# LBS-27B vs Qwen3.6-27B

> Qwen3.6-27B scores 67.2 (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/lbs-27b-vs-qwen3-6-27b
- Markdown: https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-6-27b.md (~6,300 tokens)
- Slim: https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-6-27b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-6-27b.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

Qwen3.6-27B scores 67.2 (B) on agent readiness against LBS-27B's 53.9 (D), and leads in 2 of 4 scored categories. LBS-27B leads on 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
- Qwen3.6-27B: grade B, 67.2/100, rank #11 of 113 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-6-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-6-27b.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

### 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

### Qwen3.6-27B (B)

Good for: Agents that want an open coding and tool-calling model with vision on one 24 to 32 GB GPU or Mac, with plain licence terms.

Ahead on:
- Reach, 90 against 55
- Risk, 55 against 50

Watch for: No safety results, prompt-injection guidance or security contact


## Score by category

| Category | Weight | LBS-27B | Qwen3.6-27B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 55 | 90 | Qwen3.6-27B +35 |
| Risk | 20% (20 this run) | 50 | 55 | Qwen3.6-27B +5 |
| Agent interface | 10% (10 this run) | 70 | 70 | even |
| Documentation and upkeep | 10% (10 this run) | 43 | 36 | LBS-27B +7 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **53.9 · D** | **67.2 · B** | |

## Facts side by side

| Fact | LBS-27B | Qwen3.6-27B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | inclusionAI (Ant Group) | Alibaba (Qwen team) |
| 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) | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-26 | 2026-04-22 |
| 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 | 3/5 (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.

**Qwen3.6-27B.** A capable dense cousin for agentic coding and tool calls with images and video in, running in every major runtime. Ollama's 4-bit build needs about 22 GB, and three providers host it. The main risk is silence, with no safety results, no agent guidance, no answers in its repository and Qwen3.8-27B now ahead of it.

## 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: <name>`, and don't pass `tools`; the model answers in plain text
3. Parse `<action>crop_image(image_path=..., x1=..., y1=..., x2=..., y2=...)</action>`, 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

### Qwen3.6-27B

1. Quickest start is `ollama run qwen3.6:27b`, about 18 GB of files with the vision projector
2. Alibaba's FP8 build with the card's parsers is `vllm serve Qwen/Qwen3.6-27B-FP8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, vllm 0.19.0 or later
3. Use the qwen3_coder tool parser, the template writes calls as function and parameter blocks inside tool_call
4. Thinking is on by default, pass `"chat_template_kwargs": {"enable_thinking": false}` for short tasks, and `/think` and `/nothink` aren't supported
5. `"preserve_thinking": true` in chat_template_kwargs keeps earlier reasoning in context, which helps long agent sessions but grows the prompt
6. Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 when thinking, and temperature 0.7, top_p 0.8, presence_penalty 1.5 without
7. Keep at least 128K of context if you rely on thinking, and turn on YaRN only beyond 262,144 tokens
8. Validate any JSON it returns or use the runtime's guided decoding, since structured output isn't documented
9. Qwen3.8-27B (listed as qwen3-8-27b) is the newer model at this size on the same runtimes

## Questions

### Which is better for AI agents, LBS-27B or Qwen3.6-27B?

Qwen3.6-27B scores 67.2 (B) on agent readiness against LBS-27B's 53.9 (D), and leads in 2 of 4 scored categories. LBS-27B leads on documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-6-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-6-27b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "lbs-27b", "b": "qwen3-6-27b"}`. From a terminal: `anchor compare lbs-27b qwen3-6-27b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lbs-27b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-6-27b.json

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- [LBS-27B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-8b-instruct.md)
- [LBS-27B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/lbs-27b-vs-qwopus3-8-27b-flash-v2.md)
- [LBS-27B vs Recursive Flint](https://www.anchorterminal.com/compare/lbs-27b-vs-recursive-flint.md)
- [LBS-27B vs SmolLM3-3B](https://www.anchorterminal.com/compare/lbs-27b-vs-smollm3-3b.md)
- [LBS-27B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/lbs-27b-vs-spark-x2-6-4b.md)
- [LBS-27B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/lbs-27b-vs-swift-1-5-qwen3-8-27b.md)
- [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)
- [LFM2.5-2.6B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-6-27b.md)
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- [MiniMax-M2.7 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-6-27b.md)
- [MiniMax-M3.1-Flash vs Qwen3.6-27B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-6-27b.md)
- [Muse Glimmer 30B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-6-27b.md)
- [Nanbeige4.3-33B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-6-27b.md)
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- [Nemotron 3 Nano 4B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-6-27b.md)
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- [Nemotron 3 Super 120B A12B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-6-27b.md)
- [Nemotron 3 Ultra 550B A55B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-6-27b.md)
- [occamy-1.0 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-6-27b.md)
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- [Ornith-1.5-397B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-27b.md)
- [Ornith-1.5-9B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-27b.md)
- [Qwen3-30B-A3B-Instruct-2507 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-6-27b.md)
- [Qwen3-4B-Instruct-2507 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-6-27b.md)
- [Qwen3.5-0.8B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-6-27b.md)
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- [Qwen3.5-2B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-6-27b.md)
- [Qwen3.5-397B-A17B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-6-27b.md)
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- [Qwen3.5-9B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-6-27b.md)
- [Qwen3.6-27B vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-6-35b-a3b.md)
- [Qwen3.6-27B vs Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-8-27b.md)
- [Qwen3.6-27B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-8-flash-next.md)
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- [Qwen3.6-27B vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-coder-next.md)
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- [Qwen3.6-27B vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-30b-a3b-instruct.md)
- [Qwen3.6-27B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-32b-instruct.md)
- [Qwen3.6-27B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-4b-instruct.md)
- [Qwen3.6-27B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-8b-instruct.md)
- [Qwen3.6-27B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwopus3-8-27b-flash-v2.md)
- [Qwen3.6-27B vs Recursive Flint](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-recursive-flint.md)
- [Qwen3.6-27B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-smollm3-3b.md)
- [Qwen3.6-27B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-spark-x2-6-4b.md)
- [Qwen3.6-27B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-swift-1-5-qwen3-8-27b.md)
- [Qwen3.6-27B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-swift-1-5-qwen3-8-flash-next.md)
- [Qwen3.6-27B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-thinkingcap-qwen3-8-27b.md)
- [Qwen3.6-27B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-unlimited-ocr-2.md)

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