Alibaba

IndustryLLM

Alibaba

IndustryLLM is Alibaba’s open-weight language model for industrial procurement and engineering-standard reasoning. It adapts Qwen3.5-35B-A3B-Base through continued pre-training on industrial documents and transactions followed by task-focused supervised fine-tuning. The 35.95B-parameter hybrid mixture-of-experts network activates approximately 3B per token. Training targets informal buyer language, ambiguous product codes, contradictory specifications, and evidence-based constraint evaluation. Apache-licensed BF16 weights are published. Its 262K architectural context is distinct from 12,288-token packed fine-tuning sequences; this record uses the supported training length conservatively. The September 25 paper predates later discovery-index coverage.

Qwen-Audio-3.1-Realtime

Alibaba

Qwen-Audio-3.1-Realtime is a proprietary language-capable speech conversation model for fluid real-time dialogue. It combines speech reasoning with duplex interaction, allowing conversational responses rather than only transcription or speech synthesis. The 3.1 Plus update supports a 262,144-token context, function calling, web search, voice cloning, and additional system voices, while retaining the 3.0 Plus integration protocol. Hosted streaming APIs provide access; no downloadable weights or checkpoint parameter count is verified.

Qwen3.8-Omni-Flash

Alibaba

Qwen3.8-Omni-Flash is a native omnimodal agent model built on the Qwen3.8-Flash-Next architecture. It accepts text, images, audio, and video in a million-token context, extending coding, GUI operation, and knowledge work to audio-visual productivity. Standard APIs return text, while the subsequently available Realtime variant supports text and speech output, multichannel audio, video aggregation, and remote MCP tools. Hosted access and companion plugins support video editing, multimedia summarization, and real-time conversations; its weights are not publicly released.

Qwen3.8-LiveTranslate-Flash

Alibaba

Qwen3.8-LiveTranslate-Flash is a language-capable real-time interpretation model that uses speech and visual context to translate conversations. It recognizes 60 source languages and supports spoken output in 29 languages, with text and audio responses exposed through the Realtime API. Visual grounding helps resolve terminology and references during meetings, classes, and livestreams. This update improves latency and enables real-time speaker diarization and transcription by default; the September 17 API changelog precedes the following launch article. Weights are not publicly downloadable.

Occamy 1.0

Alibaba

Occamy 1.0 is Alibaba Accio’s open-weight co-work model, first described in its September 4 research paper. Further training of Qwen3.6-35B-A3B targets long, stateful workflows using search, code, files, structured APIs, and productivity applications. Its 35B mixture-of-experts backbone activates approximately 3B parameters and retains image input. Full-parameter supervised training, preference optimization, model merging, and sustained-action optimization emphasize recovery and follow-through. Apache-licensed BF16 weights and routine deployment formats are available. The architecture supports 262K context, while supervised training uses 131K sequences; this record uses the latter conservative operating scale.

Qwen-Drive 1.0

Alibaba

Qwen-Drive1.0 is a vision-language foundation model specialized for autonomous-driving research while retaining free-form visual question answering and instruction following. Its Qwen3.5-4B language/vision backbone is paired with an external bird’s-eye-view perception head and a flow-matching Planning Expert for future trajectories. General vision-language supervision is mixed with driving data to preserve broad visual understanding. The August31 technical report precedes the public weight rollout. Apache-2.0 weights and demo data support research; SFT and reward-optimized planners are grouped as variants.

Qwen-Flash-Character

Alibaba

Qwen-Flash-Character is a hosted language-model specialization for multilingual role-playing and anthropomorphic conversation. The official model-release catalog describes improved character consistency, context-aware dialogue progression, and empathetic engagement, supporting personalized fictional personas and sustained narrative interactions. This named model identity is distinct from a role-play application or general Flash alias. Its weights, parameter count, and detailed architecture are not publicly established; access follows the provider’s hosted service documentation.

Qwen3.8-Flash-Next

Alibaba

Qwen3.8-Flash-Next introduces an efficiency-oriented architecture for multimodal reasoning and agents. It combines Gated DeltaNet with Qwen Sparse Attention, plus 51B n-gram embedding parameters held outside the accelerator and 4B multi-token-prediction parameters. The 125B backbone activates 6B parameters per token. Public weights use the Qwen Community License 1.0 and support a native 256K context with optional million-token extension. The hosted Qwen3.8-Flash service adds production features and has a separate page.

