{ "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": "MiniCPM-V 4.7 1B 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 agent interface.", "b": { "slug": "minicpm-v-4-7-1b", "name": "MiniCPM-V 4.7 1B", "vendor": "OpenBMB", "vendorUrl": "https://openbmb.cn", "kind": "weights", "category": "open-weights", "summary": "MiniCPM-V 4.7 1B is OpenBMB's small open-weight vision-language model, 1.55B parameters, that reads images and video on a phone or laptop, calls tools, and ships in BF16, 4-bit and GGUF builds under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/minicpm-v-4-7-1b", "markdownUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-1b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-1b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-1b.json", "dns": "minicpm-v-4-7-1b.grade.anchorterminal.com", "repo": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-1B" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-1B-gguf" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-1B-AWQ" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-1B-GPTQ" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-1B-int4" }, { "registry": "ollama", "name": "openbmb/minicpm-v4.7:1b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and OpenBMB's GGUF, AWQ, GPTQ and int4 repositories are not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, named in the card's metadata and carried as a LICENSE file in OpenBMB's GitHub repository. Running it yourself costs the hardware, 3.1 GB for the BF16 file or about 1.5 GB for the IQ4_XS GGUF with its projector (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/openbmb/MiniCPM-V-4.7-1B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "multimodal", "vision", "video", "on-device", "small", "tool-calling", "apache-2.0", "safetensors", "gguf", "awq", "gptq", "self-hosted" ], "lastRelease": "2026-10-30", "graded": true, "anchor": { "graded": true, "score": 61, "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": 60, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 70, "reach": 95, "risk": 55, "upkeep": 48 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A vision-language cousin small enough for a phone, under plain Apache 2.0, that every runtime we count can load. The projector is bigger than the language model, so plan for about 2 GB. The card says nothing about limits or training data and doesn't show how a tool call comes back.", "bestFor": "Agents that need a cheap local model to read screenshots, photos and short video on a phone, a laptop or beside a bigger model", "strengths": [ "Runs in about 2 GB from OpenBMB's own GGUF with its projector", "Runs in llama.cpp, Ollama, vLLM, SGLang and Transformers from their released versions", "Plain Apache 2.0, ungated, safetensors and GGUF only, no remote code", "Images, several images and video in one small model, with thinking off by default", "OpenBMB publishes 4-bit builds, a GGUF repository and a speculative-decoding head itself" ], "weaknesses": [ "No limitations section, safety results, agent-use guidance or security contact", "Training data isn't described, and the card gives no context length, languages or knowledge cut-off", "The card shows a tool-calling request but not the reply, and names no parser", "Nobody has asked a question on the repository yet, so there's no record of OpenBMB answering for this size", "No inference provider is mapped on Hugging Face" ], "agentNotes": [ "Start it with `llama-server -hf openbmb/MiniCPM-V-4.7-1B-gguf:Q4_K_M --jinja`, or `ollama run openbmb/minicpm-v4.7:1b`", "For image input in llama.cpp add `mmproj-model-f16.gguf`, or the smaller 4x projector", "The GGUF files at the repository root have thinking off. Use the files in `thinking/` when you want the trace", "Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends", "Check tool calls in your runtime first, since the card names no parser", "Validate JSON output yourself, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 70, "reach": 95, "risk": 55, "upkeep": 48 }, "provenanceScore": 58, "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": "ollama run openbmb/minicpm-v4.7:1b\n# or, with llama.cpp and OpenBMB's own GGUF:\nllama-server -hf openbmb/MiniCPM-V-4.7-1B-gguf:Q4_K_M --jinja\n# or, with Transformers' server, as the card gives it:\npip install \"transformers[serving]\u003e=5.18.0\"\ntransformers serve openbmb/MiniCPM-V-4.7-1B --port 8000 --host 0.0.0.0", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"openbmb/minicpm-v4.7:1b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/minicpm-v-4-7-1b" }, "sameCompany": [ "minicpm-v-4-7-35b-a3b", "minicpm-o-4-5", "minicpm5-2b" ], "weights": { "trainedBy": "OpenBMB (ModelBest and Tsinghua NLP Lab)", "builtOn": "Qwen3.5 and Qwen3.6 as the language backbone and SigLIP2 as the vision module, per the card's acknowledgements. Not declared in the metadata. The text config is qwen3_5_text with 24 layers", "params": "1.55B", "context": 262144, "goodFor": "Small vision-language model for images, several images and video on a phone or a laptop, with tool calling and thinking off by default.