{ "data": { "a": { "slug": "longcat-2-5", "name": "LongCat-2.5", "vendor": "Meituan (LongCat)", "vendorUrl": "https://longcat.ai", "kind": "weights", "category": "open-weights", "summary": "LongCat-2.5 is Meituan's open-weight flagship, a multimodal mixture-of-experts model of 1.6T parameters with about 48B active per token, built for long agentic coding work. MIT licence. The weights are 3,562 GB, and the card documents deployment on NPU hardware only.", "url": "https://www.anchorterminal.com/tools/longcat-2-5", "markdownUrl": "https://www.anchorterminal.com/tools/longcat-2-5.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/longcat-2-5.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json", "dns": "longcat-2-5.grade.anchorterminal.com", "repo": "https://huggingface.co/meituan-longcat/LongCat-2.5", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "meituan-longcat/LongCat-2.5" }, { "registry": "huggingface", "name": "meituan-longcat/LongCat-2.5-W4A4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use under the MIT licence. Running it yourself is a data-centre job: 3,562 GB in BF16, or 1,336 GB for Meituan's 4-bit build, which the card says is for INT4 computation on NPU accelerators (checked 2026-11-10).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://huggingface.co/meituan-longcat/LongCat-2.5", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "agentic-coding", "mit", "safetensors", "int4", "npu", "self-hosted" ], "lastRelease": "2026-11-10", "graded": true, "anchor": { "graded": true, "score": 50.5, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 107, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 75, "reach": 35, "risk": 60, "upkeep": 46 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "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.", "bestFor": "Operators with NPU clusters, and anyone tracking what the open flagships can do", "strengths": [ "MIT licence, ungated", "Tool calling documented with a worked example", "Reasoning can be switched per request and comes back as reasoning_content", "Meituan publishes its own 4-bit build", "Updated on the day we checked" ], "weaknesses": [ "3,562 GB in BF16 and 1,336 GB at 4 bits", "No released runtime has its architecture", "The only documented deployment is on NPU hardware through Meituan's fork", "config.json names model code that isn't in the repository", "No stated context length, languages, safety results or data sources", "No inference provider serves it" ], "agentNotes": [ "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_kwargs` with `enable_thinking` true or false", "With thinking on, keep earlier reasoning in the history (`save_history_reasoning_content` true), 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" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 50.5 } ], "editorialScores": { "interface": 75, "reach": 35, "risk": 60, "upkeep": 46 }, "provenanceScore": 49, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# No released runtime loads it. Meituan's NPU instructions are in its SGLang fork:\n# github.com/meituan-longcat/SGLang-FluentLLM, branch longcat-2.5-npu, npu_test/ReadMe.md", "http": "# The card's example calls an OpenAI-compatible chat completions API with tools, and passes\n# extra_body={\"chat_template_kwargs\": {\"enable_thinking\": true, \"save_history_reasoning_content\": true}}" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/longcat-2-5" }, "weights": { "trainedBy": "Meituan (LongCat team)", "builtOn": "LongCat-2.0 and LongCat-Next, per the card", "params": "1.6T", "active": "48B", "context": 524288, "goodFor": "Long agentic coding and research runs with images, for an operator with NPU clusters. Nobody else can run it yet.", "runtimes": [], "builds": [ { "name": "LongCat-2.5-W4A4", "url": "https://huggingface.co/meituan-longcat/LongCat-2.5-W4A4", "who": "publisher", "precision": "int4", "sizeGB": 1336, "memoryGB": 2048, "format": "safetensors", "note": "Meituan's 4-bit build of the experts' weights and activations, for INT4 computation on NPU accelerators" }, { "name": "LongCat-2.5 (BF16 safetensors)", "url": "https://huggingface.co/meituan-longcat/LongCat-2.5", "who": "publisher", "precision": "bf16", "sizeGB": 3562, "memoryGB": 8192, "format": "safetensors", "note": "210 files" } ], "run": "See github.com/meituan-longcat/SGLang-FluentLLM, branch longcat-2.5-npu", "runBuild": "LongCat-2.5-W4A4", "checked": "2026-11-10", "minMemoryGB": 2048 }, "family": { "lab": "Meituan (LongCat)", "family": "LongCat-2", "url": "https://www.anchorterminal.com/open-weights/#meituan-longcat", "others": [ { "name": "LongCat-2.5-W4A4", "url": "https://huggingface.co/meituan-longcat/LongCat-2.5-W4A4" }, { "name": "LongCat-2.0", "url": "https://huggingface.co/meituan-longcat/LongCat-2.0" } ] }, "area": "models", "provenance": { "legalEntity": "Meituan, named in the card's licence section. The legal entity wasn't looked up", "domain": "longcat.