Qwen3.8-Flash

Alibaba

Qwen3.8-Flash is the hosted production service based on Qwen3.8-Flash-Next. It adds a million-token context by default and official built-in tools to the efficiency-focused multimodal architecture. Text, images, and video support code assistance, GUI agents, and long-document understanding, with OpenAI- and Anthropic-compatible APIs. The downloadable Flash-Next checkpoint is separately cataloged because its native context and weight-license profile differ from hosted service access.

Qwen3.8 27B

Alibaba

Qwen3.8-27B is the dense open-weight tier of the Qwen3.8 release, with native vision support and a hybrid-attention language backbone. It targets coding, professional work, reasoning, and visual agents at a substantially smaller deployment footprint than the Max-scale checkpoint. Reasoning effort and preserved thinking support multi-turn agent workflows. Apache-2.0 weights allow self-hosting with a native 256K context and optional extension. Its multimodal capabilities and license are specific to this dense tier, differing from the text-only 2.4T open checkpoint.

Qwen3.8 2.4T-A95B

Alibaba

Qwen3.8-2.4T-A95B is the open-weight Max-scale Qwen3.8 checkpoint, with 2.4T total parameters and 95B activated per token. It advances coding, professional work, research, and long-horizon agent behavior on the Qwen3.5 architectural foundation. Unlike the hosted Qwen3.8-Max service, these weights are text-only and require thinking mode. The native 256K context can be extended through YaRN. Public weights use the custom Qwen3.8-Max License; hosted Max vision and non-thinking features should not be attributed to this checkpoint.

Qwen-Audio-3.0-Realtime

Alibaba

Qwen-Audio-3.0-Realtime is a proprietary language-capable speech conversation model for fluid real-time dialogue. It combines speech reasoning with duplex interaction, allowing conversational responses rather than only transcription or speech synthesis. The 3.0 release offers separately named Plus and Flash serving tiers, grouped here as the audio conversation family. Hosted streaming APIs provide access; no downloadable weights or checkpoint parameter count is verified.

Qwen3.8-Max

Alibaba

Qwen3.8-Max is the hosted 2.4T-scale flagship targeting long-horizon coding and professional knowledge work. Native visual understanding supports planning, execution, and verification across documents, interfaces, and video. The production service exposes a million-token context, configurable reasoning effort, built-in tools, and non-thinking behavior. Those hosted features differ from the separately downloadable text-only Qwen3.8-2.4T-A95B checkpoint. September serving updates such as Max-0902 are grouped as dated variants.

Qwen3.7-Flash

Alibaba

Qwen3.7-Flash is the lower-cost hosted multimodal tier in the Qwen3.7 line. Official documentation confirms text/image/video input, text output, million-token context, function calling, built-in tools, and structured output. It supports high-frequency reasoning, document processing, and agent workloads through a named API model. The provider does not establish a downloadable checkpoint or parameter count for this exact service. The July 25 launch catalog establishes availability; its July 15 serving snapshot is grouped as a variant.

Qwen-AgentWorld

Alibaba

Qwen-AgentWorld is an open language world model for simulating agent environments through reasoning and next-observation prediction. The 35B-A3B checkpoint covers MCP, search, terminal, software engineering, Android, web, and operating-system interactions using language representations. Continued pretraining, supervised training, and reinforcement learning on real interaction trajectories target faithful environment simulation. The technical report appeared June 23, followed by the June 24 weight announcement. This is a distinct language-model specialization rather than a tool-execution application.

Qwen3.7-Plus

Alibaba

Qwen3.7-Plus is a hosted multimodal tier for coding, professional work, and long-horizon agents. It accepts text, images, and video, returns text, and supports a million-token context. Official API documentation lists function calling, built-in tools, structured output, and thinking controls. It is separately positioned from the earlier Qwen3.7-Max and the lower-cost Flash tier. No downloadable weights or checkpoint-level parameter count are established for this exact hosted identity.

Qwen3.5-LiveTranslate-Flash

Alibaba

Qwen3.5-LiveTranslate-Flash is a language-capable real-time interpretation model that uses speech and visual context to translate conversations. It recognizes 60 source languages and supports spoken output in 29 languages, with text and audio responses exposed through the Realtime API. Visual grounding helps resolve terminology and references during meetings, classes, and livestreams. It builds on Qwen3.5-Omni and adds voice cloning and terminology handling. Weights are not publicly downloadable.

