# Arete-27B vs LBS-27B

> LBS-27B and Arete-27B score within a point of each other for open-weight models you run yourself, 55.8 and 55.1 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/arete-27b-vs-lbs-27b
- Markdown: https://www.anchorterminal.com/compare/arete-27b-vs-lbs-27b.md (~8,500 tokens)
- Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: 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)
- DNS: dig +short TXT <slug>.grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.<category>. and <a>.vs.<b>. under the zone, and the same under grade.letme.dev)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-11-10

LBS-27B and Arete-27B score within a point of each other on agent readiness, 55.8 (C) and 55.1 (C). Arete-27B leads on risk and documentation and upkeep. Both do open-weight models you run yourself.

- Arete-27B: grade C, 55.1/100, rank #123 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/arete-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/arete-27b.json
- LBS-27B: grade C, 55.8/100, rank #118 of 160 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 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Arete-27B (C)

Good for: Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine

Ahead on:
- Risk, 55 against 50
- Documentation and upkeep, 63 against 43

Watch for: No quantised build from Nous Research, so the GGUF route depends on a third party

### LBS-27B (C)

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, 60 against 50
- 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


## Why the edge

### Documentation and upkeep: Arete-27B +20

- Arete-27B (63): Model card 8, no limitations, context length, languages or cut-off. Training data 20, the training pairs and the evaluation set are published with their counts. Last change 20, a commit on 6 November 2026. Responsiveness 0, none of 13 discussions has a reply from Nous Research. Version policy 5, a first release. File health 10, the files agree. 8 + 20 + 20 + 0 + 5 + 10 = 63.
- LBS-27B (43): The model card earns 8, with intended use, one limitation and parameters, and no architecture, context, languages or cut-off. Training data earns 5 for a general description of synthetic GUI data with no cut-off. Last change earns 20 for 26 October 2026. Responsiveness earns 0, the one discussion unanswered. Version policy earns 5 for a first release. File health earns 5, as the config still sets an MTP layer the weights lack and the card names a reasoning setting the template refuses.

### Reach: LBS-27B +10

- Arete-27B (50): Memory 10, the only build from Nous Research is the 54.7 GB BF16 release, which needs about 66 GB and fits the reference machine. Runtimes 15, vLLM, SGLang and Transformers run the base's architecture from those files, and llama.cpp isn't credited because Nous Research publishes no GGUF. Builds 0, no quantised build of its own. Terms 15, Apache 2.0. Download 10, not gated. Hosted 0, no provider. 10 + 15 + 0 + 15 + 10 + 0 = 50.
- 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.

### Agent interface: LBS-27B +10

- Arete-27B (60): Chat template 20, a Jinja file in the repository. Tool-calling format 10, only the template says it. Trained tool calling 20, several calls a turn and agentic work as an intended use. Structured output 0 and reasoning control 0, neither is documented on the card. Context 10, 262,144 tokens native. 20 + 10 + 20 + 0 + 0 + 10 = 60.
- 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.

### Risk: Arete-27B +5

- Arete-27B (55): File safety 25, safetensors only and no remote code. Provenance 15, Nous Research's own organisation, linked from its blog, with a hash on every file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 15, plain Apache 2.0. Security programme 0, no way to report a flaw found. 25 + 15 + 0 + 0 + 15 + 0 = 55.
- LBS-27B (50): File safety earns 15, safetensors only but with the documented route running the publisher's scripts to serve it and to write its system prompt. Provenance earns 15, inclusionAI's own organisation with a hash for every file. Safety evaluation and agent guidance earn 0, as nothing is published on either, prompt injection through screenshots included. Licence risk keeps all 15 under Apache 2.0. The security programme earns 5 for Ant Group's AntSRC, which isn't linked to the model.

