{ "data": { "a": { "slug": "minimax-m2-7", "name": "MiniMax-M2.7", "vendor": "MiniMax", "vendorUrl": "https://www.minimax.io", "kind": "weights", "category": "open-weights", "summary": "MiniMax-M2.7 is MiniMax's open-weight mixture-of-experts model for coding, agent harnesses and office work, about 229B parameters in FP8 with thinking always on, published on Hugging Face on 9 April 2026 under a non-commercial licence.", "url": "https://www.anchorterminal.com/tools/minimax-m2-7", "markdownUrl": "https://www.anchorterminal.com/tools/minimax-m2-7.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minimax-m2-7.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minimax-m2-7.json", "dns": "minimax-m2-7.grade.anchorterminal.com", "repo": "https://huggingface.co/MiniMaxAI/MiniMax-M2.7", "license": "MiniMax M2.7 non-commercial licence (MIT-style terms, commercial use needs MiniMax's written authorisation)", "transports": [], "packages": [ { "registry": "huggingface", "name": "MiniMaxAI/MiniMax-M2.7" } ], "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 to use for personal, research and non-profit purposes. Since 11 April 2026 the licence prohibits commercial use without MiniMax's prior written authorisation, and commercial users must display \"Built with MiniMax M2.7\". Running it yourself costs the hardware, 230.2 GB of FP8 files. MiniMax's API closed M2.7 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-M2.7", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "reasoning", "tool-calling", "coding", "non-commercial", "safetensors", "fp8", "self-hosted" ], "lastRelease": "2026-04-20", "graded": true, "anchor": { "graded": true, "score": 57.6, "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": 80, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 71, "interface": 75, "reach": 60, "risk": 30, "upkeep": 42 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-04" }, "negative": 0, "verdict": "A 230B mixture-of-experts model for coding and agent harnesses, with parallel tool calls, about 200K of context and parsers in vLLM and SGLang. It needs a 4 GPU server for FP8 or about 166 GB for a 4-bit library build, and it always thinks. The main risk is the licence, switched to non-commercial two days after release.", "bestFor": "Researchers and personal projects with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls", "strengths": [ "Runs in released vLLM, SGLang, llama.cpp, Transformers and LM Studio, and in Ollama 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 about 200K tokens of context", "Safetensors only, in the publisher's organisation with a SHA-256 for every shard", "No gate or account, and served by three inference providers on Hugging Face" ], "weaknesses": [ "Commercial use needs MiniMax's written authorisation since a licence swap on 11 April 2026, made without a note", "230 GB in FP8, and about 166 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", "No safety evaluation, agent guidance or security contact, and every loading route runs repository code", "No MiniMax reply in discussions since June 2026, and the card leaves out limitations, architecture, parameters and a cut-off" ], "agentNotes": [ "Check the use is personal, research or non-profit before starting it; anything commercial needs MiniMax's written authorisation, and commercial users must show Built with MiniMax M2.7", "Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.7 --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, with 196K the most per sequence", "On one large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.7 lists a 138 GB Q4_K_M GGUF and states 138 GB minimum memory", "Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends, and budget output tokens for thinking on every turn", "Pin Transformers 4.57.1 as MiniMax's guide does; a report that it fails on Transformers v5 has no answer", "Without the hardware, call it through Novita, Featherless AI or DeepInfra; MiniMax's own API retires M2.7 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": 57.6 } ], "editorialScores": { "capability": 71, "interface": 75, "reach": 60, "risk": 30, "upkeep": 42 }, "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-M2.7 --trust-remote-code \\\n --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 \\\n --reasoning-parser minimax_m2_append_think", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"MiniMaxAI/MiniMax-M2.7\", \"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-m2-7" }, "sameCompany": [ "minimax-api", "minimax-video", "minimax-stt", "minimax-speech", "minimax-voice-cloning", "minimax-code", "minimax-m2-5", "minimax-m3-1-flash" ], "weights": { "trainedBy": "MiniMax", "params": "228.7B", "context": 204800, "goodFor": "Mixture-of-experts model for coding, agent harnesses and office documents, with parallel tool calls and always-on thinking, text only, for non-commercial use unless MiniMax authorises otherwise.