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "MiniCPM-V-4.7-1B", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B", "who": "publisher", "precision": "bf16", "sizeGB": 3.11, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ] }, { "name": "MiniCPM-V-4.7-1B-AWQ", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-AWQ", "who": "publisher", "precision": "w4a16", "sizeGB": 2.37, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM" ], "note": "AWQ, 4-bit weights with 16-bit activations" }, { "name": "MiniCPM-V-4.7-1B-GPTQ", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-GPTQ", "who": "publisher", "precision": "w4a16", "sizeGB": 1.87, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM" ], "note": "GPTQ, 4-bit weights with 16-bit activations" }, { "name": "MiniCPM-V-4.7-1B-int4", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-int4", "who": "publisher", "precision": "int4", "sizeGB": 1.57, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM" ] }, { "name": "MiniCPM-V-4_7-1B-F16", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/blob/main/MiniCPM-V-4_7-1B-F16.gguf", "who": "publisher", "precision": "f16", "sizeGB": 3.13, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "2.02 GB of weights plus the 1.11 GB projector" }, { "name": "MiniCPM-V-4_7-1B-Q8_0", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/blob/main/MiniCPM-V-4_7-1B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 2.19, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "1.08 GB of weights plus the 1.11 GB projector" }, { "name": "MiniCPM-V-4_7-1B-Q6_K", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/blob/main/MiniCPM-V-4_7-1B-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 1.95, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "0.84 GB of weights plus the 1.11 GB projector" }, { "name": "MiniCPM-V-4_7-1B-Q4_K_M", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/blob/main/MiniCPM-V-4_7-1B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 1.78, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "0.67 GB of weights plus mmproj-model-f16.gguf (1.11 GB), or 1.57 GB with the 4x projector (0.90 GB). The same file with thinking on is in the thinking/ folder" }, { "name": "MiniCPM-V-4_7-1B-IQ4_XS", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/blob/main/MiniCPM-V-4_7-1B-IQ4_XS.gguf", "who": "publisher", "precision": "iq4_xs", "sizeGB": 1.54, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "0.64 GB of weights plus the 0.90 GB 4x projector, the smallest at 4 bits. The IQ3 files are below the 4-bit line and are not listed" }, { "name": "ollama openbmb/minicpm-v4.7:1b", "url": "https://ollama.com/openbmb/minicpm-v4.7", "who": "publisher", "precision": "q4_k_m", "sizeGB": 1.8, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "OpenBMB's own namespace on Ollama, not Ollama's library. 1.8 GB as the tags page shows it, with q8_0 (2.2 GB) and fp16 (3.1 GB) tags beside it" } ], "run": "llama-server -hf openbmb/MiniCPM-V-4.7-1B-gguf:Q4_K_M --jinja", "runBuild": "MiniCPM-V-4_7-1B-Q4_K_M", "checked": "2026-11-10", "minMemoryGB": 8 }, "family": { "lab": "OpenBMB (MiniCPM)", "family": "MiniCPM-V 4", "url": "https://www.anchorterminal.com/open-weights/#openbmb", "others": [ { "name": "MiniCPM-V 4.7 35B-A3B", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B", "params": "35.2B total, 3B active (MoE; 35,212,875,824 per the safetensors count)", "listing": "https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b" } ], "olderCount": 22 }, "area": "models", "provenance": { "legalEntity": "OpenBMB (named in the GitHub LICENSE's copyright line)", "domain": "openbmb.cn", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/OpenBMB/MiniCPM-V", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "No legal entity is named on the card. OpenBMB is the open-source organisation of ModelBest and Tsinghua University's NLP lab, as the MiniCPM5-2B listing records.