Anthropic's most capable widely released model, built for the most demanding reasoning and long-horizon agentic work. Offers a 1M-token context window with 128k max output and always-on adaptive thinking, excelling at autonomously exploring underspecified tasks, planning, and carrying long-running coding and multi-agent orchestration further before it needs human input.

A daily-driver Sonnet-tier model aimed at bringing near-frontier agentic, coding, and knowledge-work capability at a lower operating cost. Its default 1M-token context and adaptive thinking fit long documents, codebases, and multi-step tool workflows; for the deepest reasoning or restricted high-risk security work, evaluate higher-tier or specialized models.

A LongCat 2.0 workhorse for project-scale coding and long-running agent tasks, with native 1M-token context plus tool calling and multi-step reasoning. It is best when you need to keep large repositories, long documents, or automation workflows in scope; for lightweight Q&A or low-latency chat, a smaller Flash/Lite-style model is usually cheaper.

Built for coding, agents, and complex productivity tasks, advancing beyond Doubao Seed 2.0 in long context, long output, and tool-oriented workflows. Compared with Qwen, GLM, and DeepSeek peers, it is a cost-effective option for Chinese office work, coding, and multi-step automation.

Built for text-to-video, with a stronger audio-native workflow than HappyHorse 1.0, generating dialogue, sound effects, and background music in one pass. Compared with Veo, Kling, and Runway, its differentiator is synchronized audiovisual generation and character-consistent workflows for short drama, ads, e-commerce, and brand marketing.

Built for reference-to-video, with a stronger audio-native workflow than HappyHorse 1.0, generating dialogue, sound effects, and background music in one pass. Compared with Veo, Kling, and Runway, its differentiator is synchronized audiovisual generation and character-consistent workflows for short drama, ads, e-commerce, and brand marketing.

Built for image-to-video, with a stronger audio-native workflow than HappyHorse 1.0, generating dialogue, sound effects, and background music in one pass. Compared with Veo, Kling, and Runway, its differentiator is synchronized audiovisual generation and character-consistent workflows for short drama, ads, e-commerce, and brand marketing.

Built for video generation and multi-asset video creation, improving on Seedance 1.x with more complete motion stability, audiovisual generation, and reference inputs. Compared with Veo, Kling, and Runway, it is better suited to Chinese creative workflows driven by mixed text, image, audio, and video inputs. This filter-off variant keeps the same tier of generation capability as standard Seedance 2.0 while applying looser content filtering. Useful for short drama, ads, e-commerce, and social assets.

Built for reasoning, coding, and long-context agent tasks, improving over GLM-5.1 in context length, tool use, and complex multi-step workflows. Compared with Chinese flagship peers such as Qwen, DeepSeek, and Kimi, it fits enterprise automation, project-level code analysis, and knowledge work in Chinese.

Built for long-context, multi-step tool use, and code/document workflows, with stronger engineering-task stability and context capacity than Kimi K2.6. Compared with Qwen, GLM, and DeepSeek, it is well suited to Chinese long-document analysis, project Q&A, and everyday agentic programming.