# Inkling vs LBS-27B > Inkling scores 64.2 (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/inkling-vs-lbs-27b - Markdown: https://www.anchorterminal.com/compare/inkling-vs-lbs-27b.md (~6,100 tokens) - Slim: https://www.anchorterminal.com/compare/inkling-vs-lbs-27b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/inkling-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 Inkling scores 64.2 (B) on agent readiness against LBS-27B's 53.9 (D), and leads in 3 of 4 scored categories. LBS-27B leads on reach. Both do open-weight models you run yourself. - Inkling: grade B, 64.2/100, rank #21 of 113 open-weight models. Markdown https://www.anchorterminal.com/tools/inkling.md · JSON https://www.anchorterminal.com/api/v1/tools/inkling.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 ### Inkling (B) Good for: Agents calling a hosted open multimodal model with audio input, or teams with a multi-GPU datacentre node who want to run it themselves. Ahead on: - Risk, 70 against 50 - Agent interface, 85 against 70 - Documentation and upkeep, 82 against 43 Watch for: The smallest build Thinking Machines publishes needs about 710 GB, beyond any single workstation ### 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: - Reach, 55 against 50 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 | Inkling | LBS-27B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 50 | 55 | LBS-27B +5 | | Risk | 20% (20 this run) | 70 | 50 | Inkling +20 | | Agent interface | 10% (10 this run) | 85 | 70 | Inkling +15 | | Documentation and upkeep | 10% (10 this run) | 82 | 43 | Inkling +39 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **64.2 · B** | **53.9 · D** | | ## Facts side by side | Fact | Inkling | LBS-27B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Thinking Machines Lab | 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 | 2026-07-23 | 2026-10-26 | | 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 | 3/5 (4) | none | ## Verdicts **Inkling.** Thinking Machines' large multimodal generalist, stronger than Inkling-Small on factual recall by its own numbers. It needs about 710 GB of datacentre GPU memory even in NVFP4, so most agents will use a host such as Together AI or Tinker. The main risks are a use policy that binds on download and can change, and no outside safety results. **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 ### Inkling 1. Unless you have a multi-GPU Blackwell or H200 node, call it through Together AI, Fireworks, DeepInfra or Tinker rather than starting it 2. On 8 B200 GPUs, SGLang's recipe is `sglang serve --trust-remote-code --model-path thinkingmachines/Inkling-NVFP4 --tp 8 --quantization modelopt_fp4 --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling` 3. Use the inkling tool parser, not Hermes, and the inkling reasoning parser 4. Pass `reasoning_effort` as none, minimal, low, medium, high (the default) or max, or a number from 0 to 0.99. The repository's template has no xhigh preset and raises an error for it, though Thinking Machines' docs list xhigh 5. Tool call arguments must reach the template as an object, not a JSON string, or it raises an error 6. Audio should be 16 kHz WAV, ideally under 20 minutes, and images 40 to 4096 px a side, per the card 7. Read the Model Acceptable Use Policy before deploying, it binds on download and bars some uses that are legal ### 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, Inkling or LBS-27B? Inkling scores 64.2 (B) on agent readiness against LBS-27B's 53.9 (D), and leads in 3 of 4 scored categories. LBS-27B leads on reach. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/inkling-vs-lbs-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/inkling-vs-lbs-27b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "inkling", "b": "lbs-27b"}`. From a terminal: `anchor compare inkling lbs-27b` - Each listing in full: https://www.anchorterminal.com/api/v1/tools/inkling.json and https://www.anchorterminal.com/api/v1/tools/lbs-27b.json ## Other comparisons with Inkling or LBS-27B - [Agents-A1-4B vs Inkling](https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling.md) - [Agents-A1-4B vs LBS-27B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lbs-27b.md) - [Apertus v1.5 8B vs Inkling](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling.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 Inkling](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-inkling.md) - [Apodex-1.1-mini vs LBS-27B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lbs-27b.md) - [BigBang-v1 vs Inkling](https://www.anchorterminal.com/compare/bigbang-v1-vs-inkling.md) - [BigBang-v1 vs LBS-27B](https://www.anchorterminal.com/compare/bigbang-v1-vs-lbs-27b.md) - 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[LBS-27B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/lbs-27b-vs-swift-1-5-qwen3-8-flash-next.md) - [LBS-27B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/lbs-27b-vs-thinkingcap-qwen3-8-27b.md) - [LBS-27B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/lbs-27b-vs-unlimited-ocr-2.md) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). 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