## Score by category

| Category | Weight | Arete-27B | LBS-27B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 50 | 60 | LBS-27B +10 |
| Risk | 20% (20 this run) | 55 | 50 | Arete-27B +5 |
| Agent interface | 10% (10 this run) | 60 | 70 | LBS-27B +10 |
| Documentation and upkeep | 10% (10 this run) | 63 | 43 | Arete-27B +20 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **55.1 · C** | **55.8 · C** | |

## Facts side by side

| Fact | Arete-27B | LBS-27B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Nous Research | 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 | 2026-11-06 | 2026-10-26 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |

## How the scores have moved

### Arete-27B

No score change since it was listed.

### LBS-27B

- +1.9 on 4 November 2026 (D to C): correction

## Verdicts

**Arete-27B.** Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.

**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

### Arete-27B

1. Start it with `llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja`, a third party's conversion, or serve the BF16 files with vLLM
2. Use the base's parsers in vLLM, `--reasoning-parser qwen3 --tool-call-parser qwen3_coder`, and check tool calls first, since the template differs slightly from Qwen3.8-27B's
3. Thinking switches with `enable_thinking` in the template. The card's own evaluation ran with it off
4. Expect answers on politically sensitive topics to differ from Qwen3.8-27B's. That is what the fine-tune is for
5. Validate JSON output yourself, since structured output isn't documented

### 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

## Questions

### Which is better for AI agents, Arete-27B or LBS-27B?

LBS-27B and Arete-27B score within a point of each other on agent readiness, 55.8 (C) and 55.1 (C). Arete-27B leads on risk and documentation and upkeep.