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the GitHub README's dated news list (the release on 12 October 2026, DSpark heads on 15 October) and the repositories' commit messages, which say what changed.", "openbmb.cn/.well-known/security.txt returns 404 and github.com/OpenBMB/MiniCPM-V has no SECURITY.md.", "unchecked: the domain's registration date." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-1b.json" }, "edges": [ { "by": 60, "category": "reach", "leads": "minicpm-v-4-7-1b", "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 30, OpenBMB's own IQ4_XS GGUF is 0.64 GB, 1.54 GB with the 0.90 GB vision projector, and 1.54 GB × 1.2 = 1.9 GB, which fits 16 GB with room to spare. Runtimes 25, MiniCPMV4_7ForConditionalGeneration in vLLM's registry at v0.33.0, the minicpmv4_7 model type in SGLang's minicpmv.py at v0.5.23, the minicpmv4_7 model in Transformers at v5.22.2, a MiniCPM-V 4.7 guide and conversion code in llama.cpp at b12242, and an openbmb/minicpm-v4.7:1b tag in OpenBMB's namespace on Ollama, read as the 35B-A3B listing's is. Builds 15, OpenBMB publishes AWQ, GPTQ and int4 safetensors builds and a GGUF repository from IQ3_XS to F16, so quantised builds at two precisions or more. Terms 15, Apache 2.0 in the card's metadata and as the LICENSE of OpenBMB's MiniCPM-V repository, an OSI licence. Download 10, gated is false on the main repository and on the GGUF, AWQ, GPTQ and int4 repositories, so the files download with no account or terms. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026), and no provider's own list was read. 30 + 25 + 15 + 15 + 10 + 0 = 95." }, "scores": { "a": 35, "b": 95 } }, { "by": 5, "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 25, safetensors in the main repository and the AWQ, GPTQ and int4 builds, GGUF in the GGUF build, with no .bin, .pt, .pth or .py files in the main repository. Provenance 15, files in the openbmb organisation that OpenBMB's GitHub README links to (10), with a SHA-256 for both large files in the main repository (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or disclaimer section, and no prompt-injection guidance, instruction hierarchy or statement of trained refusals. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, nothing is added on the card, and the LICENSE in OpenBMB's GitHub repository resolved on 10 November 2026. Security programme 0, no SECURITY.md in OpenBMB/MiniCPM-V and no security.txt on openbmb.cn (both 404 on 10 November 2026), and no advisories found. 25 + 15 + 0 + 0 + 15 + 0 = 55." }, "scores": { "a": 60, "b": 55 } }, { "by": 5, "category": "interface", "leads": "longcat-2-5", "name": "Agent interface", "reasons": { "a": "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.", "b": "Chat template 20, chat_template.jinja in the repository, 7,249 characters. Tool-calling format 10, the card shows tools declared in the OpenAI tools array in a request to Transformers' server, but not how a call comes back, and names no parser for vLLM or SGLang. Trained tool calling 20, the template loops over tool_calls, so it renders several calls in one assistant turn, and the card gives a tool-calling request as a supported use. Structured output 0, Neither JSON nor JSON Schema output is documented or claimed for the model on the card. Reasoning control 10, the card passes enable_thinking per request in its examples, the template has been off by default since 19 October 2026, and the GGUF repository keeps thinking builds in their own folder. Context 10, 262,144 tokens, max_position_embeddings in the text config. 20 + 10 + 20 + 0 + 10 + 10 = 70." }, "scores": { "a": 75, "b": 70 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Meituan (LongCat)", "b": "OpenBMB", "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": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-11-10", "b": "2026-10-30", "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" } ], "faq": [ { "answer": "MiniCPM-V 4.7 1B 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 agent interface.", "question": "Which is better for AI agents, LongCat-2.5 or MiniCPM-V 4.7 1B?" } ], "goodFor": [ { "aheadOn": [ "Risk, 60 against 55", "Agent interface, 75 against 70" ], "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, 95 against 35" ], "also": null, "goodFor": "Agents that need a cheap local model to read screenshots, photos and short video on a phone, a laptop or