# Laguna M.1 vs Laguna XS 2.1

> Laguna XS 2.1 scores 61 (C) to Laguna M.1's 55.5 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/laguna-m-1-vs-laguna-xs-2-1
- Markdown: https://www.anchorterminal.com/compare/laguna-m-1-vs-laguna-xs-2-1.md (~5,950 tokens)
- Slim: https://www.anchorterminal.com/compare/laguna-m-1-vs-laguna-xs-2-1.min.md (~380 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/laguna-m-1-vs-laguna-xs-2-1.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: 2026-10-21

Laguna XS 2.1 scores 61 (C) on agent readiness against Laguna M.1's 55.5 (C), and leads in 2 of 4 scored categories. Both do open-weight models you run yourself.

- Laguna M.1: grade C, 55.5/100, rank #79 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-m-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-m-1.json
- Laguna XS 2.1: grade C, 61/100, rank #40 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-xs-2-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-xs-2-1.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Laguna M.1 (C)

Good for: Teams with a multi-GPU Blackwell or Hopper server that want a large coding agent model under Apache 2.0.

Watch for: The smallest build needs about 167 GB, more than a 128 GB machine

### Laguna XS 2.1 (C)

Good for: Agents that want a fast local coding model on a 32 GB GPU or Mac, with tool calling and a permissive licence.

Ahead on:
- Reach, 85 against 65
- Documentation and upkeep, 46 against 36

Watch for: No reply from Poolside to any discussion, including reports about the tokenizer and remote code


## Score by category

| Category | Weight | Laguna M.1 | Laguna XS 2.1 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 65 | 85 | Laguna XS 2.1 +20 |
| Risk | 20% (20 this run) | 55 | 55 | even |
| Agent interface | 10% (10 this run) | 85 | 85 | even |
| Documentation and upkeep | 10% (10 this run) | 36 | 46 | Laguna XS 2.1 +10 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **55.5 · C** | **61 · C** | |

## Facts side by side

| Fact | Laguna M.1 | Laguna XS 2.1 |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Poolside | Poolside |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | OpenMDW-1.1 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-07-14 | 2026-08-19 |
| Terms last updated |  |  |
| Privacy policy last updated |  |  |
| 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 | 2.5/5 (4) | 2.8/5 (4) |

## Verdicts

**Laguna M.1.** A large coding and agent cousin under plain Apache 2.0, for agents with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine, and no local runtime library packages it. The main risks are no upkeep since July, no hosted fallback shown today, no safety results, and repository code on the SGLang route.

**Laguna XS 2.1.** A small coding and agent cousin with 3B active parameters, so it's quick on modest hardware. Poolside's 4-bit GGUF, also Ollama's default, needs about 24 GB, and every major runtime runs it. The main risks are that nobody at Poolside answers its discussions, there are no safety results, and the SGLang route runs repository code.

## Before you call either

### Laguna M.1

1. Run the NVFP4 build on Blackwell GPUs with `vllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on vLLM 0.21.0 or later
2. Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{"enable_thinking": true}'`, which Poolside recommends for agentic coding
3. Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped
4. Sampling per the card is temperature 1.0 and top_k 20
5. The FP8 build's Docker image and serve.sh predate upstream support and set a 4,096-token context, so prefer a released vLLM
6. It doesn't fit our 128 GB reference machine at any published precision
7. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented

### Laguna XS 2.1

1. Quickest start is `ollama run laguna-xs-2.1`, 20 GB of files, the same Q4_K_M as Poolside's GGUF
2. On our reference machine use `llama-server -hf poolside/Laguna-XS-2.1-GGUF:Q4_K_M --jinja -c 131072`, from a llama.cpp release with the laguna architecture (b11772 has it), or BF16 with vLLM (66.9 GB)
3. With vLLM use `--tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on a build with vllm-project/vllm#47311, or `--tool-call-parser glm47` on older builds
4. Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{"enable_thinking": true}'`, which Poolside recommends for agentic coding
5. Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped
6. Sampling per the card is temperature 1.0 and top_k 20
7. The card warns that Ollama on Apple Silicon may return empty output until llama.cpp#25389 ships
8. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented

## Questions

### Which is better for AI agents, Laguna M.1 or Laguna XS 2.1?

Laguna XS 2.1 scores 61 (C) on agent readiness against Laguna M.1's 55.5 (C), and leads in 2 of 4 scored categories.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/laguna-m-1-vs-laguna-xs-2-1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/laguna-m-1-vs-laguna-xs-2-1.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "laguna-m-1", "b": "laguna-xs-2-1"}`. From a terminal: `anchor compare laguna-m-1 laguna-xs-2-1`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/laguna-m-1.json and https://www.anchorterminal.com/api/v1/tools/laguna-xs-2-1.json

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