Head to head · Open-weight models you run yourself · October 2026 research run
Kimi K3.1 Pro vs Laguna S 2.1
Laguna S 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.
Best open-weight models you download and run yourself for AI agents · All 4969 open weights comparisons
Which one, for what
Best 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 41
Watch for
1,561 GB of files, so it needs a node of eight large accelerators at least
Best 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
- Reach, 75 against 40
- Risk, 55 against 50
- Agent interface, 85 against 65
Watch for
No reply from Poolside to any of 34 discussions, many of them reports about thinking and tool calls
Why the edge
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
Reach Laguna S 2.1 +35
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 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.
Agent interface Laguna S 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 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.
Documentation and upkeep Kimi K3.1 Pro +10
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 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.
Risk Laguna S 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 S 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, 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.
Score by category
| Category | Weight this run | Kimi K3.1 Pro | Laguna S 2.1 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 40 | 75 | Laguna S 2.1 +35 |
| Risk | 20%20 | 50 | 55 | Laguna S 2.1 +5 |
| Agent interface | 10%10 | 65 | 85 | Laguna S 2.1 +20 |
| Documentation and upkeep | 10%10 | 51 | 41 | Kimi K3.1 Pro +10 |
| Negative events | ≤15 | 0 | 0 | |
| Total | D 48.6/100 | C 61/100 |
Facts side by side
| Fact | Kimi K3.1 Pro | Laguna S 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 | 2.3/5 (4) |
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 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.
Before you call either
Kimi K3.1 Pro
- Use Moonshot's API or Together unless you have a node of eight B300-class accelerators
- With vLLM use 0.34.0 or later and the recipe's flags,
--tool-call-parser kimi_k3 --reasoning-parser kimi_k3 - Pass the whole assistant message back in later turns, reasoning_content and tool_calls included, as the card requires
- Set
reasoning_effortto low, high or max per request. Max is the default and thinking can't be turned off - Kimi K3's model code doesn't load it, because the attention layers changed
- Validate JSON output yourself, since the card doesn't document structured output for the weights
Laguna S 2.1