beside a bigger model", "slug": "minicpm-v-4-7-1b", "watchFor": "No limitations section, safety results, agent-use guidance or security contact" } ], "history": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-longcat-2-5.json", "title": "Agents-A1-4B vs LongCat-2.5", "url": 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"title": "MiniCPM-V 4.7 1B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-5-4b.json", "title": "MiniCPM-V 4.7 1B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-5-9b.json", "title": "MiniCPM-V 4.7 1B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-6-27b.json", "title": "MiniCPM-V 4.7 1B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-6-35b-a3b.json", "title": "MiniCPM-V 4.7 1B vs Qwen3.6-35B-A3B", "url": 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"https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "MiniCPM-V 4.7 1B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-2b-instruct.json", "title": "MiniCPM-V 4.7 1B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "MiniCPM-V 4.7 1B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-32b-instruct.json", "title": "MiniCPM-V 4.7 1B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-4b-instruct.json", "title": "MiniCPM-V 4.7 1B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-8b-instruct.json", "title": "MiniCPM-V 4.7 1B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwopus3-8-27b-flash-v2.json", "title": "MiniCPM-V 4.7 1B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-recursive-flint.json", "title": "MiniCPM-V 4.7 1B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-ripple-65b-preview.json", "title": "MiniCPM-V 4.7 1B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-sensenova-loop-9b-preview.json", "title": "MiniCPM-V 4.7 1B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-smollm3-3b.json", "title": "MiniCPM-V 4.7 1B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-spark-x2-6-4b.json", "title": "MiniCPM-V 4.7 1B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-step-3-7-flash.json", "title": "MiniCPM-V 4.7 1B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-swift-1-5-qwen3-8-27b.json", "title": "MiniCPM-V 4.7 1B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "MiniCPM-V 4.7 1B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-thinkingcap-qwen3-8-27b.json", "title": "MiniCPM-V 4.7 1B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-unlimited-ocr-2.json", "title": "MiniCPM-V 4.7 1B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-2b.json", "title": "MiniCPM-V 4.7 1B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-4b.json", "title": "MiniCPM-V 4.7 1B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-zaya1-8b.json", "title": "MiniCPM-V 4.7 1B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 60, "edge": "minicpm-v-4-7-1b", "key": "reach", "longcat-2-5": 35, "minicpm-v-4-7-1b": 95, "name": "Reach", "weight": 25 }, { "by": 5, "edge": "longcat-2-5", "key": "risk", "longcat-2-5": 60, "minicpm-v-4-7-1b": 55, "name": "Risk", "weight": 20 }, { "by": 5, "edge": "longcat-2-5", "key": "interface", "longcat-2-5": 75, "minicpm-v-4-7-1b": 70, "name": "Agent interface", "weight": 10 }, { "by": 2, "edge": "minicpm-v-4-7-1b", "key": "upkeep", "longcat-2-5": 46, "minicpm-v-4-7-1b": 48, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "MiniCPM-V 4.7 1B 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 agent interface. 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.", "minicpm-v-4-7-1b": "A vision-language cousin small enough for a phone, under plain Apache 2.0, that every runtime we count can load. The projector is bigger than the language model, so plan for about 2 GB. The card says nothing about limits or training data and doesn't show how a tool call comes back." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minicpm-v-4-7-1b", "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minicpm-v-4-7-1b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minicpm-v-4-7-1b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minicpm-v-4-7-1b.min.md" }, "markdown": "MiniCPM-V 4.7 1B 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 agent interface. 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- MiniCPM-V 4.7 1B: grade C, 61/100, rank #76 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm-v-4-7-1b.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-1b.