Qwen3.7-Max

Alibaba

Qwen3.7-Max is a proprietary agent foundation model for coding, office automation, and sustained autonomous execution. Training and evaluation emphasize long-horizon tasks, cross-harness generalization, MCP integrations, and multi-step software engineering. Qwen provides the model through Alibaba Cloud Model Studio APIs. It supports million-token contexts and controllable reasoning effort; weights are not released. The later multimodal Plus and Flash tiers are separate hosted identities rather than aliases for this text-focused release. The June 10 serving update adds native visual understanding, represented as a dated variant.

Qwen3.6-Max-Preview

Alibaba

Qwen3.6-Max-Preview is a publicly accessible proprietary preview targeting stronger agentic coding, world knowledge, and instruction following than Qwen3.6-Plus. Qwen documents Qwen Studio access and a named Alibaba Cloud Model Studio endpoint, with preserved thinking recommended for agent workloads. It qualifies as a released preview even though development continued after launch. The announcement’s changing API rollout language means early preview and later hosted access should not be confused with a final GA model.

Qwen3.6-Flash

Alibaba

Qwen3.6-Flash is the efficiency-oriented hosted Qwen3.6 tier for multimodal reasoning, coding, and agent workflows. Official documentation lists a million-token context, text output, function calling, built-in tools, and structured output. It provides a lower-cost service option than Plus while accepting images and video. Its API identity should not be treated as interchangeable with a particular downloadable Qwen3.6 checkpoint, because hosted production features and context limits differ.

Qwen3.6

Alibaba

Qwen3.6 is an open-weight multimodal update focused on practical agentic coding and development stability. The first checkpoint is a 35B-A3B MoE model, followed on April 22 by a 27B dense model. Both build on the Qwen3.5 hybrid-attention architecture and support preserving reasoning history across turns. Images and video enable visually grounded coding and reasoning. Apache-2.0 weights support local deployment with a native 256K context; larger contexts require explicit extension. The proprietary hosted Plus and Flash tiers are distinct service identities.

Qwen3.6-Plus

Alibaba

Qwen3.6-Plus is a proprietary hosted multimodal model improving practical coding agents, repository-level reasoning, and visual understanding over Qwen3.5-Plus. The service supports a million-token context by default and preserves thinking across turns for multi-step tasks. Official APIs include function calling, built-in tools, and structured output. It is a distinct hosted tier from the subsequently released Apache-2.0 Qwen3.6 35B and 27B checkpoints.

Qwen3.5-Max-Preview

Alibaba

Qwen3.5-Max-Preview is a publicly accessible Qwen model preview deployed on Arena for community evaluation. The official announcement invites users to test its capabilities while final optimization continues. This page represents the actual Arena preview identity; it does not assert that a final Qwen3.5-Max checkpoint, API, or downloadable weights were released. The public announcement does not disclose complete architecture or parameter specifications.

Qwen3.5-Omni-Flash

Alibaba

Qwen3.5-Omni-Flash is a proprietary native omnimodal model for text, image, audio, and video understanding with text and speech output. The Thinker–Talker architecture combines hybrid reasoning, audio-visual comprehension, and streaming speech synthesis, targeting multimodal assistants and real-time interaction. This separately named hosted tier is available through Qwen APIs, including a Realtime interface. The family was publicly released in March 2026; the dated March 15 serving snapshot is not used as an unverified launch date. No public downloadable weights are established for this tier.

Qwen3.5-Omni-Plus

Alibaba

Qwen3.5-Omni-Plus is a proprietary native omnimodal model for text, image, audio, and video understanding with text and speech output. The Thinker–Talker architecture combines hybrid reasoning, audio-visual comprehension, and streaming speech synthesis, targeting multimodal assistants and real-time interaction. This separately named hosted tier is available through Qwen APIs, including a Realtime interface. The family was publicly released in March 2026; the dated March 15 serving snapshot is not used as an unverified launch date. No public downloadable weights are established for this tier.

