines where tasks unfold over time - large codebases, multi-stage debugging, and end-to-end project orchestration.

Beyond coding, Opus 4.7 brings improved knowledge work capabilities - from drafting documents and building presentations to analyzing data. It maintains coherence across very long outputs and extended sessions, making it a strong default for tasks that require persistence, judgment, and follow-through.

Claude Opus 5 is Anthropic’s flagship Opus-series model for advanced reasoning, coding, and high-value knowledge work, delivering frontier-level performance close to Claude Fable 5 at roughly half the cost. It supports text, image, and file inputs with text output, offers a 1M-token context window, and is well suited for complex software engineering, scientific research, and long-horizon agentic workflows.

Claude Fable 5.1 is Anthropic’s latest model for demanding reasoning and long-horizon agentic work, with stronger performance in long-running coding, multistep research, and document, spreadsheet, and slide workflows. It supports text and image inputs, text output, a 1M-token context window, and adaptive thinking that is always on with effort-based steering.

Claude Sonnet 5 is built to be the most agentic Sonnet model yet. It can plan, use tools such as browsers and terminals, and operate autonomously at a level that only recently required larger and more expensive models.

Sonnet 4.6 is Anthropic's most capable Sonnet-class model yet, with frontier performance across coding, agents, and professional work. It excels at iterative development, complex codebase navigation, end-to-end project management with memory, polished document creation, and confident computer use for web QA and workflow automation.

Opus 4.6 is Anthropic’s strongest model for coding and long-running professional tasks. It is built for agents that operate across entire workflows rather than single prompts, making it especially effective for large codebases, complex refactors, and multi-step debugging that unfolds over time. The model shows deeper contextual understanding, stronger problem decomposition, and greater reliability on hard engineering tasks than prior generations.

Beyond coding, Opus 4.6 excels at sustained knowledge work. It produces near-production-ready documents, plans, and analyses in a single pass, and maintains coherence across very long outputs and extended sessions. This makes it a strong default for tasks that require persistence, judgment, and follow-through, such as technical design, migration planning, and end-to-end project execution.

For users upgrading from earlier Opus versions, see our official migration guide here

Claude Opus 4.5 is Anthropic's latest frontier reasoning model, purpose-built for complex software engineering, agentic workflows, and long-horizon computer use. It delivers strong multimodal capabilities, competitive performance on real-world coding and reasoning benchmarks, and improved robustness against prompt injection attacks. The model is designed to operate efficiently across varied effort levels, allowing developers to balance speed, depth, and token usage based on their specific task requirements—you can fine-tune token efficiency through the OpenRouter Verbosity parameter, which offers low, medium, and high settings. Beyond that, Opus 4.5 supports advanced tool use, extended context management, and coordinated multi-agent setups, making it ideal for autonomous research, debugging, multi-step planning, and spreadsheet or browser manipulation. Compared to previous Opus generations, it brings substantial improvements in structured reasoning, execution reliability, and alignment, while reducing token overhead and delivering more consistent performance on long-running tasks.

Claude Opus 5.5 is Anthropic's flagship model for demanding reasoning, coding, and long-horizon agentic work, succeeding Claude Opus 5. It is particularly strong at multi-step changes in large codebases, code review and bug finding, financial and scientific analysis, and reading dense charts, diagrams, and screenshots, and it is more careful than its predecessor about only stating figures and citing sources it can back up.

The model completes comparable tasks in fewer steps and with fewer tokens than Opus 5, and reports on its work in plainer language, with clear updates on what it did, what it found, and what it needs from the user. Thinking is always adaptive, so effort is the main lever for trading off depth, latency, and cost, and lower effort settings remain effective for latency-sensitive workloads.

Claude Sonnet 5.5 is Anthropic’s upgraded Sonnet-class model, delivering stronger performance than Claude Sonnet 5 while running over 30% faster and at lower cost for many workloads. It is designed for agentic coding, tool use, and everyday professional tasks, making it well suited for developers and teams building fast, capable AI agents.

Claude Haiku 4.5 is Anthropic’s fastest and most efficient model, delivering near-frontier intelligence at a fraction of the cost and latency of larger Claude models. Matching Claude Sonnet 4’s performance across reasoning, coding, and computer-use tasks, Haiku 4.5 brings frontier-level capability to real-time and high-volume applications.

It introduces extended thinking to the Haiku line; enabling controllable reasoning depth, summarized or interleaved thought output, and tool-assisted workflows with full support for coding, bash, web search, and computer-use tools. Scoring >73% on SWE-bench Verified, Haiku 4.5 ranks among the world’s best coding models while maintaining exceptional responsiveness for sub-agents, parallelized execution, and scaled deployment.

Claude Fable 5 is Anthropic's Mythos-class model, designed for autonomous knowledge work and coding. It accepts text, image, and file inputs, produces text output, and features reasoning capabilities along with a 1M-token context window. The model excels at long-running, complex, and asynchronous tasks that once demanded frequent human oversight.

