Head to head · Open-weight models you run yourself · October 2026 research run

LBS-27B vs SenseNova-Loop-9B-Preview

LBS-27B scores 55.8 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 2 of 4 scored categories. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons

Which one, for what

LBS-27B C

Best 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, 60 against 40
  • 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

SenseNova-Loop-9B-Preview D

Best for Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

No released runtime runs it. vLLM and SGLang need unmerged pull requests

Why the edge

Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.

Reach LBS-27B +20

LBS-27B 60

Memory earns 10, since the only build is 54.73 GB of BF16, about 66 GB with a fifth added, which fits the reference machine but not 64 GB. Runtimes earn 25 from released vLLM, SGLang, Transformers and llama.cpp, which all carry Qwen3.8-27B's architecture, and Ollama through the llama.cpp release it pins, so 25. Builds earn 0, as inclusionAI publishes no quantised build and the GGUF copies are bartowski's and mradermacher's. Terms earn 15 for Apache 2.0 with nothing added, on the weights and on the base. Download earns 10, with no gate. Hosted earns 0, as no provider or inclusionAI API serves it.

SenseNova-Loop-9B-Preview 40

Memory 15, the only build is the safetensors set, 38.14 GB with its MTP head, since the Hub counts 9.41B of its parameters in F32. Runtimes 0, none of the six runs it in a released version. Builds 0, SenseNova publishes one set of weights and no quantised build. Terms 15, Apache 2.0 in the card's metadata and on the card, which links the LICENSE in the GitHub repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API that serves it. 15 + 0 + 0 + 15 + 10 + 0 = 40.

Agent interface LBS-27B +10

LBS-27B 70

The chat template earns 20, Qwen's Jinja file in the repository. The tool-calling format earns 10, as general tool calls rest on the inherited template and the crop action is parsed by inclusionAI's own code, not a runtime parser. Trained tool calling earns 20 for up to two crop actions per turn learned in GRPO. Structured output earns 0. Reasoning control earns 10, with reasoning effort documented and the reasoning shown as think text. Context earns 10 for 262,144 tokens with no scaling.

SenseNova-Loop-9B-Preview 60

Chat template 20, chat_template.jinja in the repository (7,756 characters). Tool-calling format 10, only the template says it: a tools block, and calls written as tool_call blocks with function and parameter tags. Trained tool calling 10, the card says the training recipes include tool trajectories and that the model is for agentic workflows. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the card documents enable_thinking in the chat template call, on or off per request, and the template puts the reasoning in think tags ahead of the answer. Context 10, 262,144 tokens in training, per the card's specification table, and config.json gives the language model 262,144 positions. 20 + 10 + 10 + 0 + 10 + 10 = 60.

Score by category

CategoryWeight this runLBS-27BSenseNova-Loop-9B-PreviewEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%256040LBS-27B +20
Risk20%205050even
Agent interface10%107060LBS-27B +10
Documentation and upkeep10%104344SenseNova-Loop-9B-Preview +1
Negative events≤1500
TotalC 55.8/100D 46.8/100

Facts side by side

FactLBS-27BSenseNova-Loop-9B-Preview
KindOpen-weight modelOpen-weight model
VendorinclusionAI (Ant Group)SenseTime (SenseNova)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-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 documentednono
llms.txtnono
Last release2026-10-262026-11-02
Terms last updatedno document linkednot read yet
Privacy policy last updatedno document linkedno document linked

How the scores have moved

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.

SenseNova-Loop-9B-Preview

An unusual architecture under Apache 2.0, with a 256K training context, a thinking switch and tool calling in the template. It's a preview in practice too: no released runtime runs it, loading it runs the repository's code, the files are twice the size the card implies, and nobody from SenseNova has answered a question yet.

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

SenseNova-Loop-9B-Preview

  1. Read modeling_sensenova_loop.py before you load it. trust_remote_code runs it
  2. Pin transformers 5.18.0, the version the card checked
  3. Load with dtype=torch.bfloat16 as the card does, or the 38 GB of F32 weights stay at full size
  4. Pass enable_thinking=False to apply_chat_template for a direct answer
  5. Tool calls come back as tool_call blocks with function and parameter tags. Parse them yourself, since no runtime ships a parser
  6. Wait for the vLLM or SGLang pull request to land in a release before putting it behind an API

Questions

Which is better for AI agents, LBS-27B or SenseNova-Loop-9B-Preview?

LBS-27B scores 55.8 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 2 of 4 scored categories.

Other comparisons with LBS-27B or SenseNova-Loop-9B-Preview

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