Find the right AI model for your work

The Seed2.1 series is a low-cost, low-latency version designed for large-scale production scenarios, with a full feature set and performance comparable to Doubao-Seed-2.1-pro. It fully supports Coding, Agent, GUI tasks, and image and video understanding, and offers a 256K context window and a maximum output of 256K, making it suitable for reliably handling a large volume of online calls.

A new-generation flagship model moving toward production-grade intelligence, with fully upgraded Coding, Agent, and multimodal capabilities. With stronger autonomous planning, long-chain execution, and dynamic repair capabilities, it is equipped to handle real-world complex enterprise tasks. It features a 1M ultra-long context and 256K maximum output, with outstanding performance in image, video, and spatial understanding.

A new-generation omni-modal large model that natively supports unified understanding of text, vision, and audio, features a 1M ultra-long context window, and delivers excellent comprehensive multimodal capabilities. On 26/10/15, support for System One decision API capabilities was added.

A 2.4-trillion-parameter MoE flagship with fully upgraded coding and office capabilities, capable of autonomously coding for over ten days to deliver a complete project. It handles hundreds of professional tasks across law, finance, design, and more, delivering production-grade results end-to-end in a single conversation. Native visual understanding runs through the entire planning, execution, and verification workflow, supporting deep semantic analysis of ultra-long documents and long videos. In long-horizon tasks, it autonomously plans and iterates in a closed loop, continuously evolving.

Qwen3.8-Flash is Qwen’s latest multimodal large model, combining strong understanding and generation capabilities with excellent response speed. The model natively supports a million-level context window and can process ultra-long documents, code repositories, and complex conversations in one go. It performs particularly well in coding assistance, agent collaboration, image-text understanding, and other scenarios—whether automatically fixing code, operating desktop applications, or analyzing charts and long videos, it can deliver accurate, high-quality results.

GLM-5.3-Flash is the first native multimodal model in the GLM-5 series, achieving intelligence stronger than GLM-5.2 with an ultra-low-cost architecture.

GLM-5.3 is Zhipu AI's latest flagship model, with comprehensive advancements in complex software engineering and Agent tasks. It uses the same base model as GLM-5.2—all improvements come from post-training. Compared with GLM-5.2, it performs even better on complex programming and long-range tasks.

Use the experimental deepseek-v4-flash-vision-exp model to perform mixed image and text tasks, such as screenshot inspection, document extraction, chart analysis, and visual agent.

Kimi K3 is a flagship reasoning router for complex tasks, suitable for screenshot reconstruction and interactive frontend prototyping, large code repositories, multi-document evidence synthesis, long-running agents, and knowledge tasks that genuinely require a 1.05 million Token working context.

Qwen3.7-Max is the flagship model of Alibaba's Qwen3.7 series. It supports text input and output, is designed for agent-centric workloads, and offers unique advantages in coding, office and productivity tasks, and long-term autonomous execution. Compared with previous generations of Qwen, this model achieves significant improvements in coding and agent performance, and supports explicit prompt caching for efficient reuse of repeated contexts.

Qwen3.7-Plus is a cost-effective model in Alibaba's Qwen3.7 series. It supports text and image input as well as text output. Building on the series' text capabilities, it delivers a comprehensive upgrade to visual language abilities while retaining full-stack, agent-level intelligence for coding, tool use, and productivity workflows. Its standout feature is the multimodal interactive hybrid agent capability: it can perceive real-world scenes, read screens and interact with GUI, generate code from visual references, and perform end-to-end navigation in mobile applications.

GLM 5.2 is Z.ai's large-scale reasoning model. It supports text input and output with a 1M token context window, suitable for long-horizon agent workflows, project-level software engineering, and complex multi-step automation. Supports reasoning effort high and xhigh; xhigh maps to maximum reasoning. It is particularly adept at coding and tool use across long-running tasks, capable of maintaining engineering environments within a single task and consistently following standards throughout the entire development workflow (from requirements to multi-platform deployment).

GLM-5V-Turbo is Zhipu's first multimodal Agent foundation model, deeply optimized for visual programming and complex task scenarios. It supports multimodal inputs including images, videos, text, and files, with enhanced visual understanding, long-horizon planning, and action execution capabilities. Compared with general-purpose multimodal models, it is better suited for integration into Agent workflows, completing the full closed loop of “environment perception → task planning → execution,” enabling multimodal capabilities to move from “being able to understand” to “being able to act.”