Its core strength lies in handling end-to-end work that would typically take a person hours, days, or even weeks — tackling problems that are extended, ambiguous, or involve many interdependent steps. It carries out well-defined tasks with minimal errors, self-corrects through built-in verification loops, and comes with robust safeguards.

Claude Sonnet 4.5 is Anthropic’s most advanced Sonnet model to date, optimized for real-world agents and coding workflows. It delivers state-of-the-art performance on coding benchmarks such as SWE-bench Verified, with improvements across system design, code security, and specification adherence. The model is designed for extended autonomous operation, maintaining task continuity across sessions and providing fact-based progress tracking.

Sonnet 4.5 also introduces stronger agentic capabilities, including improved tool orchestration, speculative parallel execution, and more efficient context and memory management. With enhanced context tracking and awareness of token usage across tool calls, it is particularly well-suited for multi-context and long-running workflows. Use cases span software engineering, cybersecurity, financial analysis, research agents, and other domains requiring sustained reasoning and tool use.

Claude Haiku 5.5 is Anthropic’s cheapest, fastest, and most capable small model to date, built for high-volume, cost-sensitive workloads. It handles summaries, compaction, database queries, classification, and subagent coding tasks with low latency, making it especially effective for live customer support, browser use, and other speed-sensitive applications.

Claude Opus 4.8 is Anthropic's most capable generally available model in the Opus family. It supports text, image, and file inputs with text output, with reasoning support and a 1M-token context window. It is suited for highly autonomous agents, long-horizon agentic work, knowledge work, and memory-driven tasks where coherence over extended sessions matters.

It is particularly strong on multi-step reasoning, complex coding, and end-to-end project orchestration - large codebases, multi-stage debugging, and long-running asynchronous agent pipelines. Beyond coding, it handles knowledge work such as drafting documents, building presentations, and analyzing data, maintaining quality across very long outputs.

Gemini 3.5 Flash is engineered specifically to power high-speed, cost-efficient, and complex developer and agentic workflows. It brings powerful multi-step reasoning capabilities to a highly optimized, low-latency framework, making it highly effective for running massive reasoning loops at scale.

Nano Banana 2 Lite (Gemini 3.1 Flash Lite Image) is Google's fastest, most cost-efficient Gemini image model, built for high-velocity developer pipelines and rapid-fire visual exploration. It delivers text-to-image generation in roughly 4 seconds — about 2.7× faster than Gemini 3.1 Flash Image — while keeping the character consistency, precise editing, and real-world knowledge of the Nano Banana family.

A single drop-in API handles text-to-image, image editing, and multi-image composition. As a multimodal model it also returns text alongside images. Outputs are generated at 1K resolution across 14 aspect ratios and carry an invisible SynthID watermark so they can be identified as AI-generated.

Positioned as the best balance of quality and speed in the Nano Banana 2 line, it lets you generate thousands of images at a fraction of the cost of heavier production models — ideal for prototyping, real-time apps, and visual workflows at scale.

Gemini 3.8 Flash is Google’s most intelligent workhorse model yet, delivering major gains over 3.7 Flash in software engineering, agentic workflows, and multi-step reasoning for specialized domains. It approaches the quality of higher-cost frontier models while still offering configurable effort levels for balancing performance and token efficiency.

Gemini 3.1 Flash-Lite is Google's most cost-efficient Gemini model, optimized for low latency use cases for high-volume, cost-sensitive LLM traffic. It provides a significant quality increase over Gemini 2.0/2.5 Flash-Lite models, matching Gemini 2.5 Flash performance across key capability areas.

Gemini 3 Flash Preview is a low-latency model in the Gemini 3 family, optimized for fast, high-throughput inference. It retains the core multimodal and reasoning capabilities of Gemini 3 while prioritizing responsiveness and execution efficiency. Built on the same architecture as Gemini 3 Pro, Gemini 3 Flash Preview supports native multimodal inputs—including text, images, and audio—and incorporates the improved reasoning and long-context handling introduced in the Gemini 3 generation. It is designed for real-time and scalable workloads where latency and cost efficiency are primary considerations.

Gemini 4 Argon is a frontier model from Google DeepMind, released on September 30, 2026. It is the first model in the Gemini 4 series and uses a new naming scheme. It is strong in coding, financial research, legal drafting, and autonomously patching cybersecurity vulnerabilities. It accepts text, image, video, and file inputs and has a 1-million-token context window. On benchmarks, it scores 77.9% on DeepSWE v1.1, ahead of Claude Opus 5.5 and GPT-6 Astra, and ranks first on the Vals Index with 68.90%.

Gemini 2.5 Flash Image, a.k.a. "Nano Banana," is now generally available. It is a state of the art image generation model with contextual understanding. It is capable of image generation, edits, and multi-turn conversations.