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "LM Studio", "Transformers", "Ollama" ], "builds": [ { "name": "LM Studio minimax/minimax-m2.7 (Q4_K_M GGUF)", "url": "https://huggingface.co/lmstudio-community/MiniMax-M2.7-GGUF", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 138.3, "memoryGB": 192, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "MiniMax-M2.7-Q4_K_M split GGUF, 138.34 GB summed from the Hub's file list. The catalogue entry also lists Q6_K (187.8 GB) and Q8_0 (243.1 GB). LM Studio states 138 GB minimum memory" }, { "name": "MiniMax-M2.7", "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.7", "who": "publisher", "precision": "fp8", "sizeGB": 230.2, "memoryGB": 512, "format": "safetensors", "note": "Native FP8 E4M3 with 128 by 128 blocks; the only build MiniMax publishes. Size summed from the Hub's file list" } ], "run": "SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.7 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think", "runBuild": "MiniMax-M2.7", "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-M2.5", "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.5", "params": "230B total, 10B active (Hub count; card doesn't restate)", "listing": "https://www.anchorterminal.com/tools/minimax-m2-5" } ], "olderCount": 7, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "MiniMax (named in the licence's copyright line; 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 current licence reads Copyright (c) 2026 MiniMax. The platform terms' legal entity and the domain's registration date are as recorded on the minimax-api listing.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.", "No changelog. Commit messages are uploads and README or LICENSE updates; the licence change on 11 April 2026 reads only \"Delete LICENSE-MODEL\" and \"Upload LICENSE with huggingface_hub\".", "www.minimax.io/.well-known/security.txt redirects to the home page on 21 October 2026, and MiniMax-AI/MiniMax-M2.7 has no SECURITY.md, so there is no security.txt.", "MiniMax's site (www.minimax.io/models/text) links to huggingface.co/MiniMaxAI, and the card links the GitHub repository, so the repository is the publisher's own." ], "score": 60 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/minimax-m2-7.json", "live": { "slug": "minimax-m2-7", "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" } }, "answer": "MiniMax-M2.7 scores 57.6 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 2 of 4 scored categories. SenseNova-Loop-9B-Preview leads on risk.", "b": { "slug": "sensenova-loop-9b-preview", "name": "SenseNova-Loop-9B-Preview", "vendor": "SenseTime (SenseNova)", "vendorUrl": "https://www.sensetime.com/en", "kind": "weights", "category": "open-weights", "summary": "SenseNova-Loop-9B-Preview is SenseTime's open-weight multimodal model of about 9B parameters. It runs 24 of its 32 decoder layers twice, with text and images in, tool calling, a thinking switch and a 256K context. Apache 2.0. No released runtime loads it.", "url": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview", "markdownUrl": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/sensenova-loop-9b-preview.json", "dns": "sensenova-loop-9b-preview.grade.anchorterminal.com", "repo": "https://huggingface.co/sensenova/SenseNova-Loop-9B-Preview", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "sensenova/SenseNova-Loop-9B-Preview" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use under Apache 2.0. Running it yourself costs the hardware: the files are 38.1 GB because most weights are stored in 32-bit floats, and the card's example loads them as BF16 (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://github.com/OpenSenseNova/SenseNova-Loop", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "multimodal", "vision", "looped-transformer", "tool-calling", "reasoning", "long-context", "preview", "apache-2.0", "safetensors", "custom-code", "self-hosted" ], "lastRelease": "2026-11-02", "graded": true, "anchor": { "graded": true, "score": 46.8, "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": 110, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 60, "reach": 40, "risk": 50, "upkeep": 44 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "An unusual architecture under Apache 2.0, with a 256K training context, a thinking switch and tool calling in the template. It's a preview in practice too: no released runtime runs it, loading it runs the repository's code, the files are twice the size the card implies, and nobody from SenseNova has answered a question yet.", "bestFor": "Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today", "strengths": [ "Apache 2.0, ungated", "A 256K training context, stated on the card", "Thinking can be switched per request", "The architecture and parameter counts are documented", "Changed in the last ten days" ], "weaknesses": [ "No released runtime runs it. vLLM and SGLang need unmerged pull requests", "Loading it runs Python from the repository", "The files are 38.1 GB, mostly F32, though the card says BF16", "No quantised build", "No tool-calling documentation beyond the template", "No limitations, safety results or data sources, and the technical report isn't