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "https://huggingface.co/meituan-longcat/LongCat-2.5/blob/main/LICENSE", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/meituan-longcat/LongCat-2.5/commits/main", "securityTxt": "unknown", "checked": "2026-11-10", "notes": [ "longcat.ai has no robots.txt (the request returns a not-found message) and its pages are JavaScript applications that gave no text on 10 November 2026, so the site's own statements weren't read.", "The domain's registration date wasn't looked up.", "Nothing is hosted under this listing, so the MIT licence stands in for terms of service and no privacy policy is scored.", "The changelog is the commit history, 50 commits or more since 4 November 2026." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/longcat-2-5.json" }, "answer": "MiniMax-M2.5 scores 59 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 1 of 4 scored categories. LongCat-2.5 leads on risk and documentation and upkeep.", "b": { "slug": "minimax-m2-5", "name": "MiniMax-M2.5", "vendor": "MiniMax", "vendorUrl": "https://www.minimax.io", "kind": "weights", "category": "open-weights", "summary": "MiniMax-M2.5 is MiniMax's open-weight mixture-of-experts model for coding and agent work, about 229B parameters in FP8 with a 196,608-token context and thinking always on, published on Hugging Face on 12 February 2026.", "url": "https://www.anchorterminal.com/tools/minimax-m2-5", "markdownUrl": "https://www.anchorterminal.com/tools/minimax-m2-5.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minimax-m2-5.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minimax-m2-5.json", "dns": "minimax-m2-5.grade.anchorterminal.com", "repo": "https://huggingface.co/MiniMaxAI/MiniMax-M2.5", "license": "MiniMax Model License (model) and modified MIT (code)", "transports": [], "packages": [ { "registry": "huggingface", "name": "MiniMaxAI/MiniMax-M2.5" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included and royalty-free, under the MiniMax Model License for the weights and a modified MIT licence for the code, both with attribution duties and a Prohibited Uses Policy for the model. Running it yourself costs the hardware, 230.2 GB of FP8 files. MiniMax's API closed M2.5 to new users on 19 October 2026 and retires it on 18 November 2026 (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/MiniMaxAI/MiniMax-M2.5", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "reasoning", "tool-calling", "coding", "custom-licence", "safetensors", "fp8", "self-hosted" ], "lastRelease": "2026-02-12", "graded": true, "anchor": { "graded": true, "score": 59, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 76, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 73, "interface": 75, "reach": 65, "risk": 30, "upkeep": 37 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-04" }, "negative": 0, "verdict": "MiniMax-M2.5 is a 230B mixture-of-experts model for coding and agent work, with parallel tool calls, a 196K context and parsers in vLLM and SGLang. It needs a 4-GPU server for FP8 or about 155 GB for a 4-bit build. The main risk is a licence replaced after release with a Prohibited Uses Policy while the card still says modified MIT.", "bestFor": "Teams with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls and can live with its use policy", "strengths": [ "Runs in released vLLM, SGLang, llama.cpp, Transformers and LM Studio, and in Ollama 0.40.14 through its pinned llama.cpp", "Tool-call format documented by MiniMax with named vLLM and SGLang parsers and a parsing function", "Several tool calls in one block, and 196,608 tokens of context", "Safetensors only, in the publisher's organisation with a SHA-256 for every shard", "No gate or account, and served by two inference providers on Hugging Face" ], "weaknesses": [ "230 GB in FP8, and about 155 GB for the smallest 4-bit library build, more than a 128 GB machine", "Thinking is always on, with no documented way to turn it off or budget it", "The card says modified MIT and links to a deleted file, while the licence in the repository adds a