Models

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.

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

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 MiMo model for multimodal coding agents and long-context automation

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

Lean Gemini 2.5 lane for cheap multimodal traffic and quick agents

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

Fast Gemini workhorse for multimodal apps where latency and price matter

Earlier Gemini Flash workhorse for responsive multimodal apps and tool use

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