# BigBang-v1 vs LBS-27B

> BigBang-v1 and LBS-27B score within a point of each other for open-weight models you run yourself, 54.4 and 53.9 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/bigbang-v1-vs-lbs-27b
- Markdown: https://www.anchorterminal.com/compare/bigbang-v1-vs-lbs-27b.md (~6,100 tokens)
- Slim: https://www.anchorterminal.com/compare/bigbang-v1-vs-lbs-27b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/bigbang-v1-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

BigBang-v1 and LBS-27B score within a point of each other on agent readiness, 54.4 (C) and 53.9 (D). LBS-27B leads on agent interface. Both do open-weight models you run yourself.

- BigBang-v1: grade C, 54.4/100, rank #90 of 113 open-weight models. Markdown https://www.anchorterminal.com/tools/bigbang-v1.md · JSON https://www.anchorterminal.com/api/v1/tools/bigbang-v1.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

### BigBang-v1 (C)

Good for: Agents with 96 GB or more that want an Apache 2.0 mixture-of-experts model tuned for research, search and coding, and can do without publisher support.

Ahead on:
- Risk, 55 against 50

Watch for: BF16 is the only build from the publisher, needing about 86 GB

### 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


## Score by category

| Category | Weight | BigBang-v1 | LBS-27B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 55 | 55 | even |
| Risk | 20% (20 this run) | 55 | 50 | BigBang-v1 +5 |
| Agent interface | 10% (10 this run) | 60 | 70 | LBS-27B +10 |
| Documentation and upkeep | 10% (10 this run) | 46 | 43 | BigBang-v1 +3 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **54.4 · C** | **53.9 · D** | |

## Facts side by side

| Fact | BigBang-v1 | LBS-27B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | The Endless Frontier (endless-frontier) | 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 | 2026-08-06 | 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 | 2.3/5 (4) | none |

## Verdicts

**BigBang-v1.** A Qwen3.6-35B-A3B fine-tuned on synthetic research problems, under plain Apache 2.0, for search, science and coding work. The publisher ships BF16 only, about 86 GB to run, and has no report, safety results or replies to questions, so smaller builds and support come from others.

**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

### BigBang-v1

1. On the reference machine use `vllm serve endless-frontier/BigBang-v1 --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`
2. The card says to keep at least 128K of context so its thinking isn't cut short
3. Thinking control isn't documented, try the base template's `enable_thinking` false in chat_template_kwargs and check the output
4. For a smaller build you'll need someone else's quantisation, the publisher has none
5. Validate JSON output yourself or use the runtime's guided decoding

### 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, BigBang-v1 or LBS-27B?

BigBang-v1 and LBS-27B score within a point of each other on agent readiness, 54.4 (C) and 53.9 (D). LBS-27B leads on agent interface.


## For agents

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

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- [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 Ling-3.0-tiny](https://www.anchorterminal.com/compare/lbs-27b-vs-ling-3-0-tiny.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 MiniCPM-o 4.5](https://www.anchorterminal.com/compare/lbs-27b-vs-minicpm-o-4-5.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 Muse Glimmer 30B](https://www.anchorterminal.com/compare/lbs-27b-vs-muse-glimmer-30b.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 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-1.5-35B-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 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 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)

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