Prohibited Uses Policy and termination for breach", "No safety evaluation, agent guidance or security contact", "No MiniMax reply in discussions since April 2026, and the card leaves out limitations, architecture, parameters and a cut-off" ], "agentNotes": [ "Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think", "In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code", "MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache", "On a single large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.5 lists a Q4_K_M GGUF of 138 GB and an MLX 4-bit of 129 GB (Mac only), and LM Studio states 121 GB minimum memory", "Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends", "Budget output tokens for thinking on every turn, since the template always opens a think block", "Read LICENSE-MODEL, not the card's licence link, before shipping, and keep the required Notice text in every copy", "Without the hardware, call it through Novita or Featherless AI; MiniMax's own API retires M2.5 on 18 November 2026" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 1.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 59 } ], "editorialScores": { "capability": 73, "interface": 75, "reach": 65, "risk": 30, "upkeep": 37 }, "provenanceScore": 60, "checked": { "categories": { "capability": "2026-11-04", "interface": "2026-11-04", "reach": "2026-11-04", "risk": "2026-11-04", "upkeep": "2026-11-04" }, "last": "2026-11-04", "lastMoved": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# MiniMax's vLLM guide, 4 GPUs\nuv pip install vllm --torch-backend=auto\nSAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code \\\n --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 \\\n --reasoning-parser minimax_m2_append_think\n# or, with llama.cpp and LM Studio's GGUF:\nllama-server -hf lmstudio-community/MiniMax-M2.5-GGUF:Q4_K_M --jinja", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"MiniMaxAI/MiniMax-M2.5\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"temperature\": 1.0, \"top_p\": 0.95, \"top_k\": 40}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/minimax-m2-5" }, "sameCompany": [ "minimax-api", "minimax-video", "minimax-stt", "minimax-speech", "minimax-voice-cloning", "minimax-code", "minimax-m2-7", "minimax-m3-1-flash" ], "weights": { "trainedBy": "MiniMax", "params": "228.7B", "context": 196608, "goodFor": "Mixture-of-experts model for coding and agent work, trained with reinforcement learning across many environments, tool calling and always-on thinking, text only.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "lmstudio-community MiniMax-M2.5-GGUF (Q4_K_M)", "url": "https://huggingface.co/lmstudio-community/MiniMax-M2.5-GGUF", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 138.34, "memoryGB": 192, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "From lmstudio-community, LM Studio's organisation on the Hub. 138.34 GB of Q4_K_M weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet" }, { "name": "lmstudio-community MiniMax-M2.5-GGUF (Q6_K)", "url": "https://huggingface.co/lmstudio-community/MiniMax-M2.5-GGUF", "who": "lmstudio", "precision": "q6_k", "sizeGB": 187.76, "memoryGB": 256, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "From lmstudio-community, LM Studio's organisation on the Hub. 187.76 GB of Q6_K weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet" }, { "name": "MiniMax-M2.5", "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.5", "who": "publisher", "precision": "fp8", "sizeGB": 230.1, "memoryGB": 512, "format": "safetensors", "note": "Native FP8; the only build MiniMax publishes" }, { "name": "lmstudio-community MiniMax-M2.5-GGUF (Q8_0)", "url": "https://huggingface.co/lmstudio-community/MiniMax-M2.5-GGUF", "who": "lmstudio", "precision": "q8_0", "sizeGB": 243.14, "memoryGB": 512, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "From lmstudio-community, LM Studio's organisation on the Hub. 243.14 GB of Q8_0 weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet" } ], "run": "SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think", "runBuild": "MiniMax-M2.5", "checked": "2026-11-04", "minMemoryGB": 192 }, "family": { "lab": "MiniMax", "family": "MiniMax-M2", "url": "https://www.anchorterminal.com/open-weights/#minimax", "others": [ { "name": "MiniMax-M2", "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2", "params": "230B total, 10B active" }, { "name": "MiniMax-M2.1", "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.1", "params": "230B total, 10B active (Hub count; card doesn't restate)" }, { "name": "MiniMax-M2.7", "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.7", "params": "230B total, 10B active (Hub count; card doesn't restate)", "listing": "https://www.anchorterminal.com/tools/minimax-m2-7" } ], "olderCount": 7, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "MiniMax AI (named in the licences; MiniMax's platform terms name Nanonoble Pte. Ltd., Singapore)", "domain": "minimax.io", "domainRegistered": "2021-03-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The MiniMax Model License and the MIT code licence name MiniMax AI. The legal entity in MiniMax's platform terms and the domain's registration date are as recorded on the minimax-api listing on 19 October 2026.", "Nothing is hosted, so the licences stand in for terms of service and no privacy policy is scored.", "No changelog. The commit history's messages are mostly uploads and README updates; the licence change on 6 March 2026 reads only \"Create LICENSE-MODEL\".", "www.minimax.io/.well-known/security.txt redirects to the home page on 21 October 2026, so there is no security.txt.", "MiniMax's site (www.minimax.io/models/text) links to huggingface.co/MiniMaxAI/MiniMax-M2.5, so the repository is the publisher's own." ], "score": 60 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/minimax-m2-5.json", "live": { "slug": "minimax-m2-5", "securityTxt": { "url": "https://minimax.io/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:08:11.844974518Z" }, "domain": { "domain": "minimax.io", "checkedAt": "2026-11-08T14:54:44.54388672Z" }, "updatedAt": "2026-11-10T16:08:11.844974518Z" } }, "edges": [ { "by": 30, "category": "reach", "leads": "minimax-m2-5", "name": "Reach", "reasons": { "a": "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.", "b": "Memory earns 5. MiniMax only publishes its 230 GB FP8 release, and the smallest build of 4-bit or better from a runtime's own library is LM Studio's MLX 4-bit at 128.7 GB, about 154 GB with the fifth added, so it needs more than our 107.4 GB machine and fits within 256 GB. Runtimes earn the full 25, with model code in released vLLM v0.33.0, SGLang v0.5.23, llama.cpp and Transformers, an LM Studio catalogue entry, and Ollama 0.40.14, whose pinned llama.cpp b11963 has the minimax-m2 architecture, though Ollama's library lists no build. Builds earn 10 for one publisher precision, the FP8 release. Terms earn 10 for a custom licence that allows free commercial use under a use policy. Download earns 10, with no gate or account. Hosted earns 5, since Novita and Featherless AI serve it through Hugging Face, while MiniMax's own API is closed to new users and retires it on 18 November 2026. 5+25+10+10+10+5 = 65." }, "scores": { "a": 35, "b": 65 } }, { "by": 30, "category": "risk", "leads": "longcat-2-5", "name": "Risk", "reasons": { "a": "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.", "b": "File safety earns 15, safetensors only with no pickle files, less 10 because every documented loading route passes trust_remote_code for the repository's Python. Provenance earns the full 15, files in the organisation MiniMax links to with a SHA-256 for each shard. Safety evaluation and agent guidance earn nothing, as MiniMax publishes neither. Licence risk earns nothing (read 21 October 2026). The Prohibited Uses Policy goes beyond the law, MiniMax can end the licence for any breach and replaced its terms three weeks after release, and that change on 6 March 2026 came with no dated note while the card still links to a deleted file. The security programme earns nothing, with no security.txt, disclosure policy, SECURITY.md or advisories, and only a general contact address on the card." }, "scores": { "a": 60, "b": 30 } }, { "by": 9, "category": "upkeep", "leads": "longcat-2-5", "name": "Documentation and upkeep", "reasons": { "a": "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.", "b": "The model card earns 2, stating only intended use and leaving out limitations, architecture, parameters, context, natural languages and a cut-off. Training data earns 5 for a general description with no cut-off. The last change earns 20, a card edit on 26 October 2026 (commit 2515a3ea, a link renamed from MiniMax Agent to MiniMax Code), 9 days before this check; the files, template and code are unchanged since February. Responsiveness earns 5, with a MiniMax reply in 2 of the 40 most recent discussions and none since April. Version policy earns 5, since earlier and later models sit in their own repositories without dated notes. File health earns nothing, because the card's licence metadata still contradicts the licence in the files and an FP8 output report still waits without a reply. 