A balanced model for high-frequency enterprise scenarios that balances performance and cost, with overall capabilities surpassing the previous generation Doubao-Seed-1.8. It is well-suited for production tasks such as unstructured information processing, content creation, search recommendation, and data analysis, supporting long context, multi-source information fusion, multi-step instruction execution, and high-fidelity structured output. It significantly optimizes costs while ensuring stable performance.

Flagship all-purpose general model, designed for complex reasoning and long-chain task execution scenarios in the Agent era. Emphasizes multimodal understanding, long-context reasoning, structured generation, and tool-augmented execution. Outstanding capability in following complex instructions and satisfying multiple constraints, reliably handling multi-step complex planning, complex image-text reasoning, video content understanding, and high-difficulty analysis scenarios.

DeepSeek V4 Pro is a large-scale Mixture-of-Experts model launched by DeepSeek, with 1.6T total parameters, 49B activated parameters, and a 1M-token context window. It is specifically designed for advanced reasoning, coding, and long-horizon agentic workflows, delivering strong performance on knowledge, math, and software engineering benchmarks. It adopts the same architecture as DeepSeek V4 Flash and introduces a hybrid attention system that enables efficient long-context processing, along with support for multiple reasoning modes to balance speed and depth according to the task. It is ideal for complex workloads such as full codebase analysis, multi-step automation, and large-scale information synthesis, where both capability and efficiency are critical.

DeepSeek V4 Flash is DeepSeek's efficiency-optimized mixture-of-experts model, with 284B total parameters, 13B activated parameters, and support for a 1M token context window. It is designed for fast inference and high-throughput workloads while maintaining strong reasoning and coding performance. The model includes hybrid attention for efficient long-context processing and supports configurable inference modes. It is ideal for applications such as coding assistants, chat systems, and agent workflows, where responsiveness and cost efficiency are critical.

Qwen3.6 native vision-language Flash model is built on a hybrid architecture that combines linear attention mechanism with sparse mixture-of-experts model to achieve higher inference efficiency. Compared to the 3 series, these models achieve a performance leap in pure-text and multimodal tasks, providing fast response times while balancing inference speed and overall performance.

Qwen3.6 Max Preview is built on a hybrid architecture that combines efficient linear attention with sparse mixture-of-experts routing, enabling strong scalability and high-performance inference. Compared to the 3.5 series, it brings significant advancements in agentic coding, front-end development, and overall reasoning, with a markedly improved "vibe coding" experience. The model excels at complex tasks such as 3D scenes, games, and repository-level problem solving, scoring 78.8 on SWE-bench Verified. It represents a major leap in both text-only and multimodal capabilities, reaching the level of leading state-of-the-art models.

Qwen 3.6 Plus is built on a hybrid architecture that combines efficient linear attention with sparse mixture-of-experts routing, enabling strong scalability and high-performance inference. Compared to the 3.5 series, it delivers major improvements in agentic coding, front-end development, and overall reasoning, with a significantly enhanced "vibe coding" experience. The model excels at complex tasks such as 3D scenes, games, and repository-level problem solving, scoring 78.8 on SWE-bench Verified. It represents a major leap in text-only and multimodal capabilities, reaching the level of leading state-of-the-art models.

GLM-5 is Z.ai's flagship open-source foundation model, designed for complex system design and long-horizon agentic workflows. It is built for expert developers, delivering production-grade performance in large-scale programming tasks, comparable to leading closed-source models. With advanced agentic planning, deep backend reasoning, and iterative self-correction, GLM-5 goes beyond code generation to full-system building and autonomous execution.

GLM-5 Turbo is Z.ai's new model, specifically designed for fast inference and powerful performance in agent-driven environments such as OpenClaw scenarios. It is deeply optimized for real-world agent workflows involving long execution chains, improving complex instruction decomposition, tool use, planning and persistent execution, as well as overall stability for extended tasks.

GLM-5.1 achieves a major leap in coding capabilities, particularly notable in handling long-horizon tasks. Unlike previous models built around minute-level interactions, GLM-5.1 can independently and continuously handle a single task for over 8 hours, autonomously planning, executing, and improving itself throughout the process, ultimately delivering complete engineering-level results.

The Qwen3.5 native vision-language Flash model is built on a hybrid architecture, combining linear attention mechanisms with sparse mixture-of-experts models to achieve higher inference efficiency. Compared with the 3 series, these models deliver a significant performance leap in both text-only and multimodal tasks, offering fast response times while balancing inference speed and overall performance.

