Head to head · Open-weight models you run yourself · October 2026 research run
Laguna XS 2.1 vs LongCat-2.5
Laguna XS 2.1 scores 61 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 2 of 4 scored categories. LongCat-2.5 leads on risk. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons
Which one, for what
Best 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 35
- Agent interface, 85 against 75
Watch for
No reply from Poolside to any discussion, including reports about the tokenizer and remote code
Best for Operators with NPU clusters, and anyone tracking what the open flagships can do
Ahead on
- Risk, 60 against 55
Watch for
3,562 GB in BF16 and 1,336 GB at 4 bits
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 XS 2.1 +45
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.
LongCat-2.5 35
Memory 0, the smallest build is Meituan's W4A4 repository, 1,336 GB, and 1,336 GB × 1.2 = 1,603 GB, far above 256 GB. The BF16 files are 3,562 GB. Runtimes 0, none of the six runs it in a released version: vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2 and llama.cpp b12242 have no LongcatCausalLM or pro_v code, and the model code config.json names isn't in the repository. Builds 10, one quantised build from Meituan, the W4A4 repository (4-bit experts, 1,336 GB). Terms 15, MIT in the metadata, the LICENSE file and the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 0 + 0 + 10 + 15 + 10 + 0 = 35.
Agent interface Laguna XS 2.1 +10
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.
LongCat-2.5 75
Chat template 20, chat_template.jinja in the repository (11,823 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card documents how tools are declared (the OpenAI function format) and how calls come back (tool_calls with an id, a name and JSON arguments, answered by a tool message), in a worked example against an OpenAI-compatible server. Trained tool calling 10, the card describes reinforcement learning on long agentic tasks and shows one tool call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking on or off per request through chat_template_kwargs, with the reasoning returned as reasoning_content, both shown on the card. Context 10, the card states no context length, but config.json gives 524,288 positions and the card's own evaluations ran at 512K and 1M sequence lengths after what it calls long-context training, so 128K or more is taken as trained. 20 + 25 + 10 + 0 + 10 + 10 = 75.
Risk LongCat-2.5 +5
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.
LongCat-2.5 60
File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the meituan-longcat organisation, the name of Meituan's GitHub organisation that the card links for deployment, and the card's licence names Meituan (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions under Usage Considerations: not evaluated for every use, assess accuracy, safety and fairness before sensitive use, and follow the law. Licence risk 15, MIT with no use policy, no threshold and no right to change the terms. Security programme 0, no way to report a flaw on the card beyond a general contact, and Meituan's own security pages weren't looked up, so this scores as absent and is an open question. 25 + 15 + 0 + 5 + 15 + 0 = 60.
Score by category
| Category | Weight this run | Laguna XS 2.1 | LongCat-2.5 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 80 | 35 | Laguna XS 2.1 +45 |
| Risk | 20%20 | 55 | 60 | LongCat-2.5 +5 |
| Agent interface | 10%10 | 85 | 75 | Laguna XS 2.1 +10 |
| Documentation and upkeep | 10%10 | 46 | 46 | even |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | D 50.5/100 |
Facts side by side
| Fact | Laguna XS 2.1 | LongCat-2.5 |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Poolside | Meituan (LongCat) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | OpenMDW-1.1 | MIT |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-08-19 | 2026-11-10 |
| Terms last updated | 2026-07-21 | not read yet |
| 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 | 2.8/5 (4) | none |
How the scores have moved
LongCat-2.5
No score change since it was listed.
Verdicts
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.
LongCat-2.5
Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up.