2+5+20+5+5+0 = 37." }, "scores": { "a": 46, "b": 37 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Meituan (LongCat)", "b": "MiniMax", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "MIT", "b": "MiniMax Model License (model) and modified MIT (code)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-11-10", "b": "2026-02-12", "name": "Last release" }, { "a": "not read yet", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "none", "b": "1.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "MiniMax-M2.5 scores 59 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 1 of 4 scored categories. LongCat-2.5 leads on risk and documentation and upkeep.", "question": "Which is better for AI agents, LongCat-2.5 or MiniMax-M2.5?" } ], "goodFor": [ { "aheadOn": [ "Risk, 60 against 30", "Documentation and upkeep, 46 against 37" ], "also": null, "goodFor": "Operators with NPU clusters, and anyone tracking what the open flagships can do", "slug": "longcat-2-5", "watchFor": "3,562 GB in BF16 and 1,336 GB at 4 bits" }, { "aheadOn": [ "Reach, 65 against 35" ], "also": null, "goodFor": "Teams with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls and can live with its use policy", "slug": "minimax-m2-5", "watchFor": "230 GB in FP8, and about 155 GB for the smallest 4-bit library build, more than a 128 GB machine" } ], "history": { "longcat-2-5": null, "minimax-m2-5": [ { "causes": [ "product" ], "date": "2026-11-04", "delta": 1.5, "from": 57.5, "fromGrade": "C", "to": 59, "toGrade": "C" } ] }, "job": { "capability": "model.open-weights", "name": 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"https://www.anchorterminal.com/compare/minimax-m2-5-vs-sensenova-loop-9b-preview.json", "title": "MiniMax-M2.5 vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-smollm3-3b.json", "title": "MiniMax-M2.5 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-spark-x2-6-4b.json", "title": "MiniMax-M2.5 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-step-3-7-flash.json", "title": "MiniMax-M2.5 vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-27b.json", "title": "MiniMax-M2.5 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-flash-next.json", "title": "MiniMax-M2.5 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-thinkingcap-qwen3-8-27b.json", "title": "MiniMax-M2.5 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-unlimited-ocr-2.json", "title": "MiniMax-M2.5 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-wevisone-vl-2b.json", "title": "MiniMax-M2.5 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-wevisone-vl-4b.json", "title": "MiniMax-M2.5 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-zaya1-8b.json", "title": "MiniMax-M2.5 vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 30, "edge": "minimax-m2-5", "key": "reach", "longcat-2-5": 35, "minimax-m2-5": 65, "name": "Reach", "weight": 25 }, { "by": 30, "edge": "longcat-2-5", "key": "risk", "longcat-2-5": 60, "minimax-m2-5": 30, "name": "Risk", "weight": 20 }, { "by": 0, "edge": "", "key": "interface", "longcat-2-5": 75, "minimax-m2-5": 75, "name": "Agent interface", "weight": 10 }, { "by": 9, "edge": "longcat-2-5", "key": "upkeep", "longcat-2-5": 46, "minimax-m2-5": 37, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "MiniMax-M2.5 scores 59 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 1 of 4 scored categories. LongCat-2.5 leads on risk and documentation and upkeep. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "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.", "minimax-m2-5": "MiniMax-M2.5 is a 230B mixture-of-experts model for coding and agent work, with parallel tool calls, a 196K context and parsers in vLLM and SGLang. It needs a 4-GPU server for FP8 or about 155 GB for a 4-bit build. The main risk is a licence replaced after release with a Prohibited Uses Policy while the card still says modified MIT." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minimax-m2-5", "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minimax-m2-5.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minimax-m2-5.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minimax-m2-5.min.md" }, "markdown": "MiniMax-M2.5 scores 59 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 1 of 4 scored categories. LongCat-2.5 leads on risk and documentation and upkeep. Both do open-weight models you run yourself.