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 55\n- Agent interface, 75 against 70\n\nWatch for: 3,562 GB in BF16 and 1,336 GB at 4 bits\n\n### MiniCPM-V 4.7 1B (C)\n\nGood for: Agents that need a cheap local model to read screenshots, photos and short video on a phone, a laptop or beside a bigger model\n\nAhead on:\n- Reach, 95 against 35\n\nWatch for: No limitations section, safety results, agent-use guidance or security contact\n\n\n## Why the edge\n\n### Reach: MiniCPM-V 4.7 1B +60\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- MiniCPM-V 4.7 1B (95): Memory 30, OpenBMB's own IQ4_XS GGUF is 0.64 GB, 1.54 GB with the 0.90 GB vision projector, and 1.54 GB × 1.2 = 1.9 GB, which fits 16 GB with room to spare. Runtimes 25, MiniCPMV4_7ForConditionalGeneration in vLLM's registry at v0.33.0, the minicpmv4_7 model type in SGLang's minicpmv.py at v0.5.23, the minicpmv4_7 model in Transformers at v5.22.2, a MiniCPM-V 4.7 guide and conversion code in llama.cpp at b12242, and an openbmb/minicpm-v4.7:1b tag in OpenBMB's namespace on Ollama, read as the 35B-A3B listing's is. Builds 15, OpenBMB publishes AWQ, GPTQ and int4 safetensors builds and a GGUF repository from IQ3_XS to F16, so quantised builds at two precisions or more. Terms 15, Apache 2.0 in the card's metadata and as the LICENSE of OpenBMB's MiniCPM-V repository, an OSI licence. Download 10, gated is false on the main repository and on the GGUF, AWQ, GPTQ and int4 repositories, so the files download with no account or terms. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026), and no provider's own list was read. 30 + 25 + 15 + 15 + 10 + 0 = 95.\n\n### Risk: LongCat-2.5 +5\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- MiniCPM-V 4.7 1B (55): File safety 25, safetensors in the main repository and the AWQ, GPTQ and int4 builds, GGUF in the GGUF build, with no .bin, .pt, .pth or .py files in the main repository. Provenance 15, files in the openbmb organisation that OpenBMB's GitHub README links to (10), with a SHA-256 for both large files in the main repository (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or disclaimer section, and no prompt-injection guidance, instruction hierarchy or statement of trained refusals. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, nothing is added on the card, and the LICENSE in OpenBMB's GitHub repository resolved on 10 November 2026. Security programme 0, no SECURITY.md in OpenBMB/MiniCPM-V and no security.txt on openbmb.cn (both 404 on 10 November 2026), and no advisories found. 25 + 15 + 0 + 0 + 15 + 0 = 55.\n\n### Agent interface: LongCat-2.5 +5\n\n- 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.\n- MiniCPM-V 4.7 1B (70): Chat template 20, chat_template.jinja in the repository, 7,249 characters. Tool-calling format 10, the card shows tools declared in the OpenAI tools array in a request to Transformers' server, but not how a call comes back, and names no parser for vLLM or SGLang. Trained tool calling 20, the template loops over tool_calls, so it renders several calls in one assistant turn, and the card gives a tool-calling request as a supported use. Structured output 0, Neither JSON nor JSON Schema output is documented or claimed for the model on the card. Reasoning control 10, the card passes enable_thinking per request in its examples, the template has been off by default since 19 October 2026, and the GGUF repository keeps thinking builds in their own folder. Context 10, 262,144 tokens, max_position_embeddings in the text config. 20 + 10 + 20 + 0 + 10 + 10 = 70.\n\n## Score by category\n\n| Category | Weight | LongCat-2.5 | MiniCPM-V 4.7 1B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 35 | 95 | MiniCPM-V 4.7 1B +60 |\n| Risk | 20% (20 this run) | 60 | 55 | LongCat-2.5 +5 |\n| Agent interface | 10% (10 this run) | 75 | 70 | LongCat-2.5 +5 |\n| Documentation and upkeep | 10% (10 this run) | 46 | 48 | MiniCPM-V 4.7 1B +2 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **50.5 · D** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | LongCat-2.5 | MiniCPM-V 4.7 1B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Meituan (LongCat) | OpenBMB |\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 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-10 | 2026-10-30 |\n| Terms last updated | not read yet | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\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**MiniCPM-V 4.7 1B.