Before you call either
Laguna XS 2.1
- Quickest start is
ollama run laguna-xs-2.1, 20 GB of files, the same Q4_K_M as Poolside's GGUF - 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) - 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 glm47on older builds - Thinking is off in the template unless you pass
enable_thinkingtrue or start the server with--default-chat-template-kwargs '{"enable_thinking": true}', which Poolside recommends for agentic coding - Pass earlier reasoning back as
reasoning_contentin the history, since the card says reasoning may stop in later steps if it's dropped - Sampling per the card is temperature 1.0 and top_k 20
- The card warns that Ollama on Apple Silicon may return empty output until llama.cpp#25389 ships
- Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented
LongCat-2.5
- You can't run this on a workstation or a single GPU server. Plan for a provider, and none serves it yet
- If an operator serves it, call it as an OpenAI-compatible chat API with tools in the function format
- Pass
chat_template_kwargswithenable_thinkingtrue or false - With thinking on, keep earlier reasoning in the history (
save_history_reasoning_contenttrue), as the card recommends - The card's results use temperature 1.0 and top_p 0.95
- Its benchmark table is Meituan's own. The unmarked scores are in-house
Questions
Which is better for AI agents, Laguna XS 2.1 or LongCat-2.5?
Laguna XS 2.1 scores 61 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 2 of 4 scored categories. LongCat-2.5 leads on risk.
Other comparisons with Laguna XS 2.1 or LongCat-2.5
- Agents-A1-4B vs Laguna XS 2.1
- Agents-A1-4B vs LongCat-2.5
- Apertus v1.5 8B vs Laguna XS 2.1
- Apertus v1.5 8B vs LongCat-2.5
- Apodex-1.1-mini vs Laguna XS 2.1
- Apodex-1.1-mini vs LongCat-2.5
- Arete-27B vs Laguna XS 2.1
- Arete-27B vs LongCat-2.5
- BigBang-v1 vs Laguna XS 2.1
- BigBang-v1 vs LongCat-2.5
- Carbon-X vs Laguna XS 2.1
- Carbon-X vs LongCat-2.5
- Cola2 vs Laguna XS 2.1
- Cola2 vs LongCat-2.5
- Cosmos-Reason2-2B vs Laguna XS 2.1
- Cosmos-Reason2-2B vs LongCat-2.5
- Darwin-36B-Opus vs Laguna XS 2.1
- Darwin-36B-Opus vs LongCat-2.5
- DeepSeek-V3.2 vs Laguna XS 2.1
- DeepSeek-V3.2 vs LongCat-2.5
- DeepSeek V4.1 Flash vs Laguna XS 2.1
- DeepSeek V4.1 Flash vs LongCat-2.5
- DeepSeek-V4-Flash-0731 vs Laguna XS 2.1
- DeepSeek-V4-Flash-0731 vs LongCat-2.5
- Devstral Small 2 vs Laguna XS 2.1
- Devstral Small 2 vs LongCat-2.5