Qwen3.5 native vision-language series Plus models are built on a hybrid architecture, combining linear attention mechanisms with sparse mixture-of-experts models to achieve higher inference efficiency. In evaluations across various tasks, the 3.5 series consistently demonstrates performance comparable to leading state-of-the-art models. Compared to the 3 series, these models show a leap in both pure-text and multimodal capabilities.

FLUX.2 [flex] excels at rendering complex text, typography, and fine details, and supports multi-reference editing within the same unified architecture.

FLUX.2 [max] is Black Forest Labs' new top-tier image model, elevating image quality, rapid understanding, and editing consistency to the highest level seen to date.

A high-end image generation and editing model focused on cutting-edge visual quality and reliability. It delivers strong prompt adherence, stable lighting, clear textures, and consistent character/style reproduction across multiple reference inputs. Designed for production workloads, it balances speed and quality while supporting text-to-image and image editing at up to 4 MP resolution.

Doubao-Seed-1.6 is a new multimodal deep reasoning model that simultaneously supports four reasoning effort levels: minimal/low/medium/high. Stronger model performance, serving complex tasks and challenging scenarios. Supports 256k context window, with maximum output length of 32k tokens.

Doubao-Seed-1.6-flash is a multimodal deep-thinking model with extreme inference speed, with TPOT as low as 10ms; it supports both text and visual understanding, with text understanding surpassing the previous generation lite, and visual understanding on par with competitor pro series models. Supports a 256k context window, with a maximum output length of up to 16k tokens.

Doubao-Seed-1.6 is a brand-new multimodal deep thinking model, supporting four reasoning effort levels: minimal/low/medium/high. It delivers stronger model performance, serving complex tasks and challenging scenarios. It supports a 256k context window and a maximum output length of 32k tokens.

GLM-4.5 is our latest flagship foundation model, built specifically for agent-based applications. It leverages a mixture-of-experts (MoE) architecture and supports a context length of up to 128k tokens. GLM-4.5 delivers significantly enhanced capabilities in reasoning, code generation, and agent alignment. It supports a hybrid reasoning mode with two options: a "thinking mode" designed for complex reasoning and tool use, and a "non-thinking mode" optimized for immediate responses. Users can control reasoning behavior using the reasoning-enabled boolean value.

GLM-4.6 is Zhipu's latest flagship model, with a total parameter count of 355B, active parameters of 32B, context length increased to 200K, and comprehensive improvements across 8 authoritative benchmarks. In core capabilities such as coding, reasoning, search, writing, and agent applications, it fully surpasses GLM-4.5.

The new generation 文心 model, 文心5.0, is a native omni-modal large model. It employs native omni-modal unified modeling technology to jointly model text, images, audio, and video, delivering comprehensive omni-modal capabilities. The foundational capabilities of 文心5.0 have been comprehensively upgraded, with outstanding performance on benchmark test sets, especially in multimodal understanding, instruction following, creative writing, factuality, agent planning, and tool use.

​Core Positioning: Better satisfies multi-turn long-history dialogue processing, long document understanding and Q&A tasks. ​Applicable Scenarios: 1) Complex semantic understanding: Supports Chinese knowledge Q&A, literary creation, especially skilled at document understanding (e.g., DocVQA tasks). 2) Mathematical reasoning: Outstanding performance on Chinese mathematical problems (CMath benchmark).

GLM-4.5V series is a flagship visual understanding model based on the MoE architecture. With 106B total parameters and 12B activated parameters, it is a comprehensive upgrade from GLM-4.1V-Thinking, reaching SOTA level among open-source multimodal models. Combined with the innovative RLCS reinforcement learning technology, it performs excellently on tasks such as video understanding, image question answering, OCR, and document parsing, and achieves significant improvements in complex scenarios including front-end web Coding, Grounding, and spatial reasoning. It supports flexible switching between thinking / non-thinking modes, balancing reasoning depth and efficiency.

Doubao-Seed-1.6-lite is an all-new multimodal deep thinking model that supports adjustable reasoning effort, i.e., four modes: Minimal, Low, Medium, and High. It offers greater cost-effectiveness and is the best choice for common tasks, with a context window of up to 256k.

Doubao-1.5-vision-pro is a newly upgraded multimodal large model with significantly enhanced capabilities in visual understanding, classification, information extraction, problem solving, and video understanding. On multiple public benchmark evaluations, it surpasses industry-leading models such as GPT-40, Claude 3.7 Sonnet, and Gemini-2.0-pro. It supports a 128k context window and a maximum output length of 16k tokens.