- 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 - 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 - 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 - Thinking is on by default in the template, pass
chat_template_kwargswithenable_thinkingfalse to turn it off, and keepreasoning_contentfrom earlier turns - 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 - 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
- Expect occasional tool-schema slips in harnesses unlike Poolside's own, and validate JSON arguments, which Poolside lists as known limitations
Questions
Which is better for AI agents, Kimi K3.1 Pro or Laguna S 2.1?
Laguna S 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.
Other comparisons with Kimi K3.1 Pro or Laguna S 2.1
- Agents-A1-4B vs Kimi K3.1 Pro
- Agents-A1-4B vs Laguna S 2.1
- Apertus v1.5 8B vs Kimi K3.1 Pro
- Apertus v1.5 8B vs Laguna S 2.1
- Apodex-1.1-mini vs Kimi K3.1 Pro
- Apodex-1.1-mini vs Laguna S 2.1
- Arete-27B vs Kimi K3.1 Pro
- Arete-27B vs Laguna S 2.1
- BigBang-v1 vs Kimi K3.1 Pro
- BigBang-v1 vs Laguna S 2.1
- Carbon-X vs Kimi K3.1 Pro
- Carbon-X vs Laguna S 2.1
- Cosmos-Reason2-2B vs Kimi K3.1 Pro
- Cosmos-Reason2-2B vs Laguna S 2.1
- Darwin-36B-Opus vs Kimi K3.1 Pro
- Darwin-36B-Opus vs Laguna S 2.1
- DeepSeek-V3.2 vs Kimi K3.1 Pro
- DeepSeek-V3.2 vs Laguna S 2.1
- DeepSeek V4.1 Flash vs Kimi K3.1 Pro
- DeepSeek V4.1 Flash vs Laguna S 2.1
- DeepSeek-V4-Flash-0731 vs Kimi K3.1 Pro
- DeepSeek-V4-Flash-0731 vs Laguna S 2.1
- DiffusionGemma 26B A4B IT vs Kimi K3.1 Pro
- DiffusionGemma 26B A4B IT vs Laguna S 2.1
- Gemma 4 12B IT vs Kimi K3.1 Pro
- Gemma 4 12B IT vs Laguna S 2.1
- Gemma 4 26B A4B IT vs Kimi K3.1 Pro
- Gemma 4 26B A4B IT vs Laguna S 2.1
- Gemma 4 31B IT vs Kimi K3.1 Pro
- Gemma 4 31B IT vs Laguna S 2.1
- Gemma 4 E2B IT vs Kimi K3.1 Pro
- Gemma 4 E2B IT vs Laguna S 2.1
- Gemma 4 E4B IT vs Kimi K3.1 Pro
- Gemma 4 E4B IT vs Laguna S 2.1
- GLM-4.5-Air vs Kimi K3.1 Pro
- GLM-4.5-Air vs Laguna S 2.1
- GLM-4.7-Flash vs Kimi K3.1 Pro
- GLM-4.7-Flash vs Laguna S 2.1
- GLM-5.3-Flash vs Kimi K3.1 Pro
- GLM-5.3-Flash vs Laguna S 2.1
- GLM-5.3 vs Kimi K3.1 Pro
- GLM-5.3 vs Laguna S 2.1
- gpt-oss-120b vs Kimi K3.1 Pro
- gpt-oss-120b vs Laguna S 2.1
- gpt-oss-20b vs Kimi K3.1 Pro
- gpt-oss-20b vs Laguna S 2.1
- Granite 4.2 30B vs Kimi K3.1 Pro
- Granite 4.2 30B vs Laguna S 2.1
- Granite 4.2 3B vs Kimi K3.1 Pro
- Granite 4.2 3B vs Laguna S 2.1
- Granite 4.2 8B vs Kimi K3.1 Pro
- Granite 4.2 8B vs Laguna S 2.1
- Hy-Memory-30B-A3B vs Kimi K3.1 Pro
- Hy-Memory-30B-A3B vs Laguna S 2.1
- Hy3 vs Kimi K3.1 Pro
- Hy3 vs Laguna S 2.1
- Inkling-Small vs Kimi K3.1 Pro
- Inkling-Small vs Laguna S 2.1
- Inkling vs Kimi K3.1 Pro
- Inkling vs Laguna S 2.1
