Models

Advanced Gemini model for complex reasoning, coding, and multimodal analysis

Reasoning-first Gemini preview for agentic coding and complex problem solving

Qwen vision-language model for visual reasoning, documents, and agent tasks

Cost-efficient ByteDance Seed 2.0 model for production chat, analysis, and structured generation

Lightweight ByteDance Seed 2.0 model for low-latency multimodal reasoning and high-volume tasks

ByteDance Seed coding model for multimodal software engineering and long-running agents

Flagship ByteDance Seed 2.0 model for complex multimodal reasoning and long-horizon agent workflows

ByteDance unified multimodal audio-video model supporting text, image, video and audio references, editing, extension and synchronized stereo sound.

Fast Grok model for responsive chat, reasoning, and tool-assisted work

Fast Grok model for responsive chat, reasoning, and tool-assisted work

New Gemini flash lane bringing frontier-style multimodal reasoning to cheaper runs

Multimodal reasoning model for visual analysis, planning, and tool use

Multimodal reasoning model for visual analysis, planning, and tool use

Preview Gemini flagship for complex reasoning, coding, and rich multimodal prompts

Video model for prompt-guided generation, editing, and motion workflows

Video model for prompt-guided generation, editing, and motion workflows

ByteDance Seed model for long-context reasoning, instruction following, and tool-assisted tasks

September 2025 preview of Gemini 2.5 Flash-Lite with better instruction following and multimodal quality

Low-latency ByteDance Seed model for high-throughput chat, extraction, and lightweight tool use

Lean Gemini 2.5 lane for cheap multimodal traffic and quick agents

Fast Gemini workhorse for multimodal apps where latency and price matter

Google's proven reasoning model for coding, math, and multimodal analysis

First preview of Gemini 2.5 Flash-Lite, the low-cost, low-latency 2.5 model

Preview snapshot of Gemini 2.5 Flash with controllable thinking budgets

Multimodal model for complex analysis, long-context understanding, tool use, and model distillation

Qwen omni model for text, vision, audio, and multimodal agent tasks

Earlier Gemini Flash workhorse for responsive multimodal apps and tool use

Low-latency Gemini model for high-volume multimodal and agent workloads

Efficient model for low-latency assistance, extraction, and routine automation

Flagship model for demanding analysis, coding, and production agent workflows

Most capable Vision model for complex reasoning, detailed media analysis, and structured output over a 1M-token context window.

Balanced multimodal model pairing a 1M-token context window with deeper reasoning for analysis, content creation, and tool use across text, image, audio, video, and PDF inputs.

Fast, low-cost multimodal model for understanding text, images, audio, video, and PDFs, with tool calling and a 1M-token context window.

MiniMax-M3 is a multimodal foundation model from MiniMax. It supports text, image, and video inputs with text output, a 1M-token context window, and is suited for long-horizon agentic work, coding,...

NVIDIA Nemotron™ 3 Nano Omni is a 30B-A3B open multimodal model designed to function as a perception and context sub-agent in enterprise agent systems. It accepts text, image, video, and...

This model always redirects to the latest model in the Gemini Pro family.

This model always redirects to the latest model in the Kimi family.

This model always redirects to the latest model in the Gemini Flash family.

Qwen3.5 Plus (April 2026) is a large-scale multimodal language model from Alibaba. It accepts text, image, and video input and produces text output, with a 1M token context window. This...

Qwen3.6 Flash is a fast, efficient language model from Alibaba's Qwen 3.6 series. It supports text, image, and video input with a 1M token context window. Tiered pricing kicks in...

Qwen3.6-35B-A3B is an open-weight multimodal model from Alibaba Cloud with 35 billion total parameters and 3 billion active parameters per token. It uses a hybrid sparse mixture-of-experts architecture combining Gated...

Qwen3.6 27B is a dense 27-billion-parameter language model from the Qwen Team at Alibaba, released in April 2026. It features hybrid multimodal capabilities — accepting text, image, and video inputs...

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...

Gemma 4 31B Instruct is Google DeepMind's 30.7B dense multimodal model supporting text and image input with text output. Features a 256K token context window, configurable thinking/reasoning mode, native function...

Qwen 3.6 Plus builds on a hybrid architecture that combines efficient linear attention with sparse mixture-of-experts routing, enabling strong scalability and high-performance inference. Compared to the 3.5 series, it delivers...

GLM-5V-Turbo is Z.ai’s first native multimodal agent foundation model, built for vision-based coding and agent-driven tasks. It natively handles image, video, and text inputs, excels at long-horizon planning, complex coding,...

Qwen3.5-9B is a multimodal foundation model from the Qwen3.5 family, designed to deliver strong reasoning, coding, and visual understanding in an efficient 9B-parameter architecture. It uses a unified vision-language design...

The Qwen3.5 Series 35B-A3B is a native vision-language model designed with a hybrid architecture that integrates linear attention mechanisms and a sparse mixture-of-experts model, achieving higher inference efficiency. Its overall...

The Qwen3.5 27B native vision-language Dense model incorporates a linear attention mechanism, delivering fast response times while balancing inference speed and performance. Its overall capabilities are comparable to those of...

The Qwen3.5 122B-A10B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. In terms of...