# LBS-27B vs LFM2.5-2.6B > LBS-27B scores 53.9 (D) to LFM2.5-2.6B's 51.5 (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-lfm2-5-2-6b - Markdown: https://www.anchorterminal.com/compare/lbs-27b-vs-lfm2-5-2-6b.md (~6,300 tokens) - Slim: https://www.anchorterminal.com/compare/lbs-27b-vs-lfm2-5-2-6b.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/lbs-27b-vs-lfm2-5-2-6b.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 LBS-27B scores 53.9 (D) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 0 of 4 scored categories. LFM2.5-2.6B leads on reach, agent interface and documentation and upkeep. 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 - LFM2.5-2.6B: grade D, 51.5/100, rank #99 of 113 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm2-5-2-6b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.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 ### LFM2.5-2.6B (D) Good for: Agents that want a small local helper for tool calls, extraction and RAG on a laptop, phone or CPU, at organisations under the licence's revenue threshold Ahead on: - Reach, 95 against 55 - Agent interface, 75 against 70 - Documentation and upkeep, 57 against 43 Watch for: Commercial use isn't licensed for organisations with $10M or more of annual revenue ## Score by category | Category | Weight | LBS-27B | LFM2.5-2.6B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 55 | 95 | LFM2.5-2.6B +40 | | Risk | 20% (20 this run) | 50 | 50 | even | | Agent interface | 10% (10 this run) | 70 | 75 | LFM2.5-2.6B +5 | | Documentation and upkeep | 10% (10 this run) | 43 | 57 | LFM2.5-2.6B +14 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **53.9 · D** | **51.5 · D** | | ## Facts side by side | Fact | LBS-27B | LFM2.5-2.6B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | inclusionAI (Ant Group) | Liquid 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) | LFM Open License v1.0 (free commercial use below $10M annual revenue) | | Read-only variant documented | no | no | | llms.txt | no | yes | | Last release | 2026-10-26 | 2026-09-22 | | 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 | 2.5/5 (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. **LFM2.5-2.6B.** A small, fast cousin for tool calls, extraction and long-context steps on a laptop or phone. Liquid's own 4-bit build is 1.6 GB, and every common runtime runs it. It always thinks, isn't meant for coding or knowledge-heavy work, and organisations over $10M of revenue need another licence. There are no safety results or security contact. ## 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 ### LFM2.5-2.6B 1. Quickest start is `ollama run LiquidAI/lfm2.5-2.6b`, 1.7 GB, or `llama-cli -hf LiquidAI/LFM2.5-2.6B-GGUF` 2. With vLLM or SGLang pass `--tool-call-parser lfm2`; for vLLM add `--reasoning-parser qwen3` so the thinking comes back separately 3. Sample at temperature 0.1, top_k 50 and repetition_penalty 1.1, as the card and generation_config.json set 4. Put tools in the system prompt as `List of tools: [...]` JSON, or pass tools to apply_chat_template; calls come back as a Python list of function calls, and the system prompt can ask for JSON calls instead 5. Leave max_tokens unset or generous, since a low cap cuts the reasoning off mid-thought 6. Earlier thinking is dropped from the prompt unless `preserve_thinking` is true 7. Liquid's 328M LFM2.5-2.6B-DSpark drafter speeds decoding about 2.6 times in SGLang and on Apple silicon, per Liquid 8. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented ## Questions ### Which is better for AI agents, LBS-27B or LFM2.5-2.6B? LBS-27B scores 53.9 (D) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 0 of 4 scored categories. LFM2.5-2.6B leads on reach, agent interface and documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/lbs-27b-vs-lfm2-5-2-6b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lbs-27b-vs-lfm2-5-2-6b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "lbs-27b", "b": "lfm2-5-2-6b"}`. 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