Qwen3.5-Flash

Alibaba

Qwen3.5-Flash is the lower-cost hosted tier of Qwen3.5, supporting text, image, and video input with text output. A million-token context and built-in tools support long-document analysis, coding agents, and visually grounded workflows. Official documentation lists function calling and structured output alongside dated API snapshots. Its serving architecture and parameter count are not sufficiently disclosed to borrow specifications from a particular open Qwen3.5 size.

Qwen3.5-Plus

Alibaba

Qwen3.5-Plus is the hosted production tier corresponding to Qwen3.5-397B-A17B. It adds a million-token context by default, official built-in tools, and adaptive tool use to the multimodal foundation model. It supports reasoning, coding, image/video understanding, function calling, and structured output. Hosted model terms govern service access; the related Apache-2.0 checkpoint has its own open-family page and does not establish downloadable weights for every proprietary serving update.

Qwen3.5

Alibaba

Qwen3.5 is a native vision-language foundation-model family combining Gated DeltaNet and gated attention. The initial 397B-A17B MoE model was followed by 122B-A10B, 35B-A3B, and dense sizes from 27B to 0.8B. Early fusion of text and visual data supports images, video, coding, reasoning, and tool-based agents, with expanded coverage of 201 languages and dialects. Apache-2.0 weights permit local use. This page groups the open sizes; separately positioned Plus and Flash hosted tiers have different service limits and their own pages.

WebWorld

Alibaba

WebWorld is Qwen’s open language-world-model family for browser-agent training and evaluation. It predicts environment states in accessibility-tree, HTML, XML, Markdown, and natural-language formats after a sequence of actions. Models built from Qwen3-8B, 14B, and 32B are trained on more than a million real web trajectories using a curriculum that first learns dynamics and then activates causal reasoning. The February16 technical report establishes the published research identity. Apache-2.0 weights and data support simulated long-horizon interaction; this page groups all three routine sizes.

Qwen3-Coder-Next

Alibaba

Qwen3-Coder-Next is an open-weight coding-agent model built from Qwen3-Next-80B-A3B-Base. It combines hybrid attention with an 80B MoE model that activates 3B parameters, targeting efficient software engineering and local agent development. Native 256K context supports repository-level tasks, while agent-oriented training improves tool use, code execution, and recovery from failures. Apache-2.0 base and instruction-tuned weights are available. The released post-trained checkpoint uses non-thinking mode and a dedicated function-call format.

Qwen3-Max-Thinking

Alibaba

Qwen3-Max-Thinking is the separately positioned hosted reasoning model in the Qwen3-Max line. Large-scale reinforcement learning and test-time scaling target factual knowledge, complex reasoning, and agent tasks. Adaptive tool use selects search and code interpretation when needed. Qwen Chat and a named Alibaba Cloud API expose the model, including dated serving snapshots. Its weights are not publicly downloadable; model-specific limits should be taken from the hosted service rather than inferred from open Qwen3 checkpoints.

Qwen3-Max

Alibaba

Qwen3-Max is Qwen’s trillion-parameter-scale proprietary MoE language model, pretrained on 36 trillion tokens. Its initial Instruct release targets knowledge, code, instruction following, and agent workflows through Alibaba Cloud and Qwen Chat. Preview and dated serving snapshots are grouped under this identity. The subsequently released Max-Thinking reasoning specialization has a separate page. The public launch explicitly described Thinking as still under training, so its later availability should not be backdated to this release.

Qwen3-VL 235B (MoE)

Alibaba

The multimodal flagship of the Qwen 3 series. It integrates 'Interleaved-MRoPE' for superior spatial-temporal modeling and 'DeepStack' for vision-language alignment. It supports native interleaved contexts of up to 256K tokens.

Qwen3-Omni 30B (MoE)

Alibaba

A native end-to-end omni-modal model capable of processing text, image, audio, and video. It supports audio understanding of up to 40 minutes and features a 'Thinker-Talker' MoE architecture for low-latency streaming.

Qwen3-Next

Alibaba

Qwen3-Next explores a high-efficiency hybrid-attention MoE architecture using Gated DeltaNet and gated attention. The 80B model activates 3B parameters per token, with separate Instruct and Thinking checkpoints and a native 256K context. Multi-token prediction and a high-sparsity expert design improve inference efficiency. Open Apache-2.0 weights and reference serving instructions support long-context research and deployment. The non-thinking Instruct and reasoning-focused Thinking variants have different chat behavior, which callers should handle explicitly.