** A vision-language cousin small enough for a phone, under plain Apache 2.0, that every runtime we count can load. The projector is bigger than the language model, so plan for about 2 GB. The card says nothing about limits or training data and doesn't show how a tool call comes back.\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### MiniCPM-V 4.7 1B\n\n1. Start it with `llama-server -hf openbmb/MiniCPM-V-4.7-1B-gguf:Q4_K_M --jinja`, or `ollama run openbmb/minicpm-v4.7:1b`\n2. For image input in llama.cpp add `mmproj-model-f16.gguf`, or the smaller 4x projector\n3. The GGUF files at the repository root have thinking off. Use the files in `thinking/` when you want the trace\n4. Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends\n5. Check tool calls in your runtime first, since the card names no parser\n6. Validate JSON output yourself, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, LongCat-2.5 or MiniCPM-V 4.7 1B?\n\nMiniCPM-V 4.7 1B 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 agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/longcat-2-5-vs-minicpm-v-4-7-1b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/longcat-2-5-vs-minicpm-v-4-7-1b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"longcat-2-5\", \"b\": \"minicpm-v-4-7-1b\"}`. From a terminal: `anchor compare longcat-2-5 minicpm-v-4-7-1b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json and https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-1b.json\n\n## Other comparisons with LongCat-2.5 or MiniCPM-V 4.7 1B\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 MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-v-4-7-1b.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 MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-v-4-7-1b.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 MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-v-4-7-1b.md)\n- [Arete-27B vs LongCat-2.5](https://www.anchorterminal.com/compare/arete-27b-vs-longcat-2-5.md)\n- [Arete-27B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-v-4-7-1b.md)\n- [BigBang-v1 vs LongCat-2.5](https://www.anchorterminal.com/compare/bigbang-v1-vs-longcat-2-5.md)\n- [BigBang-v1 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-v-4-7-1b.md)\n- [Carbon-X vs LongCat-2.5](https://www.anchorterminal.com/compare/carbon-x-vs-longcat-2-5.md)\n- [Carbon-X vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/carbon-x-vs-minicpm-v-4-7-1b.md)\n- [Cola2 vs LongCat-2.5](https://www.anchorterminal.com/compare/cola2-vs-longcat-2-5.md)\n- [Cola2 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/cola2-vs-minicpm-v-4-7-1b.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 MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minicpm-v-4-7-1b.md)\n- [Darwin-36B-Opus vs LongCat-2.5](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-longcat-2-5.md)\n- [Darwin-36B-Opus vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minicpm-v-4-7-1b.md)\n- [DeepSeek-V3.2 vs LongCat-2.5](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-longcat-2-5.md)\n- [DeepSeek-V3.2 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minicpm-v-4-7-1b.md)\n- [DeepSeek V4.1 Flash vs LongCat-2.5](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-longcat-2-5.md)\n- [DeepSeek V4.1 Flash vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm-v-4-7-1b.md)\n- [DeepSeek-V4-Flash-0731 vs LongCat-2.5](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-longcat-2-5.md)\n- [DeepSeek-V4-Flash-0731 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minicpm-v-4-7-1b.md)\n- [Devstral Small 2 vs LongCat-2.5](https://www.anchorterminal.com/compare/devstral-small-2-vs-longcat-2-5.md)\n- [Devstral Small 2 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/devstral-small-2-vs-minicpm-v-4-7-1b.md)\n- [DiffusionGemma 26B A4B IT vs LongCat-2.5](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-longcat-2-5.md)\n- [DiffusionGemma 26B A4B IT vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-minicpm-v-4-7-1b.md)\n- [EXAONE 4.5 33B vs LongCat-2.5](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-longcat-2-5.md)\n- [EXAONE 4.5 33B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minicpm-v-4-7-1b.md)\n- [Gemma 4 12B IT vs LongCat-2.5](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-longcat-2-5.md)\n- [Gemma 4 12B IT vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minicpm-v-4-7-1b.md)\n- [Gemma 4 26B A4B IT vs LongCat-2.5](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-longcat-2-5.md)\n- [Gemma 4 26B A4B IT vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minicpm-v-4-7-1b.md)\n- [Gemma 