Models

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.

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.

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

Native multimodal GLM model for efficient coding and long-horizon agent tasks

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.

2.4-trillion-parameter MoE flagship for coding, professional work, multimodal understanding, and long-horizon agentic workflows

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

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

Multimodal Kimi model with 1M context and toggleable max-effort thinking for long-horizon agent work

MiniMax multimodal model for long-context coding, perception, and agent planning

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

Earlier Qwen multimodal workhorse for million-token agent and document tasks

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

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