# Laguna M.1 vs LBS-27B > Laguna M.1 scores 55.5 (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-m-1-vs-lbs-27b - Markdown: https://www.anchorterminal.com/compare/laguna-m-1-vs-lbs-27b.md (~6,200 tokens) - Slim: https://www.anchorterminal.com/compare/laguna-m-1-vs-lbs-27b.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/laguna-m-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 M.1 scores 55.5 (C) on agent readiness against LBS-27B's 53.9 (D), and leads in 3 of 4 scored categories. LBS-27B leads on documentation and upkeep. Both do open-weight models you run yourself. - Laguna M.1: grade C, 55.5/100, rank #85 of 113 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-m-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-m-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 M.1 (C) Good for: Teams with a multi-GPU Blackwell or Hopper server that want a large coding agent model under Apache 2.0. Ahead on: - Reach, 65 against 55 - Risk, 55 against 50 - Agent interface, 85 against 70 Watch for: The smallest build needs about 167 GB, more than a 128 GB machine ### 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: - Documentation and upkeep, 43 against 36 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 M.1 | LBS-27B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 65 | 55 | Laguna M.1 +10 | | Risk | 20% (20 this run) | 55 | 50 | Laguna M.1 +5 | | Agent interface | 10% (10 this run) | 85 | 70 | Laguna M.1 +15 | | Documentation and upkeep | 10% (10 this run) | 36 | 43 | LBS-27B +7 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **55.5 · C** | **53.9 · D** | | ## Facts side by side | Fact | Laguna M.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 | Apache-2.0 | 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 | 2026-07-14 | 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 | 2.5/5 (4) | none | ## Verdicts **Laguna M.1.** A large coding and agent cousin under plain Apache 2.0, for agents with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine, and no local runtime library packages it. The main risks are no upkeep since July, no hosted fallback shown today, no safety results, and repository code on the SGLang route. **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 M.1 1. Run the NVFP4 build on Blackwell GPUs with `vllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on vLLM 0.21.0 or later 2. 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 3. Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped 4. Sampling per the card is temperature 1.0 and top_k 20 5. The FP8 build's Docker image and serve.sh predate upstream support and set a 4,096-token context, so prefer a released vLLM 6. It doesn't fit our 128 GB reference machine at any published precision 7. 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 M.1 or LBS-27B? Laguna M.1 scores 55.5 (C) on agent readiness against LBS-27B's 53.9 (D), and leads in 3 of 4 scored categories. LBS-27B leads on documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/laguna-m-1-vs-lbs-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/laguna-m-1-vs-lbs-27b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "laguna-m-1", "b": "lbs-27b"}`. From a terminal: `anchor compare laguna-m-1 lbs-27b` - Each listing in full: https://www.anchorterminal.com/api/v1/tools/laguna-m-1.json and https://www.anchorterminal.com/api/v1/tools/lbs-27b.json ## Other comparisons with Laguna M.1 or LBS-27B - [Agents-A1-4B vs Laguna M.1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-m-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 M.1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-m-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 M.1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-m-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 M.1](https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-m-1.md) - [BigBang-v1 vs LBS-27B](https://www.anchorterminal.com/compare/bigbang-v1-vs-lbs-27b.md) - 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