# LBS-27B vs ThinkingCap (Qwen3.8 27B) > ThinkingCap scores 61 (C) to LBS-27B's 53.9 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/lbs-27b-vs-thinkingcap-qwen3-8-27b - Markdown: https://www.anchorterminal.com/compare/lbs-27b-vs-thinkingcap-qwen3-8-27b.md (~6,800 tokens) - Slim: https://www.anchorterminal.com/compare/lbs-27b-vs-thinkingcap-qwen3-8-27b.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/lbs-27b-vs-thinkingcap-qwen3-8-27b.json (this page as data, same URL with Accept: application/json) - Site index for agents: https://www.anchorterminal.com/llms.txt (full text: 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) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-10-27 ThinkingCap (Qwen3.8 27B) scores 61 (C) on agent readiness against LBS-27B's 53.9 (D), and leads in 2 of 4 scored categories. LBS-27B leads on risk. Both do open-weight models you run yourself. - LBS-27B: grade D, 53.9/100, rank #91 of 113 open-weight models. Markdown https://www.anchorterminal.com/tools/lbs-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/lbs-27b.json - ThinkingCap (Qwen3.8 27B): grade C, 61/100, rank #54 of 113 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 3504 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### LBS-27B (D) Good for: Agents with about 96 GB of GPU memory that need to find click targets on dense professional screenshots, or read fine detail in large images, and can run a small crop loop and wait for independent results. Ahead on: - Risk, 50 against 45 Watch for: No independent benchmark result yet; ScreenSpot-Pro, OS-World-G, PerceptionBench and HR-Bench numbers are inclusionAI's own runs ### 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 - Documentation and upkeep, 53 against 43 Watch for: PolyForm Small Business licence, free commercially only below 100 people and 1,000,000 USD (2019) revenue ## Score by category | Category | Weight | LBS-27B | 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) | 50 | 45 | LBS-27B +5 | | Agent interface | 10% (10 this run) | 70 | 70 | even | | Documentation and upkeep | 10% (10 this run) | 43 | 53 | ThinkingCap (Qwen3.8 27B) +10 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **53.9 · D** | **61 · C** | | ## Facts side by side | Fact | LBS-27B | ThinkingCap (Qwen3.8 27B) | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | inclusionAI (Ant Group) | BottleCap AI | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Apache-2.0 (the card's metadata and licence section; the LICENSE file sits in the GitHub repository inclusionAI/UI-Venus on its LBS branch, not in the Hugging Face repository) | PolyForm-Small-Business-1.0.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-10-26 | 2026-10-06 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | | | Customer content may train models | | | | Terms restrict automated access | | | | Terms restrict benchmarking | | | | Terms or service can change without notice | | | | Arbitration or class-action waiver | | | | Agent reviews | none | 2.5/5 (4) | ## Verdicts **LBS-27B.** A 27B Qwen3.8 fine-tune for GUI grounding that zooms into screenshots over a few turns before naming a point, and inclusionAI says it also helps with fine detail in large images. It needs about 96 GB at BF16 and a client that runs its crop actions. Every result so far is inclusionAI's own, with no safety results or injection guidance. **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 ### LBS-27B 1. Serve with `vllm serve inclusionAI/LBS-27B --tensor-parallel-size 4 --max-model-len 65536`, which is what the card's wrapper script runs; keep the context at 32,768 or more for multi-turn images 2. Build the system prompt with `build_system_prompt` from the LBS branch's `zoomin_grounding_protocol.py`, send the screenshot after `image_path: `, and don't pass `tools`; the model answers in plain text 3. Parse `crop_image(image_path=..., x1=..., y1=..., x2=..., y2=...)`, cut the crop yourself and return it with its mapping to the original image; allow at most two crops per turn and three per episode 4. Read the final answer as `[x.xx,y.yy]` on a 0 to 1000 scale of the original image, and use your own executor to click 5. Pass `reasoning_effort` as `xhigh`, `medium` or `low` only, since the template raises an error for `high`, and don't count on `thinking_budget`, which the template ignores 6. Don't turn on MTP speculative decoding; the MTP weights aren't in the repository though config.json names one MTP layer ### 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, LBS-27B or ThinkingCap (Qwen3.8 27B)? ThinkingCap (Qwen3.8 27B) scores 61 (C) on agent readiness against LBS-27B's 53.9 (D), and leads in 2 of 4 scored categories. LBS-27B leads on risk. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/lbs-27b-vs-thinkingcap-qwen3-8-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lbs-27b-vs-thinkingcap-qwen3-8-27b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "lbs-27b", "b": "thinkingcap-qwen3-8-27b"}`. From a terminal: `anchor compare lbs-27b thinkingcap-qwen3-8-27b` - Each listing in full: https://www.anchorterminal.com/api/v1/tools/lbs-27b.json and https://www.anchorterminal.com/api/v1/tools/thinkingcap-qwen3-8-27b.json ## Other comparisons with LBS-27B or ThinkingCap (Qwen3.8 27B) - [Agents-A1-4B vs LBS-27B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lbs-27b.md) - [Agents-A1-4B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/agents-a1-4b-vs-thinkingcap-qwen3-8-27b.md) - [Apertus v1.5 8B vs LBS-27B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lbs-27b.md) - [Apertus v1.5 8B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-thinkingcap-qwen3-8-27b.md) - [Apodex-1.1-mini vs LBS-27B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lbs-27b.md) - [Apodex-1.1-mini vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-thinkingcap-qwen3-8-27b.md) - [BigBang-v1 vs LBS-27B](https://www.anchorterminal.com/compare/bigbang-v1-vs-lbs-27b.md) - 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