# Kimi K3.1 Pro vs Laguna XS 2.1

> Laguna XS 2.1 scores 61 (C) to Kimi K3.1 Pro's 48.6 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-laguna-xs-2-1
- Markdown: https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-laguna-xs-2-1.md (~9,250 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 XS 2.1 scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 3 of 4 scored categories. Kimi K3.1 Pro leads on documentation and upkeep. Both do open-weight models you run yourself.

- Kimi K3.1 Pro: grade D, 48.6/100, rank #151 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/kimi-k3-1-pro.md · JSON https://www.anchorterminal.com/api/v1/tools/kimi-k3-1-pro.json
- Laguna XS 2.1: grade C, 61/100, rank #46 of 160 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 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Kimi K3.1 Pro (D)

Good for: Operators with a multi-accelerator node who want Moonshot's newest open-weight model for coding and long agent sessions, and everyone else through an API

Ahead on:
- Documentation and upkeep, 51 against 46

Watch for: 1,561 GB of files, so it needs a node of eight large accelerators at least

### 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, 80 against 40
- Risk, 55 against 50
- Agent interface, 85 against 65

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


## Why the edge

### Reach: Laguna XS 2.1 +40

- Kimi K3.1 Pro (40): Memory 0, the only build is Moonshot's 1,561 GB MXFP4 release, far above 256 GB. Runtimes 5, llama.cpp b12242 alone has the model code in a release, since vLLM's recipe needs 0.34.0 and SGLang's cookbook a branch. Builds 10, one precision. Terms 10, free commercial use below a model-as-a-service revenue threshold. Download 10, not gated. Hosted 5, Moonshot's API and Together serve it. 0 + 5 + 10 + 10 + 10 + 5 = 40.
- Laguna XS 2.1 (80): Memory 20 of 30, the smallest 4-bit build with an exact size is Poolside's Q4_K_M GGUF at 20.27 GB, the same file as Ollama's default, which needs 32 GB with a fifth added. 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 0, Poolside's OpenRouter endpoints expired on 31 October 2026 and no other host was found.

### Agent interface: Laguna XS 2.1 +20

- Kimi K3.1 Pro (65): Chat template 10, the format is only code to run. Tool-calling format 10, the parser is named in the runtimes' recipes and the card doesn't say how tools are declared. Trained tool calling 20, several calls a turn on Kimi K3's tools channel. Structured output 5, named among linked guides without the how. Reasoning control 10, reasoning_effort per request. Context 10, 1M tokens. 10 + 10 + 20 + 5 + 10 + 10 = 65.
- Laguna XS 2.1 (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and calls come back and names the poolside_v1 parser for three runtimes. Trained tool calling 20, the template renders several calls a turn. Structured output 0, not documented. Reasoning control 10, enable_thinking per request with the reasoning in reasoning_content. Context 10, 128K trained per Poolside's report, with 262,144 reached by YaRN.

### Risk: Laguna XS 2.1 +5

- Kimi K3.1 Pro (50): File safety 15, safetensors only, less 10 for remote code and prompts built by the repository's script. Provenance 15, Moonshot's own organisation with a hash on every file. Safety evaluation 10, Moonshot's own cyber benchmark results and nobody else's yet. Agent guidance 0, none on the card. Licence risk 10, the revenue threshold. Security programme 0, no way to report a flaw found. 15 + 15 + 10 + 0 + 10 + 0 = 50.
- Laguna XS 2.1 (55): File safety 15 of 25, safetensors and GGUF only, but the repository ships model code behind auto_map and the card's SGLang and TensorRT-LLM commands use trust_remote_code. Provenance 15, the verified poolside organisation that Poolside's blog links, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 5, general cautions only. Licence risk 15, OpenMDW-1.1 has no use policy, threshold or revocation clause. Security programme 5, a signed security.txt and a security contact on the card, with no public advisories found.

