# LBS-27B vs Ling-3.0-tiny > Ling-3.0-tiny scores 55 (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-ling-3-0-tiny - Markdown: https://www.anchorterminal.com/compare/lbs-27b-vs-ling-3-0-tiny.md (~6,400 tokens) - Slim: https://www.anchorterminal.com/compare/lbs-27b-vs-ling-3-0-tiny.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/lbs-27b-vs-ling-3-0-tiny.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 Ling-3.0-tiny scores 55 (C) on agent readiness against LBS-27B's 53.9 (D), and leads in 2 of 4 scored categories. LBS-27B leads on agent interface. 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 - Ling-3.0-tiny: grade C, 55/100, rank #88 of 113 open-weight models. Markdown https://www.anchorterminal.com/tools/ling-3-0-tiny.md · JSON https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.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: - Agent interface, 70 against 60 Watch for: No independent benchmark result yet; ScreenSpot-Pro, OS-World-G, PerceptionBench and HR-Bench numbers are inclusionAI's own runs ### Ling-3.0-tiny (C) Good for: Agents on a laptop or small GPU that want a fast local model for tool calls and short reasoning, under MIT Ahead on: - Reach, 90 against 55 - Documentation and upkeep, 56 against 43 Watch for: No hosted provider today, and Ollama support is only an unmerged pull request ## Score by category | Category | Weight | LBS-27B | Ling-3.0-tiny | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 55 | 90 | Ling-3.0-tiny +35 | | Risk | 20% (20 this run) | 50 | 50 | even | | Agent interface | 10% (10 this run) | 70 | 60 | LBS-27B +10 | | Documentation and upkeep | 10% (10 this run) | 43 | 56 | Ling-3.0-tiny +13 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **53.9 · D** | **55 · C** | | ## Facts side by side | Fact | LBS-27B | Ling-3.0-tiny | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | inclusionAI (Ant Group) | 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 (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) | MIT | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-10-26 | 2026-08-31 | | 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. **Ling-3.0-tiny.** A small, quick hybrid reasoning cousin for local agent work, tool calls and coding, under MIT. It runs in about 6 GB from inclusionAI's 4-bit GGUF in released Transformers, vLLM, SGLang and llama.cpp, with no host. The main risks are a card that doesn't match the files in places, the vLLM route running repository code, and no model safety results. ## 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 ### Ling-3.0-tiny 1. Start it with `llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768`, which needs llama.cpp b11772 or a recent release with bailingmoe3 2. With vLLM 0.32.0, `vllm serve inclusionAI/Ling-3.0-tiny --trust-remote-code --enable-prefix-caching --mamba-cache-mode align --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3`, which runs the repository's config code 3. With SGLang 0.5.22, its cookbook uses `--reasoning-parser deepseek-r1 --tool-call-parser glm45` for this model, and drop the card's NEXTN flags since tiny has no MTP layer 4. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks 5. Sampling per the card is temperature 1.0, top_p 0.95 and top_k 20 6. The template keeps earlier reasoning in the prompt, which grows long agent sessions 7. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented 8. For a guardrail, inclusionAI's Ling-3.0-tiny-singprobe scores intent, unsafe output and hallucination per token, but only through SGLang and vLLM integration branches ## Questions ### Which is better for AI agents, LBS-27B or Ling-3.0-tiny? Ling-3.0-tiny scores 55 (C) on agent readiness against LBS-27B's 53.9 (D), and leads in 2 of 4 scored categories. LBS-27B leads on agent interface. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/lbs-27b-vs-ling-3-0-tiny.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lbs-27b-vs-ling-3-0-tiny.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "lbs-27b", "b": "ling-3-0-tiny"}`. 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[Ling-3.0-tiny vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-unlimited-ocr-2.md) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). How: https://www.anchorterminal.com/agents/#mcp