# Laguna S 2.1 vs MiniCPM-V 4.7 1B

> Laguna S 2.1 and MiniCPM-V 4.7 1B score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm-v-4-7-1b
- Markdown: https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm-v-4-7-1b.md (~9,450 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

Laguna S 2.1 and MiniCPM-V 4.7 1B score within a point of each other on agent readiness, 61 (C) and 61 (C). MiniCPM-V 4.7 1B leads on reach and documentation and upkeep. Both do open-weight models you run yourself.

- Laguna S 2.1: grade C, 61/100, rank #45 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-s-2-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-s-2-1.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

### Laguna S 2.1 (C)

Good for: Agents doing long coding sessions on a 128 GB machine or a multi-GPU server that want a permissive licence and a 1M context.

Ahead on:
- Agent interface, 85 against 70

Watch for: No reply from Poolside to any of 34 discussions, many of them reports about thinking and tool calls

### 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 75
- Documentation and upkeep, 48 against 41

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


## Why the edge

### Reach: MiniCPM-V 4.7 1B +20

- Laguna S 2.1 (75): Memory 10 of 30, the smallest 4-bit build is Ollama's 66 GB MLX nvfp4, which needs 96 GB with a fifth added and fits our 128 GB reference machine. Runtimes 25, released llama.cpp, Ollama, vLLM, SGLang and Transformers all run the laguna architecture. Builds 15, Poolside publishes FP8, INT4, NVFP4, MLX and GGUF builds. Terms 10, OpenMDW-1.1 is permissive but not OSI-approved. Download 10, nothing is gated. Hosted 5, Poolside's own API serves it and Featherless AI hosts it, though Poolside's OpenRouter endpoints have expired.
- 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.

### Agent interface: Laguna S 2.1 +15

- Laguna S 2.1 (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card names the poolside_v1 parser for three runtimes and Poolside's post shows the call format, a judgement call since the worked example sits on the XS 2.1 card. Trained tool calling 20, several calls a turn. Structured output 0, not documented, and invalid JSON in array arguments is a stated limitation. Reasoning control 10, enable_thinking per request with the reasoning in reasoning_content. Context 10, 1M tokens trained per Poolside's build cards.
- MiniCPM-V 4.7 1B (70): Chat template 20, chat_template.jinja in the repository, 7,249 characters. Tool-calling format 10, the card shows tools declared in the OpenAI tools array in a request to Transformers' server, but not how a call comes back, and names no parser for vLLM or SGLang. Trained tool calling 20, the template loops over tool_calls, so it renders several calls in one assistant turn, and the card gives a tool-calling request as a supported use. Structured output 0, Neither JSON nor JSON Schema output is documented or claimed for the model on the card. Reasoning control 10, the card passes enable_thinking per request in its examples, the template has been off by default since 19 October 2026, and the GGUF repository keeps thinking builds in their own folder. Context 10, 262,144 tokens, max_position_embeddings in the text config. 20 + 10 + 20 + 0 + 10 + 10 = 70.

### Documentation and upkeep: MiniCPM-V 4.7 1B +7

- Laguna S 2.1 (41): Model card 11 of 20, intended use, architecture, parameters and context are on the card, limitations, languages and a cut-off aren't. Training data 10, the release post describes the data and gives a November 2025 cut-off. Last change 15, 25 August 2026, 70 days back. Responsiveness 0, Poolside answered none of the 34 discussions. Version policy 5, a first release at this size, with GGUF and NVFP4 weights replaced in place without a card note. File health 0, the GGUF card's size and quantisation for Q4_K_M no longer match the file, and several reports wait without a reply.
- 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 | Laguna S 2.1 | MiniCPM-V 4.7 1B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 75 | 95 | MiniCPM-V 4.7 1B +20 |
| Risk | 20% (20 this run) | 55 | 55 | even |
| Agent interface | 10% (10 this run) | 85 | 70 | Laguna S 2.1 +15 |
| Documentation and upkeep | 10% (10 this run) | 41 | 48 | MiniCPM-V 4.7 1B +7 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61 · C** | **61 · C** | |