out", "None of 5 questions answered" ], "agentNotes": [ "Read modeling_sensenova_loop.py before you load it. trust_remote_code runs it", "Pin transformers 5.18.0, the version the card checked", "Load with dtype=torch.bfloat16 as the card does, or the 38 GB of F32 weights stay at full size", "Pass enable_thinking=False to apply_chat_template for a direct answer", "Tool calls come back as tool_call blocks with function and parameter tags. Parse them yourself, since no runtime ships a parser", "Wait for the vLLM or SGLang pull request to land in a release before putting it behind an API" ], "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": 46.8 } ], "editorialScores": { "interface": 60, "reach": 40, "risk": 50, "upkeep": 44 }, "provenanceScore": 51, "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": "pip install \"transformers==5.18.0\" accelerate safetensors\n# then AutoModelForCausalLM.from_pretrained(\"sensenova/SenseNova-Loop-9B-Preview\", trust_remote_code=True, dtype=torch.bfloat16)\n# trust_remote_code runs modeling_sensenova_loop.py from the repository. Read it first", "http": "# No released server runs it. The card's vLLM and SGLang commands build from open pull requests,\n# and its LightLLM example serves an OpenAI-compatible API at http://localhost:8000/v1 on four GPUs" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/sensenova-loop-9b-preview" }, "weights": { "trainedBy": "SenseTime (SenseNova)", "params": "9B", "context": 262144, "goodFor": "Trying a looped transformer for long-context, tool-using work, if you can run the repository's own code on a large GPU.", "runtimes": [], "builds": [ { "name": "SenseNova-Loop-9B-Preview (safetensors, mostly F32)", "url": "https://huggingface.co/sensenova/SenseNova-Loop-9B-Preview/tree/main", "who": "publisher", "precision": "f32", "sizeGB": 38.14, "memoryGB": 48, "format": "safetensors", "note": "Eight shards and the MTP head. Stored mostly as 32-bit floats; the card's example loads it as BF16, about half the size in memory" } ], "run": "python -c \"from transformers import AutoModelForCausalLM; AutoModelForCausalLM.from_pretrained('sensenova/SenseNova-Loop-9B-Preview', trust_remote_code=True)\"", "runBuild": "SenseNova-Loop-9B-Preview (safetensors, mostly F32)", "checked": "2026-11-10", "minMemoryGB": 48 }, "family": { "lab": "SenseTime (SenseNova)", "family": "SenseNova-Loop", "url": "https://www.anchorterminal.com/open-weights/#sensenova" }, "area": "models", "provenance": { "legalEntity": "SenseTime, listed on the Stock Exchange of Hong Kong (00020) per its investor page. SenseNova is its foundation model series, per its site", "domain": "sensetime.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "https://github.com/OpenSenseNova/SenseNova-Loop/blob/main/LICENSE", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/sensenova/SenseNova-Loop-9B-Preview/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The card names SenseNova only. SenseTime's site presents SenseNova as its foundation model series, and that is the tie between the two; the site wasn't read for a link to this repository.", "The domain's registration date wasn't looked up.", "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored.", "www.sensetime.com has no security.txt (404 on 10 November 2026). Its about page gives a security contact.", "The changelog is the commit history, 39 commits from 28 October to 2 November 2026." ], "score": 51 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.json" }, "edges": [ { "by": 20, "category": "reach", "leads": "minimax-m2-7", "name": "Reach", "reasons": { "a": "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 Q4_K_M GGUF at 138.3 GB, about 166 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, SGLang, llama.cpp and Transformers, an LM Studio catalogue entry, and Ollama 0.40.14, whose pinned llama.cpp b11963 has the minimax-m2 architecture (capped at 25). Builds earn 10 for one publisher precision, the FP8 release. Terms earn 5, since the licence today allows only personal, research and non-profit use without MiniMax's written authorisation. Download earns 10, with no gate or account. Hosted earns 5, since Novita, Featherless AI and DeepInfra serve it through Hugging Face, while MiniMax's own API is closed to new users and retires it on 18 November 2026.", "b": "Memory 15, the only build is the safetensors set, 38.14 GB with its MTP head, since the Hub counts 9.41B of its parameters in F32. Runtimes 0, none of the six runs it in a released version. Builds 0, SenseNova publishes one set of weights and no quantised build. Terms 15, Apache 2.0 in the card's metadata and on the card, which links the LICENSE in the GitHub repository, and nothing added. Download 10, the Hub API reports gated false 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, and the card names no API that serves it. 15 + 0 + 0 + 15 + 10 + 0 = 40." }, "scores": { "a": 60, "b": 40 } }, { "by": 20, "category": "risk", "leads": "sensenova-loop-9b-preview", "name": "Risk", "reasons": { "a": "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 appendix goes beyond the law, MiniMax swapped a commercial licence for a non-commercial one two days after release, and neither that swap nor the amendment three days later carries a dated note. The security programme earns nothing, with no security.txt, disclosure policy, SECURITY.md or advisories.", "b": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the sensenova organisation, which the GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on instructions inside tool results or images. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, SenseTime's about page gives a security contact (5). 15 + 15 + 0 + 0 + 15 + 5 = 50." }, "scores": { "a": 30, "b": 50 } }, { "by": 15, "category": "interface", "leads": "minimax-m2-7", "name": "Agent interface", "reasons": { "a": "The chat template earns 20 for chat_template.jinja in the repository. The tool-calling format earns 25, as MiniMax's own guide documents how tools go in and how calls come back, gives a parsing function, and its deployment guides name the vLLM and SGLang parsers. Trained tool calling earns 20, with several invoke blocks in one call. Structured output earns nothing, since JSON output isn't documented or claimed. Reasoning control earns nothing, because thinking is always on with no documented way to turn it off or budget it. Context earns 10 for 204,800 tokens in the config with no position scaling.", "b": "Chat template 20, chat_template.jinja in the repository (7,756 characters). Tool-calling format 10, only the template says it: a tools block, and calls written as tool_call blocks with function and parameter tags. Trained tool calling 10, the card says the training recipes include tool trajectories and that the model is for agentic workflows. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the card documents enable_thinking in the chat template call, on or off per request, and the template puts the reasoning in think tags ahead of the answer. Context 10, 262,144 tokens in training, per the card's specification table, and config.json gives the language model 262,144 positions. 20 + 10 + 10 + 0 + 10 + 10 = 60." }, "scores": { "a": 75, "b": 60 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "MiniMax", "b": "SenseTime (SenseNova)", "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": "MiniMax M2.7 non-commercial licence (MIT-style terms, commercial use needs MiniMax's written authorisation)", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-04-20", "b": "2026-11-02", "name": "Last release" }, { "a": "no document linked", "b": "not read yet", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "1.8/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "MiniMax-M2.7 scores 57.6 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 2 of 4 scored categories. SenseNova-Loop-9B-Preview leads on risk.", "question": "Which is better for AI agents, MiniMax-M2.7 or SenseNova-Loop-9B-Preview?" } ], "goodFor": [ { "aheadOn": [ "Reach, 60 against 40", "Agent interface, 75 against 60" ], "also": null, "goodFor": "Researchers and personal projects with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls", "slug": "minimax-m2-7", "watchFor": "Commercial use needs MiniMax's written authorisation since a licence swap on 11 April 2026, made without a note" }, { "aheadOn": [ "Risk, 50 against 30" ], "also": null, "goodFor": "Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today", "slug": "sensenova-loop-9b-preview", "watchFor": "No released runtime runs it. vLLM and SGLang need unmerged pull requests" } ], "history": { "minimax-m2-7": [ { "causes": [ "product" ], "date": "2026-11-04", "delta": 1.5, "from": 56.1, "fromGrade": "C", "to": 57.6, "toGrade": "C" } ], 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"https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-sensenova-loop-9b-preview.json", "title": "Qwen3-VL-8B-Instruct vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-sensenova-loop-9b-preview.json", "title": "Qwopus3.8-27B-Flash-V2 vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-sensenova-loop-9b-preview.json", "title": "Recursive Flint vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-sensenova-loop-9b-preview.json", "title": "Ripple-65B Preview vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-smollm3-3b.json", "title": "SenseNova-Loop-9B-Preview vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-spark-x2-6-4b.json", "title": "SenseNova-Loop-9B-Preview vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-step-3-7-flash.json", "title": "SenseNova-Loop-9B-Preview vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-swift-1-5-qwen3-8-27b.json", "title": "SenseNova-Loop-9B-Preview vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-swift-1-5-qwen3-8-flash-next.json", "title": "SenseNova-Loop-9B-Preview vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-thinkingcap-qwen3-8-27b.json", "title": "SenseNova-Loop-9B-Preview vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-unlimited-ocr-2.json", "title": "SenseNova-Loop-9B-Preview vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b.json", "title": "SenseNova-Loop-9B-Preview vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-4b.json", "title": "SenseNova-Loop-9B-Preview vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-zaya1-8b.json", "title": "SenseNova-Loop-9B-Preview vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 20, "edge": "minimax-m2-7", "key": "reach", "minimax-m2-7": 60, "name": "Reach", "sensenova-loop-9b-preview": 40, "weight": 25 }, { "by": 20, "edge": "sensenova-loop-9b-preview", "key": "risk", "minimax-m2-7": 30, "name": "Risk", "sensenova-loop-9b-preview": 50, "weight": 20 }, { "by": 15, "edge": "minimax-m2-7", "key": "interface", "minimax-m2-7": 75, "name": "Agent interface", "sensenova-loop-9b-preview": 60, "weight": 10 }, { "by": 2, "edge": "sensenova-loop-9b-preview", "key": "upkeep", "minimax-m2-7": 42, "name": "Documentation and upkeep", "sensenova-loop-9b-preview": 44, "weight": 10 } ], "summary": "MiniMax-M2.7 scores 57.6 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 2 of 4 scored categories. SenseNova-Loop-9B-Preview leads on risk. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "minimax-m2-7": "A 230B mixture-of-experts model for coding and agent harnesses, with parallel tool calls, about 200K of context and parsers in vLLM and SGLang. It needs a 4 GPU server for FP8 or about 166 GB for a 4-bit library build, and it always thinks. The main risk is the licence, switched to non-commercial two days after release.", "sensenova-loop-9b-preview": "An unusual architecture under Apache 2.0, with a 256K training context, a thinking switch and tool calling in the template. It's a preview in practice too: no released runtime runs it, loading it runs the repository's code, the files are twice the size the card implies, and nobody from SenseNova has answered a question yet." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-sensenova-loop-9b-preview", "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-sensenova-loop-9b-preview.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-sensenova-loop-9b-preview.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-sensenova-loop-9b-preview.min.md" }, "markdown": "MiniMax-M2.7 scores 57.6 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 2 of 4 scored categories. SenseNova-Loop-9B-Preview leads on risk. Both do open-weight models you run yourself.\n\n- MiniMax-M2.7: grade C, 57.6/100, rank #106 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/minimax-m2-7.md · JSON https://www.anchorterminal.com/api/v1/tools/minimax-m2-7.json\n- SenseNova-Loop-9B-Preview: grade D, 46.8/100, rank #155 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.md · JSON https://www.anchorterminal.com/api/v1/tools/sensenova-loop-9b-preview.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### MiniMax-M2.7 (C)\n\nGood for: Researchers and personal projects with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls\n\nAhead on:\n- Reach, 60 against 40\n- Agent interface, 75 against 60\n\nWatch for: Commercial use needs MiniMax's written authorisation since a licence swap on 11 April 2026, made without a note\n\n### SenseNova-Loop-9B-Preview (D)\n\nGood for: Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today\n\nAhead on:\n- Risk, 50 against 30\n\nWatch for: No released runtime runs it. vLLM and SGLang need unmerged pull requests\n\n\n## Why the edge\n\n### Reach: MiniMax-M2.7 +20\n\n- MiniMax-M2.7 (60): 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 Q4_K_M GGUF at 138.3 GB, about 166 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, SGLang, llama.cpp and Transformers, an LM Studio catalogue entry, and Ollama 0.40.14, whose pinned llama.cpp b11963 has the minimax-m2 architecture (capped at 25). Builds earn 10 for one publisher precision, the FP8 release. Terms earn 5, since the licence today allows only personal, research and non-profit use without MiniMax's written authorisation. Download earns 10, with no gate or account. Hosted earns 5, since Novita, Featherless AI and DeepInfra serve it through Hugging Face, while MiniMax's own API is closed to new users and retires it on 18 November 2026.\n- SenseNova-Loop-9B-Preview (40): Memory 15, the only build is the safetensors set, 38.14 GB with its MTP head, since the Hub counts 9.41B of its parameters in F32. Runtimes 0, none of the six runs it in a released version. Builds 0, SenseNova publishes one set of weights and no quantised build. Terms 15, Apache 2.0 in the card's metadata and on the card, which links the LICENSE in the GitHub repository, and nothing added. Download 10, the Hub API reports gated false 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, and the card names no API that serves it. 15 + 0 + 0 + 15 + 10 + 0 = 40.\n\n### Risk: SenseNova-Loop-9B-Preview +20\n\n- MiniMax-M2.7 (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 appendix goes beyond the law, MiniMax swapped a commercial licence for a non-commercial one two days after release, and neither that swap nor the amendment three days later carries a dated note. The security programme earns nothing, with no security.txt, disclosure policy, SECURITY.md or advisories.