4 31B IT vs LongCat-2.5](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-longcat-2-5.md)\n- [Gemma 4 31B IT vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minicpm-v-4-7-1b.md)\n- [Gemma 4 E2B IT vs LongCat-2.5](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-longcat-2-5.md)\n- [Gemma 4 E2B IT vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minicpm-v-4-7-1b.md)\n- [Gemma 4 E4B IT vs LongCat-2.5](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-longcat-2-5.md)\n- [Gemma 4 E4B IT vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minicpm-v-4-7-1b.md)\n- [GLM-4.5-Air vs LongCat-2.5](https://www.anchorterminal.com/compare/glm-4-5-air-vs-longcat-2-5.md)\n- [GLM-4.5-Air vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-minicpm-v-4-7-1b.md)\n- [GLM-4.7-Flash vs LongCat-2.5](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-longcat-2-5.md)\n- [GLM-4.7-Flash vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minicpm-v-4-7-1b.md)\n- [GLM-5.3-Flash vs LongCat-2.5](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-longcat-2-5.md)\n- [GLM-5.3-Flash vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minicpm-v-4-7-1b.md)\n- [GLM-5.3 vs LongCat-2.5](https://www.anchorterminal.com/compare/glm-5-3-vs-longcat-2-5.md)\n- [GLM-5.3 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/glm-5-3-vs-minicpm-v-4-7-1b.md)\n- [gpt-oss-120b vs LongCat-2.5](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-longcat-2-5.md)\n- [gpt-oss-120b vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-minicpm-v-4-7-1b.md)\n- [gpt-oss-20b vs LongCat-2.5](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-longcat-2-5.md)\n- [gpt-oss-20b vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minicpm-v-4-7-1b.md)\n- [Granite 4.2 30B vs LongCat-2.5](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-longcat-2-5.md)\n- [Granite 4.2 30B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-minicpm-v-4-7-1b.md)\n- [Granite 4.2 3B vs LongCat-2.5](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-longcat-2-5.md)\n- [Granite 4.2 3B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minicpm-v-4-7-1b.md)\n- [Granite 4.2 8B vs LongCat-2.5](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-longcat-2-5.md)\n- [Granite 4.2 8B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-minicpm-v-4-7-1b.md)\n- [Hy-Memory-30B-A3B vs LongCat-2.5](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-longcat-2-5.md)\n- [Hy-Memory-30B-A3B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-minicpm-v-4-7-1b.md)\n- [Hy3 vs LongCat-2.5](https://www.anchorterminal.com/compare/hy3-vs-longcat-2-5.md)\n- [Hy3 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/hy3-vs-minicpm-v-4-7-1b.md)\n- [Inkling-Small vs LongCat-2.5](https://www.anchorterminal.com/compare/inkling-small-vs-longcat-2-5.md)\n- [Inkling-Small vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/inkling-small-vs-minicpm-v-4-7-1b.md)\n- [Inkling vs LongCat-2.5](https://www.anchorterminal.com/compare/inkling-vs-longcat-2-5.md)\n- [Inkling vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/inkling-vs-minicpm-v-4-7-1b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs LongCat-2.5](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-longcat-2-5.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minicpm-v-4-7-1b.md)\n- [Kimi K3.1 Pro vs LongCat-2.5](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-longcat-2-5.md)\n- [Kimi K3.1 Pro vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minicpm-v-4-7-1b.md)\n- [Kimi K3 vs LongCat-2.5](https://www.anchorterminal.com/compare/kimi-k3-vs-longcat-2-5.md)\n- [Kimi K3 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-v-4-7-1b.md)\n- [Kolibri-1 vs LongCat-2.5](https://www.anchorterminal.com/compare/kolibri-1-vs-longcat-2-5.md)\n- [Kolibri-1 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/kolibri-1-vs-minicpm-v-4-7-1b.md)\n- [Kronos vs LongCat-2.5](https://www.anchorterminal.com/compare/kronos-vs-longcat-2-5.md)\n- [Kronos vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/kronos-vs-minicpm-v-4-7-1b.md)\n- [Laguna M.1 vs LongCat-2.5](https://www.anchorterminal.com/compare/laguna-m-1-vs-longcat-2-5.md)\n- [Laguna M.1 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/laguna-m-1-vs-minicpm-v-4-7-1b.md)\n- [Laguna S 2.1 vs LongCat-2.5](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-longcat-2-5.md)\n- [Laguna S 2.1 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm-v-4-7-1b.md)\n- [Laguna