# LBS-27B vs Qwen3.5-397B-A17B > Qwen3.5-397B-A17B scores 64.6 (B) 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-qwen3-5-397b-a17b - Markdown: https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-397b-a17b.md (~6,500 tokens) - Slim: https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-397b-a17b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-397b-a17b.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 Qwen3.5-397B-A17B scores 64.6 (B) on agent readiness against LBS-27B's 53.9 (D), and leads in 2 of 4 scored categories. 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 - Qwen3.5-397B-A17B: grade B, 64.6/100, rank #19 of 113 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-397b-a17b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-397b-a17b.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. Watch for: No independent benchmark result yet; ScreenSpot-Pro, OS-World-G, PerceptionBench and HR-Bench numbers are inclusionAI's own runs ### Qwen3.5-397B-A17B (B) Good for: Agents with access to an 8-GPU server, or calling a hosted endpoint, that want the strongest open Qwen3.5 with vision and a plain licence. Ahead on: - Reach, 70 against 55 - Risk, 55 against 50 Watch for: No build fits a 256 GB machine, and Ollama has no local build ## Score by category | Category | Weight | LBS-27B | Qwen3.5-397B-A17B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 55 | 70 | Qwen3.5-397B-A17B +15 | | Risk | 20% (20 this run) | 50 | 55 | Qwen3.5-397B-A17B +5 | | Agent interface | 10% (10 this run) | 70 | 70 | even | | Documentation and upkeep | 10% (10 this run) | 43 | 39 | LBS-27B +4 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **53.9 · D** | **64.6 · B** | | ## Facts side by side | Fact | LBS-27B | Qwen3.5-397B-A17B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | inclusionAI (Ant Group) | 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 (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) | Apache-2.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-10-26 | 2026-02-16 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | 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 | 2.8/5 (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. **Qwen3.5-397B-A17B.** Alibaba's biggest open Qwen3.5, a cousin for hard coding, agent and vision work. Even at 4 bits it needs about 270 GB, so most agents will call it through Alibaba's Model Studio or one of the many providers that host it. The main risk is silence, with no safety results, no agent guidance and template reports without a reply. ## 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 ### Qwen3.5-397B-A17B 1. The card's server command is `vllm serve Qwen/Qwen3.5-397B-A17B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, and the FP8 build halves the memory 2. It doesn't fit our 128 GB reference machine at any precision, so call a hosted endpoint instead (Model Studio's Qwen3.5-Plus, or a provider serving Qwen/Qwen3.5-397B-A17B) 3. On Model Studio pass `"enable_thinking": false` directly, while vLLM and SGLang take `"chat_template_kwargs": {"enable_thinking": false}` 4. Sampling per the card is temperature 0.6, top_p 0.95 and top_k 20 when thinking, and temperature 0.7, top_p 0.8, top_k 20 and presence_penalty 1.5 without 5. For multi-token prediction in vLLM add `--speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":2}'` 6. Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens 7. Validate any JSON it returns, since structured output isn't documented ## Questions ### Which is better for AI agents, LBS-27B or Qwen3.5-397B-A17B? Qwen3.5-397B-A17B scores 64.6 (B) on agent readiness against LBS-27B's 53.9 (D), and leads in 2 of 4 scored categories. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-397b-a17b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-397b-a17b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "lbs-27b", "b": "qwen3-5-397b-a17b"}`. 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How: https://www.anchorterminal.com/agents/#mcp