Unisound's flagship U2 model with high intelligence per token and native agent capabilities. Compact yet capable, lower inference cost; autonomously plans, executes, and refines tasks end to end.

Industry model for medical insurance. Parses records, reports, policies, and coverage docs with plain-language summaries, Q&A, and deep analysis.

Human-like TTS with style, emotion, and multi-language/dialect support. Async long-text synthesis and flexible audio output for production scale.

Clone voices from minimal samples with natural, emotional speech. Persistent cloned voices for reusable brand assets.

Universal ASR with context understanding and custom vocabulary. Multi-dialect, bilingual, with one-shot and real-time transcription.

Intelligent document parsing beyond OCR—classification, layout restoration, and key info extraction. Converts unstructured docs to structured data.

Medical document intelligence for records, reports, prescriptions, and bills. Handles handwriting, abbreviations, stamps, and complex layouts.

Efficient MoE (284B total, 13B active) with million-token context. Fast, low-latency, cost-effective for chat, content, RAG, and batch copywriting.

Flagship MoE (1.6T total, 49B active) with million-token context. Top math, reasoning, code, and long-text analysis for research and advanced agents.

Multimodal agent model unifying vision-language understanding, instant/thinking modes, and chat/agent paradigms.

Stronger long-horizon coding with better instruction following and self-correction. Text, image, and video input; thinking and non-thinking modes.

Zhipu flagship for long-horizon tasks with 1M lossless context. Full-stack engineering from design to deployment for complex projects.

Long-horizon model (744B params, 200K context, 128K output). Strong reasoning, long-text, and code generation for interactive and enterprise use.

Next-gen model for coding and agents—open-source SOTA in complex engineering and long tasks, nearing Claude Opus in real coding.

Production-grade agent model built for real-world productivity—faster, stronger, and more cost-effective.

High-value Plus model with upgraded vision-language, coding, tools, and agent workflows. Scene perception, GUI ops, and visual-reference code gen.

Largest Max model with broad, deep agent skills—coding, productivity, long autonomous runs, plus vision understanding.

Native vision-language Plus rivaling frontier models. Major gains over 3.5 in agentic coding, frontend, OCR, and object localization.

Fast vision-language model with major gains over 3.5-Flash. Stronger agentic coding, math, code reasoning, and spatial detection.

35B-A3B vision-language MoE with linear attention and sparse experts. Better efficiency; gains in agentic coding, reasoning, and spatial detection.