- DiffusionGemma 26B A4B IT vs Laguna XS 2.1
- DiffusionGemma 26B A4B IT vs LongCat-2.5
- EXAONE 4.5 33B vs Laguna XS 2.1
- EXAONE 4.5 33B vs LongCat-2.5
- Gemma 4 12B IT vs Laguna XS 2.1
- Gemma 4 12B IT vs LongCat-2.5
- Gemma 4 26B A4B IT vs Laguna XS 2.1
- Gemma 4 26B A4B IT vs LongCat-2.5
- Gemma 4 31B IT vs Laguna XS 2.1
- Gemma 4 31B IT vs LongCat-2.5
- Gemma 4 E2B IT vs Laguna XS 2.1
- Gemma 4 E2B IT vs LongCat-2.5
- Gemma 4 E4B IT vs Laguna XS 2.1
- Gemma 4 E4B IT vs LongCat-2.5
- GLM-4.5-Air vs Laguna XS 2.1
- GLM-4.5-Air vs LongCat-2.5
- GLM-4.7-Flash vs Laguna XS 2.1
- GLM-4.7-Flash vs LongCat-2.5
- GLM-5.3-Flash vs Laguna XS 2.1
- GLM-5.3-Flash vs LongCat-2.5
- GLM-5.3 vs Laguna XS 2.1
- GLM-5.3 vs LongCat-2.5
- gpt-oss-120b vs Laguna XS 2.1
- gpt-oss-120b vs LongCat-2.5
- gpt-oss-20b vs Laguna XS 2.1
- gpt-oss-20b vs LongCat-2.5
- Granite 4.2 30B vs Laguna XS 2.1
- Granite 4.2 30B vs LongCat-2.5
- Granite 4.2 3B vs Laguna XS 2.1
- Granite 4.2 3B vs LongCat-2.5
- Granite 4.2 8B vs Laguna XS 2.1
- Granite 4.2 8B vs LongCat-2.5
- Hy-Memory-30B-A3B vs Laguna XS 2.1
- Hy-Memory-30B-A3B vs LongCat-2.5
- Hy3 vs Laguna XS 2.1
- Hy3 vs LongCat-2.5
- Inkling-Small vs Laguna XS 2.1
- Inkling-Small vs LongCat-2.5
- Inkling vs Laguna XS 2.1
- Inkling vs LongCat-2.5
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Laguna XS 2.1
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs LongCat-2.5
- Kimi K3.1 Pro vs Laguna XS 2.1
- Kimi K3.1 Pro vs LongCat-2.5
- Kimi K3 vs Laguna XS 2.1
- Kimi K3 vs LongCat-2.5
- Kolibri-1 vs Laguna XS 2.1
- Kolibri-1 vs LongCat-2.5
- Kronos vs Laguna XS 2.1
- Kronos vs LongCat-2.5
- Laguna M.1 vs Laguna XS 2.1
- Laguna M.1 vs LongCat-2.5
- Laguna S 2.1 vs Laguna XS 2.1
- Laguna S 2.1 vs LongCat-2.5
- Laguna XS 2.1 vs LBS-27B
- Laguna XS 2.1 vs LFM2.5-2.6B
- Laguna XS 2.1 vs LFM3-1.7B
- Laguna XS 2.1 vs LFM3-Audio-2B
- Laguna XS 2.1 vs LFM3-Omni-2B
- Laguna XS 2.1 vs LFM3-VL-2B
- Laguna XS 2.1 vs Ling-3.0-tiny
- Laguna XS 2.1 vs Ling-3.1-flash-1019
- Laguna XS 2.1 vs MiMo-V2.6 Distill Qwen 9B
- Laguna XS 2.1 vs MiMo-V2.6-Flash-RL
- Laguna XS 2.1 vs MiniCPM-o 4.5
- Laguna XS 2.1 vs MiniCPM-V 4.7 1B
- Laguna XS 2.1 vs MiniCPM-V 4.7 35B-A3B
- Laguna XS 2.1 vs MiniCPM5-2B
- Laguna XS 2.1 vs MiniMax-M2.5
- Laguna XS 2.1 vs MiniMax-M2.7
- Laguna XS 2.1 vs MiniMax-M3.1-Flash
- Laguna XS 2.1 vs Mistral Large 4
- Laguna XS 2.1 vs Muse Glimmer 30B
- Laguna XS 2.1 vs Nanbeige4.2-3B
