# Laguna XS 2.1 vs LBS-27B > Laguna XS 2.1 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/laguna-xs-2-1-vs-lbs-27b - Markdown: https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lbs-27b.md (~6,350 tokens) - Slim: https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lbs-27b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lbs-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 Laguna XS 2.1 scores 61 (C) on agent readiness against LBS-27B's 53.9 (D), and leads in every scored category. Both do open-weight models you run yourself. - Laguna XS 2.1: grade C, 61/100, rank #42 of 113 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-xs-2-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-xs-2-1.json - 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 - 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 ### Laguna XS 2.1 (C) Good for: Agents that want a fast local coding model on a 32 GB GPU or Mac, with tool calling and a permissive licence. Ahead on: - Reach, 85 against 55 - Risk, 55 against 50 - Agent interface, 85 against 70 Watch for: No reply from Poolside to any discussion, including reports about the tokenizer and remote code ### 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 ## Score by category | Category | Weight | Laguna XS 2.1 | LBS-27B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 85 | 55 | Laguna XS 2.1 +30 | | Risk | 20% (20 this run) | 55 | 50 | Laguna XS 2.1 +5 | | Agent interface | 10% (10 this run) | 85 | 70 | Laguna XS 2.1 +15 | | Documentation and upkeep | 10% (10 this run) | 46 | 43 | Laguna XS 2.1 +3 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **53.9 · D** | | ## Facts side by side | Fact | Laguna XS 2.1 | LBS-27B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Poolside | inclusionAI (Ant Group) | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | OpenMDW-1.1 | 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) | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-08-19 | 2026-10-26 | | Terms last updated | | 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 | 2.8/5 (4) | none | ## Verdicts **Laguna XS 2.1.** A small coding and agent cousin with 3B active parameters, so it's quick on modest hardware. Poolside's 4-bit GGUF, also Ollama's default, needs about 24 GB, and every major runtime runs it. The main risks are that nobody at Poolside answers its discussions, there are no safety results, and the SGLang route runs repository code. **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. ## Before you call either ### Laguna XS 2.1 1. Quickest start is `ollama run laguna-xs-2.1`, 20 GB of files, the same Q4_K_M as Poolside's GGUF 2. On our reference machine use `llama-server -hf poolside/Laguna-XS-2.1-GGUF:Q4_K_M --jinja -c 131072`, from a llama.cpp release with the laguna architecture (b11772 has it), or BF16 with vLLM (66.9 GB) 3. With vLLM use `--tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on a build with vllm-project/vllm#47311, or `--tool-call-parser glm47` on older builds 4. Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{"enable_thinking": true}'`, which Poolside recommends for agentic coding 5. Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped 6. Sampling per the card is temperature 1.0 and top_k 20 7. The card warns that Ollama on Apple Silicon may return empty output until llama.cpp#25389 ships 8. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented ### 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 ## Questions ### Which is better for AI agents, Laguna XS 2.1 or LBS-27B? Laguna XS 2.1 scores 61 (C) on agent readiness against LBS-27B's 53.9 (D), and leads in every scored category. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lbs-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lbs-27b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "laguna-xs-2-1", "b": "lbs-27b"}`. From a terminal: `anchor compare laguna-xs-2-1 lbs-27b` - Each listing in full: https://www.anchorterminal.com/api/v1/tools/laguna-xs-2-1.json and https://www.anchorterminal.com/api/v1/tools/lbs-27b.json ## Other comparisons with Laguna XS 2.1 or LBS-27B - [Agents-A1-4B vs Laguna XS 2.1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-xs-2-1.md) - [Agents-A1-4B vs LBS-27B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lbs-27b.md) - [Apertus v1.5 8B vs Laguna XS 2.1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-xs-2-1.md) - [Apertus v1.5 8B vs LBS-27B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lbs-27b.md) - [Apodex-1.1-mini vs Laguna XS 2.1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-xs-2-1.md) - [Apodex-1.1-mini vs LBS-27B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lbs-27b.md) - [BigBang-v1 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-xs-2-1.md) - [BigBang-v1 vs LBS-27B](https://www.anchorterminal.com/compare/bigbang-v1-vs-lbs-27b.md) - 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