### Documentation and upkeep: Kimi K3.1 Pro +5

- Kimi K3.1 Pro (51): Model card 11, no limitations, languages or cut-off. Training data 5, one general sentence. Last change 20, a commit on 5 November 2026. Responsiveness 0, none of the 8 newest discussions has a reply from Moonshot. Version policy 5, Kimi K3 keeps its repository and no dated note was found. File health 10, the files agree. 11 + 5 + 20 + 0 + 5 + 10 = 51.
- Laguna XS 2.1 (46): Model card 11 of 20, intended use, architecture, parameters and context are there, limitations, languages and a cut-off aren't. Training data 10, the linked report describes sources and filtering and Poolside's S 2.1 post gives the shared November 2025 cut-off. Last change 15, 19 August 2026, 76 days back. Responsiveness 0, Poolside answered none of the 6 discussions. Version policy 5, XS.2 is kept and release notes are dated, but GGUF files were replaced in place without a card note. File health 5, generation config and template disagree on the thinking default and two file reports wait without a reply.

## Score by category

| Category | Weight | Kimi K3.1 Pro | Laguna XS 2.1 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 40 | 80 | Laguna XS 2.1 +40 |
| Risk | 20% (20 this run) | 50 | 55 | Laguna XS 2.1 +5 |
| Agent interface | 10% (10 this run) | 65 | 85 | Laguna XS 2.1 +20 |
| Documentation and upkeep | 10% (10 this run) | 51 | 46 | Kimi K3.1 Pro +5 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **48.6 · D** | **61 · C** | |

## Facts side by side

| Fact | Kimi K3.1 Pro | Laguna XS 2.1 |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Moonshot AI | Poolside |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Kimi K3.1 Pro License (modified MIT) | OpenMDW-1.1 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-11-05 | 2026-08-19 |
| Terms last updated | no document linked | 2026-07-21 |
| Privacy policy last updated | no document linked | 2026-07-21 |
| Customer content may train models | not read yet | yes, with an opt-out |
| Terms restrict automated access | not read yet | not found in the text |
| Terms restrict benchmarking | not read yet | not found in the text |
| Terms or service can change without notice | not read yet | yes |
| Arbitration or class-action waiver | not read yet | yes |
| Agent reviews | none | 2.8/5 (4) |

## How the scores have moved

### Kimi K3.1 Pro

No score change since it was listed.

### Laguna XS 2.1

- no change on 3 November 2026: product

## Verdicts

**Kimi K3.1 Pro.** Moonshot's newest flagship, eight days old when read, for operators with a rack. Only llama.cpp has its model code in a release, with vLLM and SGLang support still on a release candidate and a branch. The licence is permissive below a revenue threshold, and Moonshot hasn't answered a discussion on the repository yet.

**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

### Kimi K3.1 Pro

1. Use Moonshot's API or Together unless you have a node of eight B300-class accelerators
2. With vLLM use 0.34.0 or later and the recipe's flags, `--tool-call-parser kimi_k3 --reasoning-parser kimi_k3`
3. Pass the whole assistant message back in later turns, reasoning_content and tool_calls included, as the card requires
4. Set `reasoning_effort` to low, high or max per request. Max is the default and thinking can't be turned off
5. Kimi K3's model code doesn't load it, because the attention layers changed
6. Validate JSON output yourself, since the card doesn't document structured output for the weights

### 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, Kimi K3.1 Pro or Laguna XS 2.1?

Laguna XS 2.1 scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 3 of 4 scored categories. Kimi K3.1 Pro leads on documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-laguna-xs-2-1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-laguna-xs-2-1.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "kimi-k3-1-pro", "b": "laguna-xs-2-1"}`. From a terminal: `anchor compare kimi-k3-1-pro laguna-xs-2-1`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/kimi-k3-1-pro.json and https://www.anchorterminal.com/api/v1/tools/laguna-xs-2-1.json

## Other comparisons with Kimi K3.1 Pro or Laguna XS 2.1

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- [Laguna XS 2.1 vs ZAYA1-8B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-zaya1-8b.md)

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