- Laguna XS 2.1 vs Nanbeige4.3-33B
- Laguna XS 2.1 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Laguna XS 2.1 vs Nemotron 3.5 Lightning 30B A3B
- Laguna XS 2.1 vs Nemotron 3 Nano 30B A3B
- Laguna XS 2.1 vs Nemotron 3 Nano 4B
- Laguna XS 2.1 vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Laguna XS 2.1 vs Nemotron 3 Super 120B A12B
- Laguna XS 2.1 vs Nemotron 3 Ultra 550B A55B
- Laguna XS 2.1 vs North Mini Code 1.0
- Laguna XS 2.1 vs occamy-1.0
- Laguna XS 2.1 vs Olmo 3 7B Think
- Laguna XS 2.1 vs Ornith-1.5-122B
- Laguna XS 2.1 vs Ornith-1.5-35B-A3B
- Laguna XS 2.1 vs Ornith-1.5-397B
- Laguna XS 2.1 vs Ornith-1.5-9B
- Laguna XS 2.1 vs OTel-2.0-LLM-31B-IT
- Laguna XS 2.1 vs Qwen-AgentWorld-35B-A3B
- Laguna XS 2.1 vs Qwen3-30B-A3B-Instruct-2507
- Laguna XS 2.1 vs Qwen3-4B-Instruct-2507
- Laguna XS 2.1 vs Qwen3.5-0.8B
- Laguna XS 2.1 vs Qwen3.5-122B-A10B
- Laguna XS 2.1 vs Qwen3.5-2B
- Laguna XS 2.1 vs Qwen3.5-397B-A17B
- Laguna XS 2.1 vs Qwen3.5-4B
- Laguna XS 2.1 vs Qwen3.5-9B
- Laguna XS 2.1 vs Qwen3.6-27B
- Laguna XS 2.1 vs Qwen3.6-35B-A3B
- Laguna XS 2.1 vs Qwen3.8-27B
- Laguna XS 2.1 vs Qwen3.8-Flash-Next
- Laguna XS 2.1 vs Qwen3-Coder-30B-A3B-Instruct
- Laguna XS 2.1 vs Qwen3-Coder-Next
- Laguna XS 2.1 vs Qwen3-Omni-30B-A3B-Instruct
- Laguna XS 2.1 vs Qwen3-VL-2B-Instruct
- Laguna XS 2.1 vs Qwen3-VL-30B-A3B-Instruct
- Laguna XS 2.1 vs Qwen3-VL-32B-Instruct
- Laguna XS 2.1 vs Qwen3-VL-4B-Instruct
- Laguna XS 2.1 vs Qwen3-VL-8B-Instruct
- Laguna XS 2.1 vs Qwopus3.8-27B-Flash-V2
- Laguna XS 2.1 vs Recursive Flint
- Laguna XS 2.1 vs Ripple-65B Preview
- Laguna XS 2.1 vs SenseNova-Loop-9B-Preview
- Laguna XS 2.1 vs SmolLM3-3B
- Laguna XS 2.1 vs Spark-X2.6-4B
- Laguna XS 2.1 vs Step 3.7 Flash
- Laguna XS 2.1 vs Swift 1.5 Qwen3.8-27B
- Laguna XS 2.1 vs Swift 1.5 Qwen3.8-Flash-Next
- Laguna XS 2.1 vs ThinkingCap (Qwen3.8 27B)
- Laguna XS 2.1 vs Baidu Unlimited-OCR-2
- Laguna XS 2.1 vs WeVisOne-VL-2B
- Laguna XS 2.1 vs WeVisOne-VL-4B
- Laguna XS 2.1 vs ZAYA1-8B
- LBS-27B vs LongCat-2.5
- LFM2.5-2.6B vs LongCat-2.5
- LFM3-1.7B vs LongCat-2.5
- LFM3-Audio-2B vs LongCat-2.5
- LFM3-Omni-2B vs LongCat-2.5
- LFM3-VL-2B vs LongCat-2.5
- Ling-3.0-tiny vs LongCat-2.5
- Ling-3.1-flash-1019 vs LongCat-2.5
- LongCat-2.5 vs MiMo-V2.6 Distill Qwen 9B
- LongCat-2.5 vs MiMo-V2.6-Flash-RL
- LongCat-2.5 vs MiniCPM-o 4.5
- LongCat-2.5 vs MiniCPM-V 4.7 1B
- LongCat-2.5 vs MiniCPM-V 4.7 35B-A3B
