# Qwen-AgentWorld-35B-A3B vs Qwen3.5-0.8B > Qwen-AgentWorld-35B-A3B scores 52.1 (D) to Qwen3.5-0.8B's 46.7 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-0-8b - Markdown: https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-0-8b.md (~9,200 tokens) - Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt) - API: https://www.anchorterminal.com/api/v1/index.json - MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key) - DNS: dig +short TXT .grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.. and .vs.. under the zone, and the same under grade.letme.dev) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-11-10 Qwen-AgentWorld-35B-A3B scores 52.1 (D) on agent readiness against Qwen3.5-0.8B's 46.7 (D), and leads in 1 of 4 scored categories. Qwen3.5-0.8B leads on reach and agent interface. Both do open-weight models you run yourself. - Qwen-AgentWorld-35B-A3B: grade D, 52.1/100, rank #141 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen-agentworld-35b-a3b.json - Qwen3.5-0.8B: grade D, 46.7/100, rank #156 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-0-8b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-0-8b.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### Qwen-AgentWorld-35B-A3B (D) Good for: Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine Watch for: Not a general assistant, and the card doesn't describe using it to call tools ### Qwen3.5-0.8B (D) Good for: Builders who want a near-free local helper or fine-tuning base for narrow tasks on a phone-class device, laptop or CPU Ahead on: - Reach, 80 against 55 - Agent interface, 60 against 50 Watch for: No quantised build from Alibaba and no inference provider found ## Why the edge ### Reach: Qwen3.5-0.8B +25 - Qwen-AgentWorld-35B-A3B (55): Memory 10, Alibaba's only build is the BF16 safetensors, 69.32 GB, and 69.32 GB × 1.2 = 83.2 GB, above 64 GB and inside the reference machine's GPU share. Runtimes 20, Four released runtimes run the architecture, vLLM 0.33.0 (Qwen3_5MoeForConditionalGeneration), SGLang 0.5.23, Transformers 5.22.2 (qwen3_5_moe) and llama.cpp b12242 (its Qwen conversion code). Builds 0, Alibaba publishes the BF16 weights only. Terms 15, Apache 2.0, an OSI licence, in the metadata and as a LICENSE file, with no use policy or other terms added. Download 10, the repository isn't gated, so the files download with no account and no terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026). 10 + 20 + 0 + 15 + 10 + 0 = 55. - Qwen3.5-0.8B (80): Memory earns the full 30, since LM Studio's Q4_K_M build with its projector is 0.73 GB (0.88 GB with the fifth added) and Ollama's Q8_0 build was 1.0 GB, both far within 16 GB. Alibaba publishes no quantised 0.8B, so builds scores 0. Runtimes reach the 25 cap on llama.cpp, Ollama (through its pinned llama.cpp), vLLM, SGLang, LM Studio and Transformers. Apache 2.0 with nothing added gives terms 15 and the ungated repository download 10. Hosted too scores 0, as the Hub maps no inference provider to this size. 30+25+0+15+10+0 = 80. ### Agent interface: Qwen3.5-0.8B +10 - Qwen-AgentWorld-35B-A3B (50): Chat template 20, chat_template.jinja in the repository, the same as the template in tokenizer_config.json. Tool-calling format 10, only the template says how tools are declared and how calls come back. Trained tool calling 10, the template renders several tool_call blocks in one turn, but the card presents the model as a simulator of what tools and environments return, not as a tool caller. Structured output 0, no JSON or JSON Schema output documented for the model. Reasoning control 0, the card says thinking is on by default and names the qwen3 reasoning parser, but doesn't say how to turn thinking off or budget it. Context 10, 262,144 tokens native per the card and the text config. 20 + 10 + 10 + 0 + 0 + 10 = 50. - Qwen3.5-0.8B (60): The template ships in the repository (20) and the model was trained for tool use with several calls per turn in the template (20). Tool-calling format gets 10, because the card names the qwen3_coder parser but leaves the format to the template. Reasoning control scores 0, since the card shows how to turn thinking on but not where the reasoning comes back. 262,144 tokens is native (10). Structured output scores 0, as nothing documents JSON output for the model. 