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Kimi K3.1 Pro
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Laguna S 2.1
- Kimi K3.1 Pro vs Kolibri-1
- Kimi K3.1 Pro vs Kronos
- Kimi K3.1 Pro vs Laguna M.1
- Kimi K3.1 Pro vs Laguna XS 2.1
- Kimi K3.1 Pro vs LBS-27B
- Kimi K3.1 Pro vs LFM2.5-2.6B
- Kimi K3.1 Pro vs LFM3-1.7B
- Kimi K3.1 Pro vs LFM3-Audio-2B
- Kimi K3.1 Pro vs LFM3-Omni-2B
- Kimi K3.1 Pro vs LFM3-VL-2B
- Kimi K3.1 Pro vs Ling-3.0-tiny
- Kimi K3.1 Pro vs Ling-3.1-flash-1019
- Kimi K3.1 Pro vs MiMo-V2.6 Distill Qwen 9B
- Kimi K3.1 Pro vs MiniCPM-o 4.5
- Kimi K3.1 Pro vs MiniCPM-V 4.7 1B
- Kimi K3.1 Pro vs MiniCPM-V 4.7 35B-A3B
- Kimi K3.1 Pro vs MiniCPM5-2B
- Kimi K3.1 Pro vs MiniMax-M2.5
- Kimi K3.1 Pro vs MiniMax-M2.7
- Kimi K3.1 Pro vs MiniMax-M3.1-Flash
- Kimi K3.1 Pro vs Mistral Large 4
- Kimi K3.1 Pro vs Muse Glimmer 30B
- Kimi K3.1 Pro vs Nanbeige4.3-33B
- Kimi K3.1 Pro vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Kimi K3.1 Pro vs Nemotron 3.5 Lightning 30B A3B
- Kimi K3.1 Pro vs Nemotron 3 Nano 30B A3B
- Kimi K3.1 Pro vs Nemotron 3 Nano 4B
- Kimi K3.1 Pro vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Kimi K3.1 Pro vs Nemotron 3 Super 120B A12B
- Kimi K3.1 Pro vs Nemotron 3 Ultra 550B A55B
- Kimi K3.1 Pro vs occamy-1.0
- Kimi K3.1 Pro vs Olmo 3 7B Think
- Kimi K3.1 Pro vs Ornith-1.5-122B
- Kimi K3.1 Pro vs Ornith-1.5-35B-A3B
- Kimi K3.1 Pro vs Ornith-1.5-397B
- Kimi K3.1 Pro vs Ornith-1.5-9B
- Kimi K3.1 Pro vs Qwen-AgentWorld-35B-A3B
- Kimi K3.1 Pro vs Qwen3-30B-A3B-Instruct-2507
- Kimi K3.1 Pro vs Qwen3-4B-Instruct-2507
- Kimi K3.1 Pro vs Qwen3.5-0.8B
- Kimi K3.1 Pro vs Qwen3.5-122B-A10B
- Kimi K3.1 Pro vs Qwen3.5-2B
- Kimi K3.1 Pro vs Qwen3.5-397B-A17B
- Kimi K3.1 Pro vs Qwen3.5-4B
- Kimi K3.1 Pro vs Qwen3.5-9B
- Kimi K3.1 Pro vs Qwen3.6-27B
- Kimi K3.1 Pro vs Qwen3.6-35B-A3B
- Kimi K3.1 Pro vs Qwen3.8-27B
- Kimi K3.1 Pro vs Qwen3.8-Flash-Next
- Kimi K3.1 Pro vs Qwen3-Coder-30B-A3B-Instruct
- Kimi K3.1 Pro vs Qwen3-Coder-Next
- Kimi K3.1 Pro vs Qwen3-Omni-30B-A3B-Instruct
- Kimi K3.1 Pro vs Qwen3-VL-2B-Instruct
- Kimi K3.1 Pro vs Qwen3-VL-30B-A3B-Instruct
- Kimi K3.1 Pro vs Qwen3-VL-32B-Instruct
- Kimi K3.1 Pro vs Qwen3-VL-4B-Instruct
- Kimi K3.1 Pro vs Qwen3-VL-8B-Instruct
- Kimi K3.1 Pro vs Qwopus3.8-27B-Flash-V2
- Kimi K3.1 Pro vs Recursive Flint
- Kimi K3.1 Pro vs Ripple-65B Preview
- Kimi K3.1 Pro vs SmolLM3-3B
- Kimi K3.1 Pro vs Spark-X2.6-4B
- Kimi K3.1 Pro vs Swift 1.5 Qwen3.8-27B
- Kimi K3.1 Pro vs Swift 1.5 Qwen3.8-Flash-Next
- Kimi K3.1 Pro vs ThinkingCap (Qwen3.8 27B)
- Kimi K3.1 Pro vs Baidu Unlimited-OCR-2
- Kimi K3 vs Kimi K3.1 Pro
- Kimi K3 vs Laguna S 2.1
- Kolibri-1 vs Laguna S 2.1
- Kronos vs Laguna S 2.1