XS 2.1 vs LongCat-2.5](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-longcat-2-5.md)\n- [Laguna XS 2.1 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minicpm-v-4-7-1b.md)\n- [LBS-27B vs LongCat-2.5](https://www.anchorterminal.com/compare/lbs-27b-vs-longcat-2-5.md)\n- [LBS-27B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/lbs-27b-vs-minicpm-v-4-7-1b.md)\n- [LFM2.5-2.6B vs LongCat-2.5](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-longcat-2-5.md)\n- [LFM2.5-2.6B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-v-4-7-1b.md)\n- [LFM3-1.7B vs LongCat-2.5](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-longcat-2-5.md)\n- [LFM3-1.7B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minicpm-v-4-7-1b.md)\n- [LFM3-Audio-2B vs LongCat-2.5](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-longcat-2-5.md)\n- [LFM3-Audio-2B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-minicpm-v-4-7-1b.md)\n- [LFM3-Omni-2B vs LongCat-2.5](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-longcat-2-5.md)\n- [LFM3-Omni-2B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-minicpm-v-4-7-1b.md)\n- [LFM3-VL-2B vs LongCat-2.5](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-longcat-2-5.md)\n- [LFM3-VL-2B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minicpm-v-4-7-1b.md)\n- [Ling-3.0-tiny vs LongCat-2.5](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-longcat-2-5.md)\n- [Ling-3.0-tiny vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-minicpm-v-4-7-1b.md)\n- [Ling-3.1-flash-1019 vs LongCat-2.5](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-longcat-2-5.md)\n- [Ling-3.1-flash-1019 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-minicpm-v-4-7-1b.md)\n- [LongCat-2.5 vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/longcat-2-5-vs-mimo-v2-6-distill-qwen-9b.md)\n- [LongCat-2.5 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/longcat-2-5-vs-mimo-v2-6-flash-rl.md)\n- [LongCat-2.5 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/longcat-2-5-vs-minicpm-o-4-5.md)\n- [LongCat-2.5 vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/longcat-2-5-vs-minicpm-v-4-7-35b-a3b.md)\n- [LongCat-2.5 vs MiniCPM5-2B](https://www.anchorterminal.com/compare/longcat-2-5-vs-minicpm5-2b.md)\n- [LongCat-2.5 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/longcat-2-5-vs-minimax-m2-5.md)\n- [LongCat-2.5 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/longcat-2-5-vs-minimax-m2-7.md)\n- [LongCat-2.5 vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/longcat-2-5-vs-minimax-m3-1-flash.md)\n- [LongCat-2.5 vs Mistral Large 4](https://www.anchorterminal.com/compare/longcat-2-5-vs-mistral-large-4.md)\n- [LongCat-2.5 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/longcat-2-5-vs-muse-glimmer-30b.md)\n- [LongCat-2.5 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/longcat-2-5-vs-nanbeige-4-2-3b.md)\n- [LongCat-2.5 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/longcat-2-5-vs-nanbeige-4-3-33b.md)\n- [LongCat-2.5 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [LongCat-2.5 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [LongCat-2.5 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-nano-30b-a3b.md)\n- [LongCat-2.5 vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-nano-4b.md)\n- [LongCat-2.5 vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [LongCat-2.5 vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-super-120b-a12b.md)\n- [LongCat-2.5 vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-ultra-550b-a55b.md)\n- [LongCat-2.5 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/longcat-2-5-vs-north-mini-code-1-0.md)\n- [LongCat-2.5 vs occamy-1.0](https://www.anchorterminal.com/compare/longcat-2-5-vs-occamy-1-0.md)\n- [LongCat-2.5 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/longcat-2-5-vs-olmo-3-7b-think.md)\n- [LongCat-2.5 vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-122b.md)\n- [LongCat-2.5 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-35b-a3b.md)\n- [LongCat-2.5 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-397b.md)\n- [LongCat-2.5 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-9b.md)\n- [LongCat-2.5 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/longcat-2-5-vs-otel-2-0-llm-31b-it.md)\n- [LongCat-2.5 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen-agentworld-35b-a3b.md)\n- [LongCat-2.5 vs 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