20+10+20+0+0+10 = 60. ## Score by category | Category | Weight | Qwen-AgentWorld-35B-A3B | Qwen3.5-0.8B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 55 | 80 | Qwen3.5-0.8B +25 | | Risk | 20% (20 this run) | 55 | 55 | even | | Agent interface | 10% (10 this run) | 50 | 60 | Qwen3.5-0.8B +10 | | Documentation and upkeep | 10% (10 this run) | 41 | 37 | Qwen-AgentWorld-35B-A3B +4 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **52.1 · D** | **46.7 · D** | | ## Facts side by side | Fact | Qwen-AgentWorld-35B-A3B | Qwen3.5-0.8B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Alibaba (Qwen team) | Alibaba (Qwen team) | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Apache-2.0 | Apache-2.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-06-25 | 2026-03-02 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | none | 2.8/5 (4) | ## Verdicts **Qwen-AgentWorld-35B-A3B.** A specialist, not a chat model. It plays the environment an agent acts in, across seven domains, so you can train or test an agent without the real thing. Alibaba ships BF16 only, which fits a 128 GB machine, and hasn't answered a discussion on it or changed the files since June. **Qwen3.5-0.8B.** A tiny vision-language model for prototypes, fine-tuning and narrow local steps such as routing, tagging or reading an image. It runs in about 1 GB at 4 or 8 bits in six runtimes under plain Apache 2.0. Keep thinking off, since the card warns it loops. The card has no safety results, and Qwen has answered none of its discussions. ## Before you call either ### Qwen-AgentWorld-35B-A3B 1. Use it as the environment, not the agent. Give it the domain's system prompt from Alibaba's GitHub repository, the history and the agent's action, and it returns the predicted observation 2. With vLLM pass `--language-model-only`, or startup fails on the missing vision weights 3. Use temperature 0.6, top_p 0.95 and top_k 20, and allow up to 32,768 output tokens, as the card recommends 4. Keep at least 128K of context, since the simulation depends on the history 5. Thinking is on by default and comes back through the qwen3 reasoning parser 6. A simulated observation is a prediction. Check anything that matters against the real environment ### Qwen3.5-0.8B 1. Smallest start is `ollama run qwen3.5:0.8b`, or `qwen3.5:0.8b-q8_0` at 1.0 GB 2. Full precision `vllm serve Qwen/Qwen3.5-0.8B --port 8000 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder` 3. Thinking is off by default; if you turn it on with `"chat_template_kwargs": {"enable_thinking": true}`, stream and cut off runaway output, since the card warns of thinking loops, and add `--reasoning-parser qwen3` in vLLM or SGLang to split the reasoning out 4. There's no generation_config.json, so pass the card's sampling yourself, for example `temperature=1.0, top_p=1.0, top_k=20, presence_penalty=2.0` for text without thinking 5. Use it for narrow steps or as a fine-tuning base, not as the main agent, as the card itself suggests 6. Validate any JSON it returns, since structured output isn't documented ## Questions ### Which is better for AI agents, Qwen-AgentWorld-35B-A3B or Qwen3.5-0.8B? Qwen-AgentWorld-35B-A3B scores 52.1 (D) on agent readiness against Qwen3.5-0.8B's 46.7 (D), and leads in 1 of 4 scored categories. Qwen3.5-0.8B leads on reach and agent interface. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-0-8b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-0-8b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "qwen-agentworld-35b-a3b", "b": "qwen3-5-0-8b"}`. 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[Qwen3.5-0.8B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-wevisone-vl-2b.md) - [Qwen3.5-0.8B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-wevisone-vl-4b.md) - [Qwen3.5-0.8B vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-zaya1-8b.md) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). How: https://www.anchorterminal.com/agents/#mcp