- Laguna M.1 vs Laguna S 2.1
- Laguna S 2.1 vs Laguna XS 2.1
- Laguna S 2.1 vs LBS-27B
- Laguna S 2.1 vs LFM2.5-2.6B
- Laguna S 2.1 vs LFM3-1.7B
- Laguna S 2.1 vs LFM3-Audio-2B
- Laguna S 2.1 vs LFM3-Omni-2B
- Laguna S 2.1 vs LFM3-VL-2B
- Laguna S 2.1 vs Ling-3.0-tiny
- Laguna S 2.1 vs Ling-3.1-flash-1019
- Laguna S 2.1 vs MiMo-V2.6 Distill Qwen 9B
- Laguna S 2.1 vs MiniCPM-o 4.5
- Laguna S 2.1 vs MiniCPM-V 4.7 1B
- Laguna S 2.1 vs MiniCPM-V 4.7 35B-A3B
- Laguna S 2.1 vs MiniCPM5-2B
- Laguna S 2.1 vs MiniMax-M2.5
- Laguna S 2.1 vs MiniMax-M2.7
- Laguna S 2.1 vs MiniMax-M3.1-Flash
- Laguna S 2.1 vs Mistral Large 4
- Laguna S 2.1 vs Muse Glimmer 30B
- Laguna S 2.1 vs Nanbeige4.3-33B
- Laguna S 2.1 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Laguna S 2.1 vs Nemotron 3.5 Lightning 30B A3B
- Laguna S 2.1 vs Nemotron 3 Nano 30B A3B
- Laguna S 2.1 vs Nemotron 3 Nano 4B
- Laguna S 2.1 vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Laguna S 2.1 vs Nemotron 3 Super 120B A12B
- Laguna S 2.1 vs Nemotron 3 Ultra 550B A55B
- Laguna S 2.1 vs occamy-1.0
- Laguna S 2.1 vs Olmo 3 7B Think
- Laguna S 2.1 vs Ornith-1.5-122B
- Laguna S 2.1 vs Ornith-1.5-35B-A3B
- Laguna S 2.1 vs Ornith-1.5-397B
- Laguna S 2.1 vs Ornith-1.5-9B
- Laguna S 2.1 vs Qwen-AgentWorld-35B-A3B
- Laguna S 2.1 vs Qwen3-30B-A3B-Instruct-2507
- Laguna S 2.1 vs Qwen3-4B-Instruct-2507
- Laguna S 2.1 vs Qwen3.5-0.8B
- Laguna S 2.1 vs Qwen3.5-122B-A10B
- Laguna S 2.1 vs Qwen3.5-2B
- Laguna S 2.1 vs Qwen3.5-397B-A17B
- Laguna S 2.1 vs Qwen3.5-4B
- Laguna S 2.1 vs Qwen3.5-9B
- Laguna S 2.1 vs Qwen3.6-27B
- Laguna S 2.1 vs Qwen3.6-35B-A3B
- Laguna S 2.1 vs Qwen3.8-27B
- Laguna S 2.1 vs Qwen3.8-Flash-Next
- Laguna S 2.1 vs Qwen3-Coder-30B-A3B-Instruct
- Laguna S 2.1 vs Qwen3-Coder-Next
- Laguna S 2.1 vs Qwen3-Omni-30B-A3B-Instruct
- Laguna S 2.1 vs Qwen3-VL-2B-Instruct
- Laguna S 2.1 vs Qwen3-VL-30B-A3B-Instruct
- Laguna S 2.1 vs Qwen3-VL-32B-Instruct
- Laguna S 2.1 vs Qwen3-VL-4B-Instruct
- Laguna S 2.1 vs Qwen3-VL-8B-Instruct
- Laguna S 2.1 vs Qwopus3.8-27B-Flash-V2
- Laguna S 2.1 vs Recursive Flint
- Laguna S 2.1 vs Ripple-65B Preview
- Laguna S 2.1 vs SmolLM3-3B
- Laguna S 2.1 vs Spark-X2.6-4B
- Laguna S 2.1 vs Swift 1.5 Qwen3.8-27B
- Laguna S 2.1 vs Swift 1.5 Qwen3.8-Flash-Next
- Laguna S 2.1 vs ThinkingCap (Qwen3.8 27B)
- Laguna S 2.1 vs Baidu Unlimited-OCR-2
Machine-readable
- This page as Markdown
/compare/kimi-k3-1-pro-vs-laguna-s-2-1.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/kimi-k3-1-pro.json·/api/v1/tools/laguna-s-2-1.json - From a terminal
anchor compare kimi-k3-1-pro laguna-s-2-1(the CLI) - Over MCP
compare_tools {"a": "kimi-k3-1-pro", "b": "laguna-s-2-1"}at/mcp, no key