## For agents

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

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- [Arete-27B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-omni-30b-a3b-instruct.md)
- [Arete-27B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-2b-instruct.md)
- [Arete-27B vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-30b-a3b-instruct.md)
- [Arete-27B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-32b-instruct.md)
- [Arete-27B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-4b-instruct.md)
- [Arete-27B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-8b-instruct.md)
- [Arete-27B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/arete-27b-vs-qwopus3-8-27b-flash-v2.md)
- [Arete-27B vs Recursive Flint](https://www.anchorterminal.com/compare/arete-27b-vs-recursive-flint.md)
- [Arete-27B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/arete-27b-vs-ripple-65b-preview.md)
- [Arete-27B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/arete-27b-vs-sensenova-loop-9b-preview.md)
- [Arete-27B vs SmolLM3-3B](https://www.anchorterminal.com/compare/arete-27b-vs-smollm3-3b.md)
- [Arete-27B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/arete-27b-vs-spark-x2-6-4b.md)
- [Arete-27B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/arete-27b-vs-step-3-7-flash.md)
- [Arete-27B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/arete-27b-vs-swift-1-5-qwen3-8-27b.md)
- [Arete-27B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/arete-27b-vs-swift-1-5-qwen3-8-flash-next.md)
- [Arete-27B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/arete-27b-vs-thinkingcap-qwen3-8-27b.md)
- [Arete-27B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/arete-27b-vs-unlimited-ocr-2.md)
- [Arete-27B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-2b.md)
- [Arete-27B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-4b.md)
- [Arete-27B vs ZAYA1-8B](https://www.anchorterminal.com/compare/arete-27b-vs-zaya1-8b.md)
- [BigBang-v1 vs LBS-27B](https://www.anchorterminal.com/compare/bigbang-v1-vs-lbs-27b.md)
- [Carbon-X vs LBS-27B](https://www.anchorterminal.com/compare/carbon-x-vs-lbs-27b.md)
- [Cola2 vs LBS-27B](https://www.anchorterminal.com/compare/cola2-vs-lbs-27b.md)
- [Cosmos-Reason2-2B vs LBS-27B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lbs-27b.md)
- [Darwin-36B-Opus vs LBS-27B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lbs-27b.md)
- [DeepSeek-V3.2 vs LBS-27B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lbs-27b.md)
- [DeepSeek V4.1 Flash vs LBS-27B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lbs-27b.md)
- [DeepSeek-V4-Flash-0731 vs LBS-27B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lbs-27b.md)
- [Devstral Small 2 vs LBS-27B](https://www.anchorterminal.com/compare/devstral-small-2-vs-lbs-27b.md)
- [DiffusionGemma 26B A4B IT vs LBS-27B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lbs-27b.md)
- [EXAONE 4.5 33B vs LBS-27B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lbs-27b.md)
- [Gemma 4 12B IT vs LBS-27B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lbs-27b.md)
- [Gemma 4 26B A4B IT vs LBS-27B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lbs-27b.md)
- [Gemma 4 31B IT vs LBS-27B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lbs-27b.md)
- [Gemma 4 E2B IT vs LBS-27B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lbs-27b.md)
- [Gemma 4 E4B IT vs LBS-27B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-lbs-27b.md)
- [GLM-4.5-Air vs LBS-27B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-lbs-27b.md)
- [GLM-4.7-Flash vs LBS-27B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lbs-27b.md)
- [GLM-5.3-Flash vs LBS-27B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lbs-27b.md)
- [GLM-5.3 vs LBS-27B](https://www.anchorterminal.com/compare/glm-5-3-vs-lbs-27b.md)
- [gpt-oss-120b vs LBS-27B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lbs-27b.md)
- [gpt-oss-20b vs LBS-27B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lbs-27b.md)
- [Granite 4.2 30B vs LBS-27B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-lbs-27b.md)
- [Granite 4.2 3B vs LBS-27B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lbs-27b.md)
- [Granite 4.2 8B vs LBS-27B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-lbs-27b.md)
- [Hy-Memory-30B-A3B vs LBS-27B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-lbs-27b.md)
- [Hy3 vs LBS-27B](https://www.anchorterminal.com/compare/hy3-vs-lbs-27b.md)
- [Inkling-Small vs LBS-27B](https://www.anchorterminal.com/compare/inkling-small-vs-lbs-27b.md)
- [Inkling vs LBS-27B](https://www.anchorterminal.com/compare/inkling-vs-lbs-27b.md)
- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs LBS-27B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lbs-27b.md)
- [Kimi K3.1 Pro vs LBS-27B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-lbs-27b.md)
- [Kimi K3 vs LBS-27B](https://www.anchorterminal.com/compare/kimi-k3-vs-lbs-27b.md)
- [Kolibri-1 vs LBS-27B](https://www.anchorterminal.com/compare/kolibri-1-vs-lbs-27b.md)
- [Kronos vs LBS-27B](https://www.anchorterminal.com/compare/kronos-vs-lbs-27b.md)
- [Laguna M.1 vs LBS-27B](https://www.anchorterminal.com/compare/laguna-m-1-vs-lbs-27b.md)
- [Laguna S 2.1 vs LBS-27B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lbs-27b.md)
- [Laguna XS 2.1 vs LBS-27B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lbs-27b.md)
- [LBS-27B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/lbs-27b-vs-lfm2-5-2-6b.md)
- [LBS-27B vs LFM3-1.7B](https://www.anchorterminal.com/compare/lbs-27b-vs-lfm3-1-7b.md)