- LongCat-2.5 vs MiniCPM5-2B
- LongCat-2.5 vs MiniMax-M2.5
- LongCat-2.5 vs MiniMax-M2.7
- LongCat-2.5 vs MiniMax-M3.1-Flash
- LongCat-2.5 vs Mistral Large 4
- LongCat-2.5 vs Muse Glimmer 30B
- LongCat-2.5 vs Nanbeige4.2-3B
- LongCat-2.5 vs Nanbeige4.3-33B
- LongCat-2.5 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- LongCat-2.5 vs Nemotron 3.5 Lightning 30B A3B
- LongCat-2.5 vs Nemotron 3 Nano 30B A3B
- LongCat-2.5 vs Nemotron 3 Nano 4B
- LongCat-2.5 vs Nemotron 3 Nano Omni 30B A3B Reasoning
- LongCat-2.5 vs Nemotron 3 Super 120B A12B
- LongCat-2.5 vs Nemotron 3 Ultra 550B A55B
- LongCat-2.5 vs North Mini Code 1.0
- LongCat-2.5 vs occamy-1.0
- LongCat-2.5 vs Olmo 3 7B Think
- LongCat-2.5 vs Ornith-1.5-122B
- LongCat-2.5 vs Ornith-1.5-35B-A3B
- LongCat-2.5 vs Ornith-1.5-397B
- LongCat-2.5 vs Ornith-1.5-9B
- LongCat-2.5 vs OTel-2.0-LLM-31B-IT
- LongCat-2.5 vs Qwen-AgentWorld-35B-A3B
- LongCat-2.5 vs Qwen3-30B-A3B-Instruct-2507
- LongCat-2.5 vs Qwen3-4B-Instruct-2507
- LongCat-2.5 vs Qwen3.5-0.8B
- LongCat-2.5 vs Qwen3.5-122B-A10B
- LongCat-2.5 vs Qwen3.5-2B
- LongCat-2.5 vs Qwen3.5-397B-A17B
- LongCat-2.5 vs Qwen3.5-4B
- LongCat-2.5 vs Qwen3.5-9B
- LongCat-2.5 vs Qwen3.6-27B
- LongCat-2.5 vs Qwen3.6-35B-A3B
- LongCat-2.5 vs Qwen3.8-27B
- LongCat-2.5 vs Qwen3.8-Flash-Next
- LongCat-2.5 vs Qwen3-Coder-30B-A3B-Instruct
- LongCat-2.5 vs Qwen3-Coder-Next
- LongCat-2.5 vs Qwen3-Omni-30B-A3B-Instruct
- LongCat-2.5 vs Qwen3-VL-2B-Instruct
- LongCat-2.5 vs Qwen3-VL-30B-A3B-Instruct
- LongCat-2.5 vs Qwen3-VL-32B-Instruct
- LongCat-2.5 vs Qwen3-VL-4B-Instruct
- LongCat-2.5 vs Qwen3-VL-8B-Instruct
- LongCat-2.5 vs Qwopus3.8-27B-Flash-V2
- LongCat-2.5 vs Recursive Flint
- LongCat-2.5 vs Ripple-65B Preview
- LongCat-2.5 vs SenseNova-Loop-9B-Preview
- LongCat-2.5 vs SmolLM3-3B
- LongCat-2.5 vs Spark-X2.6-4B
- LongCat-2.5 vs Step 3.7 Flash
- LongCat-2.5 vs Swift 1.5 Qwen3.8-27B
- LongCat-2.5 vs Swift 1.5 Qwen3.8-Flash-Next
- LongCat-2.5 vs ThinkingCap (Qwen3.8 27B)
- LongCat-2.5 vs Baidu Unlimited-OCR-2
- LongCat-2.5 vs WeVisOne-VL-2B
- LongCat-2.5 vs WeVisOne-VL-4B
- LongCat-2.5 vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/laguna-xs-2-1-vs-longcat-2-5.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/laguna-xs-2-1.json·/api/v1/tools/longcat-2-5.json - From a terminal
anchor compare laguna-xs-2-1 longcat-2-5(the CLI) - Over MCP
compare_tools {"a": "laguna-xs-2-1", "b": "longcat-2-5"}at/mcp, no key