Models

Muse Spark Open is an 816B total / 42B active open-weight multimodal reasoning model from Meta for coding and long-horizon agentic work, with 1M-token context.

Google's flagship reasoning model for complex software engineering, deep math and science, and long-horizon autonomous agents

Muse Wonder is a frontier multimodal reasoning model from Meta for personal and professional agents, long-horizon coding, and computer use. It combines Active Memory, which carries relevant context across conversations, with Active Focus, which keeps it working through blockers toward a goal.

MiMo Flash model for multimodal coding agents and long-context automation

Stronger MiMo Pro tier for multimodal reasoning and coding-agent execution

MiMo pro model for strong multimodal reasoning and agent execution

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

Google's most intelligent Flash model, engineered for long-horizon software engineering, autonomous agents, and complex enterprise workflows

Muse Spark 1.3 is a multimodal reasoning model from Meta for long-running agentic, multi-agent, and coding workflows. It improves long-horizon agent collaboration, instruction following, and coding efficiency relative to Muse Spark 1.2.

High-efficiency Gemini model for agentic workflows, coding, and multimodal reasoning

High-efficiency Gemini model for agentic workflows, coding, and multimodal reasoning

Muse Spark 1.2 is a coding-focused update to Muse Spark 1.1 with improvements in code generation, complex debugging, codebase understanding, and end-to-end developer workflows.

Multimodal MoE reasoning model (276B total, 12B active) for text, image, and audio

Fast Gemini model balancing multimodal reasoning, tool use, and cost

Fast Gemini model balancing multimodal reasoning, tool use, and cost

Fast Gemini model balancing multimodal reasoning, tool use, and cost

Multimodal MoE reasoning model (975B total, 41B active) for text, image, and audio

Fast Gemini model balancing multimodal reasoning, tool use, and cost

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

Open Nemotron omni model combining reasoning with text, vision, and audio

Open MiMo model for multimodal coding agents and long-context automation

Vision-language model for embodied reasoning: spatial understanding, task planning, and physical-world agentic robotics

Open Gemma instruction model for efficient chat and self-hosted deployments

Open Gemma instruction model for efficient chat and self-hosted deployments

Music generation model for short 30-second clips, loops, and previews from text or image prompts

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

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

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

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

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

Multimodal reasoning model accepting text, images, video, and audio with text output

Instruct model with native audio input for speech understanding and tool use

Open audio-language model for speech transcription, audio understanding, and voice-driven tool use

Open audio-language model for speech transcription, audio understanding, and voice-driven tool use

Efficient on-device Gemma 3n model for text, image, and audio understanding

Efficient on-device Gemma 3n model for text, image, and audio understanding

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

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

Earlier Gemini Flash workhorse for responsive multimodal apps and tool use

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

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.