\n\n- LongCat-2.5: grade D, 50.5/100, rank #145 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/longcat-2-5.md · JSON https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json\n- MiniMax-M2.5: grade C, 59/100, rank #99 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/minimax-m2-5.md · JSON https://www.anchorterminal.com/api/v1/tools/minimax-m2-5.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### LongCat-2.5 (D)\n\nGood for: Operators with NPU clusters, and anyone tracking what the open flagships can do\n\nAhead on:\n- Risk, 60 against 30\n- Documentation and upkeep, 46 against 37\n\nWatch for: 3,562 GB in BF16 and 1,336 GB at 4 bits\n\n### MiniMax-M2.5 (C)\n\nGood for: Teams with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls and can live with its use policy\n\nAhead on:\n- Reach, 65 against 35\n\nWatch for: 230 GB in FP8, and about 155 GB for the smallest 4-bit library build, more than a 128 GB machine\n\n\n## Why the edge\n\n### Reach: MiniMax-M2.5 +30\n\n- 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.\n- MiniMax-M2.5 (65): Memory earns 5. MiniMax only publishes its 230 GB FP8 release, and the smallest build of 4-bit or better from a runtime's own library is LM Studio's MLX 4-bit at 128.7 GB, about 154 GB with the fifth added, so it needs more than our 107.4 GB machine and fits within 256 GB. Runtimes earn the full 25, with model code in released vLLM v0.33.0, SGLang v0.5.23, llama.cpp and Transformers, an LM Studio catalogue entry, and Ollama 0.40.14, whose pinned llama.cpp b11963 has the minimax-m2 architecture, though Ollama's library lists no build. Builds earn 10 for one publisher precision, the FP8 release. Terms earn 10 for a custom licence that allows free commercial use under a use policy. Download earns 10, with no gate or account. Hosted earns 5, since Novita and Featherless AI serve it through Hugging Face, while MiniMax's own API is closed to new users and retires it on 18 November 2026. 5+25+10+10+10+5 = 65.\n\n### Risk: LongCat-2.5 +30\n\n- 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.\n- MiniMax-M2.5 (30): File safety earns 15, safetensors only with no pickle files, less 10 because every documented loading route passes trust_remote_code for the repository's Python. Provenance earns the full 15, files in the organisation MiniMax links to with a SHA-256 for each shard. Safety evaluation and agent guidance earn nothing, as MiniMax publishes neither. Licence risk earns nothing (read 21 October 2026). The Prohibited Uses Policy goes beyond the law, MiniMax can end the licence for any breach and replaced its terms three weeks after release, and that change on 6 March 2026 came with no dated note while the card still links to a deleted file. The security programme earns nothing, with no security.txt, disclosure policy, SECURITY.md or advisories, and only a general contact address on the card.\n\n### Documentation and upkeep: LongCat-2.5 +9\n\n- 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.\n- MiniMax-M2.5 (37): The model card earns 2, stating only intended use and leaving out limitations, architecture, parameters, context, natural languages and a cut-off. Training data earns 5 for a general description with no cut-off. The last change earns 20, a card edit on 26 October 2026 (commit 2515a3ea, a link renamed from MiniMax Agent to MiniMax Code), 9 days before this check; the files, template and code are unchanged since February. Responsiveness earns 5, with a MiniMax reply in 2 of the 40 most recent discussions and none since April. Version policy earns 5, since earlier and later models sit in their own repositories without dated notes. File health earns nothing, because the card's licence metadata still contradicts the licence in the files and an FP8 output report still waits without a reply. 2+5+20+5+5+0 = 37.\n\n## Score by category\n\n| Category | Weight | LongCat-2.5 | MiniMax-M2.5 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 35 | 65 | MiniMax-M2.5 +30 |\n| Risk | 20% (20 this run) | 60 | 30 | LongCat-2.5 +30 |\n| Agent interface | 10% (10 this run) | 75 | 75 | even |\n| Documentation and upkeep | 10% (10 this run) | 46 | 37 | LongCat-2.5 +9 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **50.5 · D** | **59 · C** | |\n\n## Facts side by side\n\n| Fact | LongCat-2.5 | MiniMax-M2.5 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Meituan (LongCat) | MiniMax |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | MIT | MiniMax Model License (model) and modified MIT (code) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-10 | 2026-02-12 |\n| Terms last updated | not read yet | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 1.8/5 (4) |\n\n## How the scores have moved\n\n### LongCat-2.5\n\nNo score change since it was listed.