# Qwen-AgentWorld-35B-A3B vs ThinkingCap (Qwen3.8 27B) > ThinkingCap scores 61 (C) to Qwen-AgentWorld-35B-A3B's 52.1 (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-thinkingcap-qwen3-8-27b - Markdown: https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-thinkingcap-qwen3-8-27b.md (~10,600 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 ThinkingCap (Qwen3.8 27B) scores 61 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 3 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on risk. 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 - ThinkingCap (Qwen3.8 27B): grade C, 61/100, rank #58 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/thinkingcap-qwen3-8-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/thinkingcap-qwen3-8-27b.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 Ahead on: - Risk, 55 against 45 Watch for: Not a general assistant, and the card doesn't describe using it to call tools ### ThinkingCap (Qwen3.8 27B) (C) Good for: Small teams and individuals running Qwen3.8-27B for agent or reasoning work who want shorter thinking traces on one 24 GB GPU or Mac. Ahead on: - Reach, 75 against 55 - Agent interface, 70 against 50 - Documentation and upkeep, 53 against 41 Watch for: PolyForm Small Business licence, free commercially only below 100 people and 1,000,000 USD (2019) revenue ## Why the edge ### Reach: ThinkingCap (Qwen3.8 27B) +20 - 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. - ThinkingCap (Qwen3.8 27B) (75): Memory 25 of 30, BottleCap's IQ4_XS GGUF is 15.5 GB, 18.6 GB with a fifth added, which fits 24 GB. Runtimes 20, it keeps the base's qwen3_5 architecture, which llama.cpp, vLLM, SGLang and Transformers run in released versions, while Ollama's entry is the base's. Builds 15, FP8, NVFP4, MLX and GGUF from BottleCap. Terms 10, PolyForm Small Business is free commercially only below 100 people and 1,000,000 USD (2019) revenue. Download 5, the main repository is gated behind a click-through with an account and contact details, though the GGUF, NVFP4 and MLX builds aren't. Hosted 0, no API or inference provider serves it. ### Agent interface: ThinkingCap (Qwen3.8 27B) +20 - 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. - ThinkingCap (Qwen3.8 27B) (70): Chat template 20, the same Jinja template as Qwen3.8-27B, in the repository. Tool-calling format 10 of 25, the card names the parsers but the format lives only in the template. Trained tool calling 20, several calls a turn, and BottleCap evaluated agent tasks. Structured output 0, not documented. Reasoning control 10, reasoning_effort budgets thinking per request and the card says where the reasoning comes back. Context 10, 262,144 tokens. ### Documentation and upkeep: ThinkingCap (Qwen3.8 27B) +12 - Qwen-AgentWorld-35B-A3B (41): Model card 11, the card gives intended use (simulating agent environments), architecture, parameters and context length. Training data 5, A general description without a cut-off, the three training stages and a statement that no outputs from external API services were included. Last change 10, the last commit was on 25 June 2026, an update to the card, 138 days before this check, past 90 days and within 180. Responsiveness 0, 10 discussions that aren't pull requests, opened between 24 June and 5 July 2026. Version policy 5, A first release, with nothing replaced. File health 10, Config, both copies of the template and the card agree on the architecture and the context. 11 + 5 + 10 + 0 + 5 + 10 = 41. - ThinkingCap (Qwen3.8 27B) (53): Model card 8 of 20, intended use, limitations and parameters are there, but architecture, context, languages and a cut-off aren't. Training data 0, not described. Last change 20, BottleCap's GGUF build of the model changed its card on 7 October 2026, 27 days ago. Responsiveness 10, BottleCap answered 4 of 8 discussions on the main repository. Version policy 5, the earlier ThinkingCap stays in its own repository with an undated pointer to this one. File health 10, the readable metadata and builds agree and no broken-file report waits. ### Risk: Qwen-AgentWorld-35B-A3B +10 - Qwen-AgentWorld-35B-A3B (55): File safety 25, 21 safetensors shards and no pickle or Python files, and config.json has no auto_map. Provenance 15, files sit in the Qwen organisation, the one Alibaba's Qwen repositories on GitHub link to per the Qwen3.6-35B-A3B listing (10), and the Hub gives a SHA-256 for every large file (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, no prompt-injection guidance, instruction hierarchy, refusal statement or general cautions on the card. Licence risk 15, Apache 2.0 has no use policy, no threshold and no terms the licensor can change or revoke, and the card's licence link points at the LICENSE file in the repository. Security programme 0, no reporting route on the card. 25 + 15 + 0 + 0 + 15 + 0 = 55. - ThinkingCap (Qwen3.8 27B) (45): File safety 25, safetensors only and no code in the repository. Provenance 10 of 15, the files are in BottleCap's verified organisation, but the Hub hides the gated files' SHA-256 from the public, so checksums couldn't be confirmed. Safety evaluation 0, none published. Agent guidance 0. Licence risk 10, 5 off for the size and revenue threshold. Security programme 0, no security.txt, disclosure policy or security contact. ## Score by category | Category | Weight | Qwen-AgentWorld-35B-A3B | ThinkingCap (Qwen3.8 27B) | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 55 | 75 | ThinkingCap (Qwen3.8 27B) +20 | | Risk | 20% (20 this run) | 55 | 45 | Qwen-AgentWorld-35B-A3B +10 | | Agent interface | 10% (10 this run) | 50 | 70 | ThinkingCap (Qwen3.8 27B) +20 | | Documentation and upkeep | 10% (10 this run) | 41 | 53 | ThinkingCap (Qwen3.8 27B) +12 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **52.1 · D** | **61 · C** | | ## Facts side by side | Fact | Qwen-AgentWorld-35B-A3B | ThinkingCap (Qwen3.8 27B) | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Alibaba (Qwen team) | BottleCap AI | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Apache-2.0 | PolyForm-Small-Business-1.0.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-06-25 | 2026-10-06 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | 2026-01-21 | | Agent reviews | none | 2.5/5 (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. **ThinkingCap (Qwen3.8 27B).** A Qwen3.8-27B with shorter reasoning, for agent work where thinking tokens cost time, and BottleCap's 4-bit GGUF runs in about 19 GB. The main limits are the licence, which needs a paid licence above 100 people or 1,000,000 USD revenue, a gated main repository, and no safety results or training-data description. ## 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 ### ThinkingCap (Qwen3.8 27B) 1. Check the licence first, an organisation with 100 or more people or 1,000,000 USD (2019) revenue needs a commercial licence from BottleCap 2. For files without the gate use `llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF:Q4_K_M --jinja`, about 17.4 GB 3. On vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_xml`, and on SGLang `--tool-call-parser qwen3_coder` 4. Set `reasoning_effort` (xhigh, medium or low) in chat_template_kwargs, any other value makes the template raise an error 5. Keep the base's sampling (temperature 1.0, top_p 0.95, top_k 20), the GGUF card says greedy decoding can loop 6. The GGUFs carry an MTP head, and llama.cpp builds without MTP support for this architecture may refuse to load them with a missing blk.64 tensor ## Questions ### Which is better for AI agents, Qwen-AgentWorld-35B-A3B or ThinkingCap (Qwen3.8 27B)? ThinkingCap (Qwen3.8 27B) scores 61 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 3 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on risk. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-thinkingcap-qwen3-8-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-thinkingcap-qwen3-8-27b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "qwen-agentworld-35b-a3b", "b": "thinkingcap-qwen3-8-27b"}`. 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