Qwen3-Coder

Alibaba

Qwen3-Coder is an open-weight MoE coding family for agentic software development. Its flagship has 480B total parameters and 35B activated, with native 256K context extendable through YaRN. Training targets repository-scale understanding, code editing, execution feedback, and function calling, with the Qwen Code agent providing an integration example. A smaller 30B-A3B checkpoint is grouped as a routine size variant. The instruct models are non-thinking and use a dedicated tool-call format; Apache-2.0 weights enable local serving.

Qwen3 235B (MoE)

Alibaba

Qwen3-235B-A22B is the independently positioned flagship MoE tier of the Qwen3 family, with 235B total and 22B activated parameters. Its original April checkpoint combines thinking and non-thinking behavior in one model and supports a native 32K context, extendable through YaRN. The July 2507 updates offer separately trained Instruct and Thinking checkpoints with a native 256K context. This page represents the 235B tier specifically; the Qwen3 family page covers the broader size range. Apache-2.0 weights, named checkpoints, and hosted access support agent and reasoning applications.

Qwen-3

Alibaba

Third-generation Qwen model featuring hybrid reasoning capabilities that can switch between thinking and non-thinking modes. Trained on 36 trillion tokens (double that of Qwen2.5), with support for 119 languages and dialects. Available in 6 dense models (0.6B to 32B parameters) and 2 MoE models (30B/3B active and 235B/22B active).

Qwen2.5-Omni 7B

Alibaba

An end-to-end multimodal model featuring a 'Thinker-Talker' architecture. It accepts text, image, audio, and video inputs and generates real-time streaming speech and text outputs without external ASR/TTS systems.

QwQ-32B

Alibaba

QwQ-32B is Qwen’s open-weight reasoning model trained with reinforcement learning to improve mathematical problem solving, coding, and general reasoning. It expands inference-time computation through explicit thinking and integrates tool use into reasoning. The 32.5B dense model supports a 128K context and is available under Apache-2.0. The earlier QwQ-32B-Preview checkpoint is grouped as a historical preview variant. Correct thinking prefixes, sampling, and multi-turn handling are important for stable deployment.

Qwen2.5-VL 72B

Alibaba

A state-of-the-art vision-language model capable of understanding videos over 1 hour long, performing precise object grounding, and structured output generation. It can also act as a visual agent for computer/mobile GUI operations.

QVQ-72B-Preview

Alibaba

QVQ-72B-Preview is an open-weight visual reasoning model built from Qwen2-VL-72B. It combines image understanding with extended reasoning to tackle mathematical diagrams, science questions, and other visually grounded problems. The release is an experimental preview rather than a general production multimodal service. Public weights and demonstration code support research under the Qwen License Agreement. Its long reasoning behavior and visual question-solving focus distinguish it from the standard Qwen2-VL checkpoint.

Qwen2.5-Coder 32B

Alibaba

The flagship open-weight coding model of the Qwen 2.5 series. Trained on 5.5 trillion tokens of code, it matches the coding capabilities of GPT-4o in benchmarks like HumanEval and Aider. It supports over 92 programming languages.

Qwen2.5 32B

Alibaba

A highly efficient dense model designed to balance performance and memory requirements. It is open-sourced under Apache 2.0 license and supports long-context understanding. It outperforms many larger models (e.g., Llama-3.1-70B) in specific benchmarks while fitting on consumer-grade hardware.

Qwen2.5 72B

Alibaba

The flagship dense model of the Qwen 2.5 series, pretrained on 18 trillion tokens. It features significant enhancements in knowledge, coding, and mathematics compared to Qwen 2. It supports structured data generation (JSON) and long-context understanding.

Qwen2.5-Math 72B

Alibaba

A specialized model for mathematical reasoning, supporting Chain-of-Thought (CoT) and Tool-Integrated Reasoning (TIR). It achieves state-of-the-art performance among open-weight models on math benchmarks. It supports bilingual (English/Chinese) math reasoning.

Qwen-2

Alibaba

Second-generation Qwen model (72B parameters) open-sourced under Alibaba's Qianwen License. Trained on 3T tokens; released alongside smaller variants and intended as foundation for Alibaba's enterprise AI services.