# Qwen AgentWorld 35B A3B URL: https://interfaze.ai/models/qwenqwen-agentworld-35b-a3b [All models](https://interfaze.ai/models) Qwen AgentWorld 35B A3B by Qwen, a text-generation model with multimodal capabilities. Understand and compare multimodal features, benchmarks, and capabilities. ## Comparison | Feature | Qwen AgentWorld 35B A3B | Interfaze | | --- | --- | --- | | Input Modalities | text, image | image, text, audio, video, document | | Native OCR | No | Yes | | Long Document Processing | No | Yes | | Language Support | unknown | 162+ | | Native Speech-to-Text | No | Yes | | Native Object Detection | No | Yes | | Guardrail Controls | No | Yes | | Context Input Size | 262.1K | 1M | | Tool Calling | Yes | Tool calling supported + built in browser, code execution and web search | ### Scaling | Feature | Qwen AgentWorld 35B A3B | Interfaze | | --- | --- | --- | | Scaling | Self-hosted/Provider-hosted with quantization | Unlimited | [Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs) View model card on [Hugging Face](https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B) > \[!Note\] This repository contains the model weights and configuration files for **Qwen-AgentWorld-35B-A3B**, a native language world model trained for agentic environment simulation. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, etc. **Qwen-AgentWorld** is the first language world model to cover seven agent interaction domains within a single model. It simulates agentic environments via long chain-of-thought reasoning, predicting the next environment state given an agent's action and interaction history. Trained through a three-stage pipeline — CPT injects environment knowledge, SFT activates next-state-prediction reasoning, RL sharpens simulation fidelity — Qwen-AgentWorld is a **native world model**: environment modeling is the training objective from the CPT stage onward, not a post-hoc add-on. ## Highlights - **Seven Unified Domains.** A single model covers MCP (tool calling), Search, Terminal, SWE (software engineering), Android, Web, and OS — spanning both text and GUI interaction environments. - **Native World Model.** Environment modeling from CPT onward, not post-hoc adaptation on a general-purpose LLM. - **Generalizable, Scalable & Controllable Simulator.** Zero-shot generalization to OOD environments (e.g., OpenClaw); controllable perturbations and fictional-world construction surpass real-environment training. - **Agent Foundation Model.** LWM RL warm-up on single-turn, non-agentic trajectories transfers to multi-turn, tool-calling agentic tasks across 7 benchmarks, including 3 entirely out-of-domain. ## Model Overview - Type: Causal Language Model (Language World Model) - Base Model: [Qwen3.5-35B-A3B-Base](https://huggingface.co/Qwen/Qwen3.5-35B-A3B-Base) - Training Stage: Continual Pre-Training (CPT) → Supervised Fine-Tuning (SFT) → Reinforcement Learning (RL, GSPO) - Number of Parameters: 35B in total and 3B activated - Hidden Dimension: 2048 - Token Embedding: 248320 (Padded) - Number of Layers: 40 - Hidden Layout: 10 × (3 × (Gated DeltaNet → MoE) → 1 × (Gated Attention → MoE)) - Gated DeltaNet: - Number of Linear Attention Heads: 32 for V and 16 for QK - Head Dimension: 128 - Gated Attention: - Number of Attention Heads: 16 for Q and 2 for KV - Head Dimension: 256 - Rotary Position Embedding Dimension: 64 - Mixture Of Experts - Number of Experts: 256 - Number of Activated Experts: 8 Routed + 1 Shared - Expert Intermediate Dimension: 512 - Context Length: 262,144 tokens - Disclaimer: No outputs from external API services are included in the training pipeline. ## Performance ### AgentWorldBench (Open-Ended Evaluation) Five-dimensional rubric mean per domain, normalized to 0-100 scale. | Model | MCP | Search | Term. | SWE | Android | Web | OS | Overall | | --- | --- | --- | --- | --- | --- | --- | --- | --- | | GPT-5.4 | 70.10 | 37.26 | 53.69 | 66.29 | 60.00 | 51.80 | 68.58 | 58.25 | | Claude Opus 4.8 | 54.93 | 35.14 | 59.18 | 64.10 | 61.50 | 54.66 | 66.62 | 56.59 | | Claude Opus 4.6 | 69.90 | 29.30 | 57.51 | 64.55 | 61.74 | 51.42 | 70.20 | 57.80 | | Gemini 3.1 Pro | 59.07 | 30.21 | 52.47 | 59.07 | 61.40 | 52.83 | 66.92 | 54.57 | | Claude Sonnet 4.6 | 70.00 | 28.79 | 56.98 | 64.52 | 58.03 | 50.78 | 63.17 | 56.04 | | DeepSeek-V4-Pro | 63.27 | 27.61 | 51.26 | 59.44 | 55.17 | 50.32 | 63.70 | 52.97 | | GLM-5.1 | 67.60 | 22.46 | 47.32 | 52.07 | 59.10 | 51.50 | 59.13 | 51.31 | | Kimi K2.6 | 65.23 | 27.48 | 52.54 | 58.77 | 58.93 | 50.20 | 60.80 | 53.42 | | MiniMax-M2.7 | 55.82 | 27.30 | 41.62 | 37.44 | 52.40 | 50.52 | 57.73 | 46.12 | | Qwen3.5-35B-A3B | 57.87 | 25.98 | 46.13 | 47.58 | 53.18 | 47.10 | 56.27 | 47.73 | | Qwen3.5-397B-A17B | 68.31 | 30.81 | 55.30 | 64.44 | 54.90 | 48.55 | 60.85 | 54.74 | | Qwen3.6-Plus | 55.28 | 21.94 | 50.58 | 59.08 | 57.65 | 50.78 | 60.33 | 50.81 | | Qwen-AgentWorld-35B-A3B | 64.79 | 36.69 | 53.96 | 65.63 | 58.17 | 49.55 | 65.92 | 56.39 | | Qwen-AgentWorld-397B-A17B | 68.24 | 37.82 | 57.73 | 68.49 | 60.20 | 50.98 | 67.89 | 58.71 | ## Quickstart ### Deployment Qwen-AgentWorld-35B-A3B can be served via APIs with popular inference frameworks. In the following, we show example commands to launch OpenAI-compatible API servers. > \[!Important\] The model has a default context length of 262,144 tokens. If you encounter out-of-memory (OOM) errors, consider reducing the context window. However, because Qwen-AgentWorld leverages extended context for multi-turn environment simulation, we advise maintaining a context length of at least 128K tokens. #### SGLang [SGLang](https://github.com/sgl-project/sglang) is a fast serving framework for large language models. ``` python -m sglang.launch_server \ --model-path Qwen/Qwen-AgentWorld-35B-A3B \ --port 8000 \ --tp-size 4 \ --context-length 262144 \ --reasoning-parser qwen3 ``` An OpenAI-compatible API will be available at `http://localhost:8000/v1`. #### vLLM [vLLM](https://github.com/vllm-project/vllm) is a high-throughput and memory-efficient inference engine for LLMs. ``` vllm serve Qwen/Qwen-AgentWorld-35B-A3B \ --port 8000 \ --tensor-parallel-size 4 \ --max-model-len 262144 \ --reasoning-parser qwen3 \ --trust-remote-code ``` An OpenAI-compatible API will be available at `http://localhost:8000/v1`. ### Inference with Transformers ``` from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen-AgentWorld-35B-A3B" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto", ) messages = [ { "role": "system", "content": "You are a language world model simulating a Linux terminal environment. " "Given the user's command, predict the terminal output." }, { "role": "user", "content": "Action: execute_bash\nCommand: ls -la /home/user/project/" } ] text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer([text], return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.6) response = tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True) print(response) ``` ### Using via the Chat Completions API ``` from openai import OpenAI client = OpenAI( base_url="http://localhost:8000/v1", api_key="EMPTY", ) messages = [ { "role": "system", "content": "You are a language world model simulating a Linux terminal environment. " "Given the user's command, predict the terminal output." }, { "role": "user", "content": "Action: execute_bash\nCommand: ls -la /home/user/project/" } ] response = client.chat.completions.create( model="Qwen/Qwen-AgentWorld-35B-A3B", messages=messages, max_tokens=32768, temperature=0.6, ) print(response.choices[0].message.content) ``` > \[!Note\] We provide **domain-specific world model system prompt templates** in [`prompts/`](https://github.com/QwenLM/Qwen-AgentWorld/tree/master/prompts) of the GitHub repository for all 7 domains. These serve as general-purpose system prompts when using Qwen-AgentWorld as an environment simulator. Each domain folder contains a `system_prompt.txt` (world model system prompt) and a `judge_system_prompt.txt` (evaluation prompt). ## Evaluate on AgentWorldBench AgentWorldBench evaluates language world models by scoring each predicted environment observation on 5 dimensions: **Format**, **Factuality**, **Consistency**, **Realism**, and **Quality**. ### Setup ``` git clone https://github.com/QwenLM/Qwen-AgentWorld.git cd Qwen-AgentWorld huggingface-cli download Qwen/AgentWorldBench --repo-type dataset --local-dir ./AgentWorldBench pip install openai ``` ### Run Evaluation The evaluation follows a three-step pipeline: ``` cd eval python eval.py infer \ --data-dir ../AgentWorldBench \ --model-base-url http://localhost:8000/v1 \ --model-name Qwen/Qwen-AgentWorld-35B-A3B \ --output-dir ./results export OPENAI_API_KEY="your-api-key" python eval.py judge \ --predictions ./results/predictions.jsonl \ --judge-base-url https://api.openai.com/v1 \ --judge-model gpt-5.2-2025-12-11 \ --output-dir ./results python eval.py score --predictions ./results/judged.jsonl ``` ## Best Practices 1. **Sampling Parameters**: We recommend `temperature=0.6`, `top_p=0.95`, `top_k=20` for world model inference. The model uses thinking mode by default (`...`) to reason about environment state transitions before producing the predicted observation. 2. **Adequate Output Length**: We recommend an output length of 32,768 tokens for most queries. For long, multi-step trajectories, you may increase the max output length to accommodate detailed environment observations. 3. **Domain-Specific System Prompts**: For optimal simulation fidelity, use the domain-specific system prompts provided in the [`prompts/`](https://github.com/QwenLM/Qwen-AgentWorld/tree/master/prompts) directory of the GitHub repository. ## Citation If you find our work helpful, feel free to give us a cite. ``` @article{zuo2026qwen, title={Qwen-agentworld: language world models for general agents}, author={Zuo, Yuxin and Xiao, Zikai and Sheng, Li and Huang, Fei and Tu, Jianhong and Liu, Yuxuan and Tang, Tianyi and Hu, Xiaomeng and Su, Yang and Lan, Qingfeng and others}, journal={arXiv preprint arXiv:2606.24597}, year={2026} } ``` ## Want more deterministic results? [Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)