- [LBS-27B vs LFM3-Audio-2B](https://www.anchorterminal.com/compare/lbs-27b-vs-lfm3-audio-2b.md)
- [LBS-27B vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/lbs-27b-vs-lfm3-omni-2b.md)
- [LBS-27B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/lbs-27b-vs-lfm3-vl-2b.md)
- [LBS-27B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/lbs-27b-vs-ling-3-0-tiny.md)
- [LBS-27B vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/lbs-27b-vs-ling-3-1-flash-1019.md)
- [LBS-27B vs LongCat-2.5](https://www.anchorterminal.com/compare/lbs-27b-vs-longcat-2-5.md)
- [LBS-27B vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/lbs-27b-vs-mimo-v2-6-distill-qwen-9b.md)
- [LBS-27B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/lbs-27b-vs-mimo-v2-6-flash-rl.md)
- [LBS-27B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/lbs-27b-vs-minicpm-o-4-5.md)
- [LBS-27B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/lbs-27b-vs-minicpm-v-4-7-1b.md)
- [LBS-27B vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/lbs-27b-vs-minicpm-v-4-7-35b-a3b.md)
- [LBS-27B vs MiniCPM5-2B](https://www.anchorterminal.com/compare/lbs-27b-vs-minicpm5-2b.md)
- [LBS-27B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/lbs-27b-vs-minimax-m2-5.md)
- [LBS-27B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/lbs-27b-vs-minimax-m2-7.md)
- [LBS-27B vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/lbs-27b-vs-minimax-m3-1-flash.md)
- [LBS-27B vs Mistral Large 4](https://www.anchorterminal.com/compare/lbs-27b-vs-mistral-large-4.md)
- [LBS-27B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/lbs-27b-vs-muse-glimmer-30b.md)
- [LBS-27B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/lbs-27b-vs-nanbeige-4-2-3b.md)
- [LBS-27B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/lbs-27b-vs-nanbeige-4-3-33b.md)
- [LBS-27B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-5-bolt-e4b-4gb.md)
- [LBS-27B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-5-lightning-30b-a3b.md)
- [LBS-27B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-nano-30b-a3b.md)
- [LBS-27B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-nano-4b.md)
- [LBS-27B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)
- [LBS-27B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-super-120b-a12b.md)
- [LBS-27B vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-ultra-550b-a55b.md)
- [LBS-27B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/lbs-27b-vs-north-mini-code-1-0.md)
- [LBS-27B vs occamy-1.0](https://www.anchorterminal.com/compare/lbs-27b-vs-occamy-1-0.md)
- [LBS-27B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/lbs-27b-vs-olmo-3-7b-think.md)
- [LBS-27B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/lbs-27b-vs-ornith-1-5-122b.md)
- [LBS-27B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/lbs-27b-vs-ornith-1-5-35b-a3b.md)
- [LBS-27B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/lbs-27b-vs-ornith-1-5-397b.md)
- [LBS-27B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/lbs-27b-vs-ornith-1-5-9b.md)
- [LBS-27B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/lbs-27b-vs-otel-2-0-llm-31b-it.md)
- [LBS-27B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen-agentworld-35b-a3b.md)
- [LBS-27B vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-30b-a3b-instruct-2507.md)
- [LBS-27B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-4b-instruct-2507.md)
- [LBS-27B vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-0-8b.md)
- [LBS-27B vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-122b-a10b.md)
- [LBS-27B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-2b.md)
- [LBS-27B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-397b-a17b.md)
- [LBS-27B vs Qwen3.5-4B](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-4b.md)
- [LBS-27B vs Qwen3.5-9B](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-9b.md)
- [LBS-27B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-6-27b.md)
- [LBS-27B vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-6-35b-a3b.md)
- [LBS-27B vs Qwen3.8-27B](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-8-27b.md)
- [LBS-27B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-8-flash-next.md)
- [LBS-27B vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-coder-30b-a3b-instruct.md)
- [LBS-27B vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-coder-next.md)
- [LBS-27B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-omni-30b-a3b-instruct.md)
- [LBS-27B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-2b-instruct.md)
- [LBS-27B vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-30b-a3b-instruct.md)
- [LBS-27B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-32b-instruct.md)
- [LBS-27B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-4b-instruct.md)
- [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 Ripple-65B Preview](https://www.anchorterminal.com/compare/lbs-27b-vs-ripple-65b-preview.md)
- [LBS-27B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/lbs-27b-vs-sensenova-loop-9b-preview.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 Step 3.7 Flash](https://www.anchorterminal.com/compare/lbs-27b-vs-step-3-7-flash.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)
- [LBS-27B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/lbs-27b-vs-wevisone-vl-2b.md)
- [LBS-27B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/lbs-27b-vs-wevisone-vl-4b.md)
- [LBS-27B vs ZAYA1-8B](https://www.anchorterminal.com/compare/lbs-27b-vs-zaya1-8b.md)

---

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