# LBS-27B vs MiniCPM-V 4.7 1B

> MiniCPM-V 4.7 1B scores 61 (C) to LBS-27B's 55.8 (C) 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-minicpm-v-4-7-1b
- Markdown: https://www.anchorterminal.com/compare/lbs-27b-vs-minicpm-v-4-7-1b.md (~9,100 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

MiniCPM-V 4.7 1B scores 61 (C) on agent readiness against LBS-27B's 55.8 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.

- 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
- MiniCPM-V 4.7 1B: grade C, 61/100, rank #76 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm-v-4-7-1b.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-1b.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

### 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.

Watch for: No independent benchmark result yet; ScreenSpot-Pro, OS-World-G, PerceptionBench and HR-Bench numbers are inclusionAI's own runs

### MiniCPM-V 4.7 1B (C)

Good for: Agents that need a cheap local model to read screenshots, photos and short video on a phone, a laptop or beside a bigger model

Ahead on:
- Reach, 95 against 60
- Risk, 55 against 50
- Documentation and upkeep, 48 against 43

Watch for: No limitations section, safety results, agent-use guidance or security contact


## Why the edge

### Reach: MiniCPM-V 4.7 1B +35

- 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.
- MiniCPM-V 4.7 1B (95): Memory 30, OpenBMB's own IQ4_XS GGUF is 0.64 GB, 1.54 GB with the 0.90 GB vision projector, and 1.54 GB × 1.2 = 1.9 GB, which fits 16 GB with room to spare. Runtimes 25, MiniCPMV4_7ForConditionalGeneration in vLLM's registry at v0.33.0, the minicpmv4_7 model type in SGLang's minicpmv.py at v0.5.23, the minicpmv4_7 model in Transformers at v5.22.2, a MiniCPM-V 4.7 guide and conversion code in llama.cpp at b12242, and an openbmb/minicpm-v4.7:1b tag in OpenBMB's namespace on Ollama, read as the 35B-A3B listing's is. Builds 15, OpenBMB publishes AWQ, GPTQ and int4 safetensors builds and a GGUF repository from IQ3_XS to F16, so quantised builds at two precisions or more. Terms 15, Apache 2.0 in the card's metadata and as the LICENSE of OpenBMB's MiniCPM-V repository, an OSI licence. Download 10, gated is false on the main repository and on the GGUF, AWQ, GPTQ and int4 repositories, so the files download with no account or terms. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026), and no provider's own list was read. 30 + 25 + 15 + 15 + 10 + 0 = 95.

### Risk: MiniCPM-V 4.7 1B +5

- 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.
- MiniCPM-V 4.7 1B (55): File safety 25, safetensors in the main repository and the AWQ, GPTQ and int4 builds, GGUF in the GGUF build, with no .bin, .pt, .pth or .py files in the main repository. Provenance 15, files in the openbmb organisation that OpenBMB's GitHub README links to (10), with a SHA-256 for both large files in the main repository (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or disclaimer section, and no prompt-injection guidance, instruction hierarchy or statement of trained refusals. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, nothing is added on the card, and the LICENSE in OpenBMB's GitHub repository resolved on 10 November 2026. Security programme 0, no SECURITY.md in OpenBMB/MiniCPM-V and no security.txt on openbmb.cn (both 404 on 10 November 2026), and no advisories found. 25 + 15 + 0 + 0 + 15 + 0 = 55.

### Documentation and upkeep: MiniCPM-V 4.7 1B +5

- 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.
- MiniCPM-V 4.7 1B (48): Model card 8, Intended use (image, multi-image and video understanding on device), architecture (early visual-token compression inside the vision encoder) and parameters (1B) are there. Training data 0, no description of the training data on the card. Last change 20, the last commit, on 30 October 2026, updated the card and removed an unused asset folder, eleven days before this check. Responsiveness 0, the repository has no discussions that aren't pull requests (two pull requests in all), so nobody has asked anything yet and the line reads as none answered, as settled in calibration. Version policy 10, MiniCPM-V 4.5 and 4.6 stay in their own repositories and the GitHub README dates each release. File health 10, Config, template and card agree on the architecture and the thinking switch, and no report waits without a reply, since there are no discussions. 8 + 0 + 20 + 0 + 10 + 10 = 48.

## Score by category

| Category | Weight | LBS-27B | MiniCPM-V 4.7 1B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 60 | 95 | MiniCPM-V 4.7 1B +35 |
| Risk | 20% (20 this run) | 50 | 55 | MiniCPM-V 4.7 1B +5 |
| Agent interface | 10% (10 this run) | 70 | 70 | even |
| Documentation and upkeep | 10% (10 this run) | 43 | 48 | MiniCPM-V 4.7 1B +5 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **55.8 · C** | **61 · C** | |

## Facts side by side

| Fact | LBS-27B | MiniCPM-V 4.7 1B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | inclusionAI (Ant Group) | OpenBMB |
| 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 | 2026-10-30 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |

## How the scores have moved

### LBS-27B

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

### MiniCPM-V 4.7 1B

No score change since it was listed.

## 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.

**MiniCPM-V 4.7 1B.** A vision-language cousin small enough for a phone, under plain Apache 2.0, that every runtime we count can load. The projector is bigger than the language model, so plan for about 2 GB. The card says nothing about limits or training data and doesn't show how a tool call comes back.

## 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

### MiniCPM-V 4.7 1B

1. Start it with `llama-server -hf openbmb/MiniCPM-V-4.7-1B-gguf:Q4_K_M --jinja`, or `ollama run openbmb/minicpm-v4.7:1b`
2. For image input in llama.cpp add `mmproj-model-f16.gguf`, or the smaller 4x projector
3. The GGUF files at the repository root have thinking off. Use the files in `thinking/` when you want the trace
4. Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends
5. Check tool calls in your runtime first, since the card names no parser
6. Validate JSON output yourself, since structured output isn't documented

## Questions

### Which is better for AI agents, LBS-27B or MiniCPM-V 4.7 1B?

MiniCPM-V 4.7 1B scores 61 (C) on agent readiness against LBS-27B's 55.8 (C), and leads in 3 of 4 scored categories.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/lbs-27b-vs-minicpm-v-4-7-1b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lbs-27b-vs-minicpm-v-4-7-1b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "lbs-27b", "b": "minicpm-v-4-7-1b"}`. From a terminal: `anchor compare lbs-27b minicpm-v-4-7-1b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lbs-27b.json and https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-1b.json

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- [MiniCPM-V 4.7 1B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-thinkingcap-qwen3-8-27b.md)
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