\n\n### MiniMax-M2.5\n\n- +1.5 on 4 November 2026: product\n\n## Verdicts\n\n**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.\n\n**MiniMax-M2.5.** MiniMax-M2.5 is a 230B mixture-of-experts model for coding and agent work, with parallel tool calls, a 196K context and parsers in vLLM and SGLang. It needs a 4-GPU server for FP8 or about 155 GB for a 4-bit build. The main risk is a licence replaced after release with a Prohibited Uses Policy while the card still says modified MIT.\n\n## Before you call either\n\n### LongCat-2.5\n\n1. You can't run this on a workstation or a single GPU server. Plan for a provider, and none serves it yet\n2. If an operator serves it, call it as an OpenAI-compatible chat API with tools in the function format\n3. Pass `chat_template_kwargs` with `enable_thinking` true or false\n4. With thinking on, keep earlier reasoning in the history (`save_history_reasoning_content` true), as the card recommends\n5. The card's results use temperature 1.0 and top_p 0.95\n6. Its benchmark table is Meituan's own. The unmarked scores are in-house\n\n### MiniMax-M2.5\n\n1. Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think\n2. In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code\n3. MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache\n4. On a single large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.5 lists a Q4_K_M GGUF of 138 GB and an MLX 4-bit of 129 GB (Mac only), and LM Studio states 121 GB minimum memory\n5. Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends\n6. Budget output tokens for thinking on every turn, since the template always opens a think block\n7. Read LICENSE-MODEL, not the card's licence link, before shipping, and keep the required Notice text in every copy\n8. Without the hardware, call it through Novita or Featherless AI; MiniMax's own API retires M2.5 on 18 November 2026\n\n## Questions\n\n### Which is better for AI agents, LongCat-2.5 or MiniMax-M2.5?\n\nMiniMax-M2.5 scores 59 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 1 of 4 scored categories. LongCat-2.5 leads on risk and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/longcat-2-5-vs-minimax-m2-5.json, and with the fewest tokens: https://www.anchorterminal.com/compare/longcat-2-5-vs-minimax-m2-5.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"longcat-2-5\", \"b\": \"minimax-m2-5\"}`. From a terminal: `anchor compare longcat-2-5 minimax-m2-5`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json and https://www.anchorterminal.com/api/v1/tools/minimax-m2-5.json\n\n## Other comparisons with LongCat-2.5 or MiniMax-M2.5\n\n- [Agents-A1-4B vs LongCat-2.5](https://www.anchorterminal.com/compare/agents-a1-4b-vs-longcat-2-5.md)\n- [Agents-A1-4B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-5.md)\n- [Apertus v1.5 8B vs LongCat-2.5](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-longcat-2-5.md)\n- [Apertus v1.5 8B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-5.md)\n- [Apodex-1.1-mini vs LongCat-2.5](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-longcat-2-5.md)\n- [Apodex-1.1-mini vs MiniMax-M2.5](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-5.md)\n- [Arete-27B vs LongCat-2.5](https://www.anchorterminal.com/compare/arete-27b-vs-longcat-2-5.md)\n- [Arete-27B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/arete-27b-vs-minimax-m2-5.md)\n- [BigBang-v1 vs LongCat-2.5](https://www.anchorterminal.com/compare/bigbang-v1-vs-longcat-2-5.md)\n- [BigBang-v1 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-5.md)\n- [Carbon-X vs LongCat-2.5](https://www.anchorterminal.com/compare/carbon-x-vs-longcat-2-5.md)\n- [Carbon-X vs MiniMax-M2.5](https://www.anchorterminal.com/compare/carbon-x-vs-minimax-m2-5.md)\n- [Cola2 vs LongCat-2.5](https://www.anchorterminal.com/compare/cola2-vs-longcat-2-5.md)\n- [Cola2 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/cola2-vs-minimax-m2-5.md)\n- [Cosmos-Reason2-2B vs LongCat-2.5](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-longcat-2-5.md)\n- [Cosmos-Reason2-2B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minimax-m2-5.md)\n- 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