Gemini Nano Banana 2.1 is an upgraded multimodal reasoning image model for production-grade image generation and editing, with stronger prompt adherence, multi-reference editing, and improved visual fidelity. It understands text, images, video, and PDF inputs, supports interleaved text and image generation, and can reason over complex spatial and compositional instructions before generating images. The model also supports grounding with Google Search and Google Image Search for better long-tail entity recognition, visual understanding, and output accuracy.

Gemini 3.8 Flash TTS is a text-to-speech model from Google and the successor to Gemini 3.1 Flash TTS Preview. It is the creative tier of the 3.8 TTS family, suited for narration, character work, and multi-speaker dialogue where voice fidelity, acting nuance, and regional dialect coverage matter more than latency.

Gemini 3.8 Flash Lite TTS is a text-to-speech model from Google and the fast, high-throughput member of the 3.8 TTS family alongside Gemini 3.8 Flash TTS. It is suited for conversational voice-agent pipelines, high-volume read-aloud, and voice replication workloads where latency and cost matter more than maximum expressiveness.

Nano Banana Pro is an AI image generation model that is a significant upgrade from its predecessor, built on Google's Gemini 3 Pro. It promises to move beyond simple pattern matching to a more reasoning-driven system with improved physics understanding, text rendering, and image consistency. Key features include faster processing, native 2K resolution, and the ability to edit existing images with greater control, aiming to produce more reliable and professional-grade results.

Gemini 2.5 Flash-Lite is a lightweight reasoning model in the Gemini 2.5 family, optimized for ultra-low latency and cost efficiency. It offers improved throughput, faster token generation, and better performance across common benchmarks compared to earlier Flash models. By default, "thinking" (i.e. multi-pass reasoning) is disabled to prioritize speed, but developers can enable it via the Reasoning API parameter to selectively trade off cost for intelligence.

Gemini 2.5 Flash is Google's state-of-the-art workhorse model, specifically designed for advanced reasoning, coding, mathematics, and scientific tasks. It includes built-in "thinking" capabilities, enabling it to provide responses with greater accuracy and nuanced context handling.

Additionally, Gemini 2.5 Flash is configurable through the "max tokens for reasoning" parameter, as described in the documentation.

Designed for speed and efficiency, the Gemini 3.1 Flash Image generation model is effective for quick, interactive responses and high throughput.

Gemini 3.1 Flash TTS Preview is a text-to-speech model from Google, and a substantial generational step up from Gemini 2.5 Flash TTS. It takes text input and produces audio output across 70+ languages — nearly 3× the language coverage of its predecessor.

Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy and nuanced context handling. Gemini 2.5 Pro achieves top-tier performance on multiple benchmarks, including first-place positioning on the LMArena leaderboard, reflecting superior human-preference alignment and complex problem-solving abilities.

Gemini 3.6 Flash is a high-efficiency model from Google for coding, agentic workflows, and web and app development. It is designed to produce polished outputs with fewer unnecessary edits and less hedging, while reducing token use and the number of model calls needed to complete a task.

Gemini 3.5 Flash-Lite is a high-efficiency model from Google with upgraded agentic capabilities. It is suited for subagents that execute focused tasks within complex, multi-agent workflows.

Gemini 3.1 Pro is the next generation in the Gemini series of models, a suite of highly-capable, natively multimodal, reasoning models. Gemini 3 Pro is now Google’s most advanced model for complex tasks, and can comprehend vast datasets, challenging problems from different information sources, including text, audio, images, video, and entire code repositories

Gemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind. Despite 25.2B total parameters, only 3.8B activate per token during inference — delivering near-31B quality at a fraction of the compute cost. Supports multimodal input including text, images, and video (up to 60s at 1fps). Features a 256K token context window, native function calling, configurable thinking/reasoning mode, and structured output support. Released under Apache 2.0.

Gemini 3.7 Flash is Google’s efficient multimodal model designed for coding and agentic workflows. It improves code generation, web application development, tool use, and reliability in complex multi-step tasks while supporting text, image, audio, and video inputs. With a 1-million-token context window, up to 64,000 tokens of output, and adjustable reasoning levels, it is well suited for coding agents, enterprise automation, complex document analysis, and large-scale multimodal applications.

Veo 3.1 Lite is Google DeepMind's most cost-efficient AI video generation model, released on March 31, 2026. It is designed to provide professional-grade video capabilities at a significantly lower price point, making it ideal for developers and content teams who need to scale high-volume video production

Veo 3.1 is Google's state-of-the-art model for generating high-fidelity, 8-second 720p, 1080p or 4k videos featuring stunning realism and natively generated audio. You can access this model programmatically using the Gemini API. To learn more about the available Veo model variants, see the Model Versions section.

Veo 3.1 Fast is a speed-optimized variant of Google DeepMind's flagship video generation model. It is designed to generate high-quality video significantly faster and at a lower cost than the standard Veo 3.1 Quality model, making it ideal for rapid prototyping and high-volume content creation.