Head to head · Open-weight models you run yourself · October 2026 research run
Laguna S 2.1 vs LongCat-2.5
Laguna S 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 and documentation and upkeep. 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 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 35
- Agent interface, 85 against 75
Watch for
No reply from Poolside to any of 34 discussions, many of them reports about thinking and tool calls
Best for Operators with NPU clusters, and anyone tracking what the open flagships can do
Ahead on
- Risk, 60 against 55
- Documentation and upkeep, 46 against 41
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 S 2.1 +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.
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 S 2.1 +10
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.
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 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.
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.
Documentation and upkeep LongCat-2.5 +5
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.
LongCat-2.5 46
Model card 11, Intended use, limitations (the usage considerations), the architecture and the parameter counts are on the card. Training data 5, A general description of the training stages and where the reinforcement learning tasks came from, with no sources, sizes or cut-off. Last change 20, the last commit, on 10 November 2026, updated the card, on the day of this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (9 and 10 November 2026) has a reply from a meituan-longcat member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, LongCat-2.0 stays in its own repositories and 2.5 came out in new ones, with no dated note on the older ones that we read. File health 5, the template is in both places and agrees, but config.json's auto_map names configuration_longcat_oe and modeling_longcat_oe, which aren't in the repository, and the tokenizer config's 131,072-token length is a quarter of the config's 524,288 positions. 11 + 5 + 20 + 0 + 5 + 5 = 46.
Score by category
| Category | Weight this run | Laguna S 2.1 | LongCat-2.5 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 75 | 35 | Laguna S 2.1 +40 |
| Risk | 20%20 | 55 | 60 | LongCat-2.5 +5 |
| Agent interface | 10%10 | 85 | 75 | Laguna S 2.1 +10 |
| Documentation and upkeep | 10%10 | 41 | 46 | LongCat-2.5 +5 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | D 50.5/100 |
Facts side by side
| Fact | Laguna S 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 | 2.3/5 (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.
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 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
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 S 2.1 or LongCat-2.5?
Laguna S 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 and documentation and upkeep.
Other comparisons with Laguna S 2.1 or LongCat-2.5
- Agents-A1-4B vs Laguna S 2.1
- Agents-A1-4B vs LongCat-2.5
- Apertus v1.5 8B vs Laguna S 2.1
- Apertus v1.5 8B vs LongCat-2.5
- Apodex-1.1-mini vs Laguna S 2.1
- Apodex-1.1-mini vs LongCat-2.5
- Arete-27B vs Laguna S 2.1
- Arete-27B vs LongCat-2.5
- BigBang-v1 vs Laguna S 2.1
- BigBang-v1 vs LongCat-2.5
- Carbon-X vs Laguna S 2.1
- Carbon-X vs LongCat-2.5
- Cola2 vs Laguna S 2.1
- Cola2 vs LongCat-2.5
- Cosmos-Reason2-2B vs Laguna S 2.1
- Cosmos-Reason2-2B vs LongCat-2.5
- Darwin-36B-Opus vs Laguna S 2.1
- Darwin-36B-Opus vs LongCat-2.5
- DeepSeek-V3.2 vs Laguna S 2.1
- DeepSeek-V3.2 vs LongCat-2.5
- DeepSeek V4.1 Flash vs Laguna S 2.1
- DeepSeek V4.1 Flash vs LongCat-2.5
- DeepSeek-V4-Flash-0731 vs Laguna S 2.1
- DeepSeek-V4-Flash-0731 vs LongCat-2.5
- Devstral Small 2 vs Laguna S 2.1
- Devstral Small 2 vs LongCat-2.5
- DiffusionGemma 26B A4B IT vs Laguna S 2.1
- DiffusionGemma 26B A4B IT vs LongCat-2.5
- EXAONE 4.5 33B vs Laguna S 2.1
- EXAONE 4.5 33B vs LongCat-2.5
- Gemma 4 12B IT vs Laguna S 2.1
- Gemma 4 12B IT vs LongCat-2.5
- Gemma 4 26B A4B IT vs Laguna S 2.1
- Gemma 4 26B A4B IT vs LongCat-2.5
- Gemma 4 31B IT vs Laguna S 2.1
- Gemma 4 31B IT vs LongCat-2.5
- Gemma 4 E2B IT vs Laguna S 2.1
- Gemma 4 E2B IT vs LongCat-2.5
- Gemma 4 E4B IT vs Laguna S 2.1
- Gemma 4 E4B IT vs LongCat-2.5
- GLM-4.5-Air vs Laguna S 2.1
- GLM-4.5-Air vs LongCat-2.5
- GLM-4.7-Flash vs Laguna S 2.1
- GLM-4.7-Flash vs LongCat-2.5
- GLM-5.3-Flash vs Laguna S 2.1
- GLM-5.3-Flash vs LongCat-2.5
- GLM-5.3 vs Laguna S 2.1
- GLM-5.3 vs LongCat-2.5
- gpt-oss-120b vs Laguna S 2.1
- gpt-oss-120b vs LongCat-2.5
- gpt-oss-20b vs Laguna S 2.1
- gpt-oss-20b vs LongCat-2.5
- Granite 4.2 30B vs Laguna S 2.1
- Granite 4.2 30B vs LongCat-2.5
- Granite 4.2 3B vs Laguna S 2.1
- Granite 4.2 3B vs LongCat-2.5
- Granite 4.2 8B vs Laguna S 2.1
- Granite 4.2 8B vs LongCat-2.5
- Hy-Memory-30B-A3B vs Laguna S 2.1
- Hy-Memory-30B-A3B vs LongCat-2.5
- Hy3 vs Laguna S 2.1
- Hy3 vs LongCat-2.5
- Inkling-Small vs Laguna S 2.1
- Inkling-Small vs LongCat-2.5
- Inkling vs Laguna S 2.1
- Inkling vs LongCat-2.5
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Laguna S 2.1
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs LongCat-2.5
- Kimi K3.1 Pro vs Laguna S 2.1
- Kimi K3.1 Pro vs LongCat-2.5
- Kimi K3 vs Laguna S 2.1
- Kimi K3 vs LongCat-2.5
- Kolibri-1 vs Laguna S 2.1
- Kolibri-1 vs LongCat-2.5
- Kronos vs Laguna S 2.1
- Kronos vs LongCat-2.5
- Laguna M.1 vs Laguna S 2.1
- Laguna M.1 vs LongCat-2.5
- 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 MiMo-V2.6-Flash-RL
- 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 Nanbeige4.2-3B
- 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 North Mini Code 1.0
- 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 OTel-2.0-LLM-31B-IT
- 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 SenseNova-Loop-9B-Preview
- Laguna S 2.1 vs SmolLM3-3B
- Laguna S 2.1 vs Spark-X2.6-4B
- Laguna S 2.1 vs Step 3.7 Flash
- 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
- Laguna S 2.1 vs WeVisOne-VL-2B
- Laguna S 2.1 vs WeVisOne-VL-4B
- Laguna S 2.1 vs ZAYA1-8B
- Laguna XS 2.1 vs LongCat-2.5
- 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-s-2-1-vs-longcat-2-5.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/laguna-s-2-1.json·/api/v1/tools/longcat-2-5.json - From a terminal
anchor compare laguna-s-2-1 longcat-2-5(the CLI) - Over MCP
compare_tools {"a": "laguna-s-2-1", "b": "longcat-2-5"}at/mcp, no key