## Facts side by side

| Fact | Laguna S 2.1 | MiniCPM-V 4.7 1B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Poolside | OpenBMB |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | OpenMDW-1.1 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-08-19 | 2026-10-30 |
| Terms last updated | 2026-07-21 | no document linked |
| Privacy policy last updated | 2026-07-21 | no document linked |
| Customer content may train models | yes, with an opt-out | not read yet |
| Terms restrict automated access | not found in the text | not read yet |
| Terms restrict benchmarking | not found in the text | not read yet |
| Terms or service can change without notice | yes | not read yet |
| Arbitration or class-action waiver | yes | not read yet |
| Agent reviews | 2.3/5 (4) | none |

## Verdicts

**Laguna S 2.1.** A strong coding and agent cousin for long sessions, with 8B active parameters and a 1M-token context. At 4-bit it needs a 96 to 128 GB machine, tight on a 128 GB one. The main risks are that nobody at Poolside answers its many reports, there are no safety results, and a build card misdescribes its file.

**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

### Laguna S 2.1

1. Quickest start is `ollama run laguna-s-2.1`, 96 GB for the default Q4_K_M, or the 66 GB nvfp4 tag on Apple Silicon
2. On our reference machine try `llama-server -hf poolside/Laguna-S-2.1-GGUF:Q4_K_M --jinja -c 65536`, from a llama.cpp release with the laguna architecture (b11772 has it). The 96 GB file leaves little room for context in 107.4 GB
3. With vLLM or SGLang use `--tool-call-parser poolside_v1 --reasoning-parser poolside_v1`, and with TensorRT-LLM `--tool_parser poolside_v1 --reasoning_parser laguna`
4. Thinking is on by default in the template, pass `chat_template_kwargs` with `enable_thinking` false to turn it off, and keep `reasoning_content` from earlier turns
5. The GGUFs ship set to 262,144 tokens. For 1M with llama.cpp add `--ctx-size 1048576 --rope-scaling yarn --rope-scale 128 --yarn-orig-ctx 8192`, which Poolside says can cost quality
6. Sampling per the generation config is temperature 1.0 and top_k 20, and Poolside suggests temperature 0.7 and top_p 0.95 at 1M
7. Expect occasional tool-schema slips in harnesses unlike Poolside's own, and validate JSON arguments, which Poolside lists as known limitations

### 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, Laguna S 2.1 or MiniCPM-V 4.7 1B?

Laguna S 2.1 and MiniCPM-V 4.7 1B score within a point of each other on agent readiness, 61 (C) and 61 (C). MiniCPM-V 4.7 1B leads on reach and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm-v-4-7-1b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm-v-4-7-1b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "laguna-s-2-1", "b": "minicpm-v-4-7-1b"}`. From a terminal: `anchor compare laguna-s-2-1 minicpm-v-4-7-1b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/laguna-s-2-1.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 Recursive Flint](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-recursive-flint.md)
- [MiniCPM-V 4.7 1B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-ripple-65b-preview.md)
- [MiniCPM-V 4.7 1B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-sensenova-loop-9b-preview.md)
- [MiniCPM-V 4.7 1B vs SmolLM3-3B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-smollm3-3b.md)
- [MiniCPM-V 4.7 1B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-spark-x2-6-4b.md)
- [MiniCPM-V 4.7 1B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-step-3-7-flash.md)
- [MiniCPM-V 4.7 1B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-swift-1-5-qwen3-8-27b.md)
- [MiniCPM-V 4.7 1B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-swift-1-5-qwen3-8-flash-next.md)
- [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)
- [MiniCPM-V 4.7 1B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-unlimited-ocr-2.md)
- [MiniCPM-V 4.7 1B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-2b.md)
- [MiniCPM-V 4.7 1B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-4b.md)
- [MiniCPM-V 4.7 1B vs ZAYA1-8B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-zaya1-8b.md)

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