\n- SenseNova-Loop-9B-Preview (50): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the sensenova organisation, which the GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on instructions inside tool results or images. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, SenseTime's about page gives a security contact (5). 15 + 15 + 0 + 0 + 15 + 5 = 50.\n\n### Agent interface: MiniMax-M2.7 +15\n\n- MiniMax-M2.7 (75): The chat template earns 20 for chat_template.jinja in the repository. The tool-calling format earns 25, as MiniMax's own guide documents how tools go in and how calls come back, gives a parsing function, and its deployment guides name the vLLM and SGLang parsers. Trained tool calling earns 20, with several invoke blocks in one call. Structured output earns nothing, since JSON output isn't documented or claimed. Reasoning control earns nothing, because thinking is always on with no documented way to turn it off or budget it. Context earns 10 for 204,800 tokens in the config with no position scaling.\n- SenseNova-Loop-9B-Preview (60): Chat template 20, chat_template.jinja in the repository (7,756 characters). Tool-calling format 10, only the template says it: a tools block, and calls written as tool_call blocks with function and parameter tags. Trained tool calling 10, the card says the training recipes include tool trajectories and that the model is for agentic workflows. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the card documents enable_thinking in the chat template call, on or off per request, and the template puts the reasoning in think tags ahead of the answer. Context 10, 262,144 tokens in training, per the card's specification table, and config.json gives the language model 262,144 positions. 20 + 10 + 10 + 0 + 10 + 10 = 60.\n\n## Score by category\n\n| Category | Weight | MiniMax-M2.7 | SenseNova-Loop-9B-Preview | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 60 | 40 | MiniMax-M2.7 +20 |\n| Risk | 20% (20 this run) | 30 | 50 | SenseNova-Loop-9B-Preview +20 |\n| Agent interface | 10% (10 this run) | 75 | 60 | MiniMax-M2.7 +15 |\n| Documentation and upkeep | 10% (10 this run) | 42 | 44 | SenseNova-Loop-9B-Preview +2 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **57.6 · C** | **46.8 · D** | |\n\n## Facts side by side\n\n| Fact | MiniMax-M2.7 | SenseNova-Loop-9B-Preview |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | MiniMax | SenseTime (SenseNova) |\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 | MiniMax M2.7 non-commercial licence (MIT-style terms, commercial use needs MiniMax's written authorisation) | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-04-20 | 2026-11-02 |\n| Terms last updated | no document linked | not read yet |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 1.8/5 (4) | none |\n\n## How the scores have moved\n\n### MiniMax-M2.7\n\n- +1.5 on 4 November 2026: product\n\n### SenseNova-Loop-9B-Preview\n\nNo score change since it was listed.\n\n## Verdicts\n\n**MiniMax-M2.7.** A 230B mixture-of-experts model for coding and agent harnesses, with parallel tool calls, about 200K of context and parsers in vLLM and SGLang. It needs a 4 GPU server for FP8 or about 166 GB for a 4-bit library build, and it always thinks. The main risk is the licence, switched to non-commercial two days after release.\n\n**SenseNova-Loop-9B-Preview.** An unusual architecture under Apache 2.0, with a 256K training context, a thinking switch and tool calling in the template. It's a preview in practice too: no released runtime runs it, loading it runs the repository's code, the files are twice the size the card implies, and nobody from SenseNova has answered a question yet.\n\n## Before you call either\n\n### MiniMax-M2.7\n\n1. Check the use is personal, research or non-profit before starting it; anything commercial needs MiniMax's written authorisation, and commercial users must show Built with MiniMax M2.7\n2. Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.7 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think\n3. In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code\n4. MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache, with 196K the most per sequence\n5. On one large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.7 lists a 138 GB Q4_K_M GGUF and states 138 GB minimum memory\n6. Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends, and budget output tokens for thinking on every turn\n7. Pin Transformers 4.57.1 as MiniMax's guide does; a report that it fails on Transformers v5 has no answer\n8. Without the hardware, call it through Novita, Featherless AI or DeepInfra; MiniMax's own API retires M2.7 on 18 November 2026\n\n### SenseNova-Loop-9B-Preview\n\n1. Read modeling_sensenova_loop.py before you load it. trust_remote_code runs it\n2. Pin transformers 5.18.0, the version the card checked\n3. Load with dtype=torch.bfloat16 as the card does, or the 38 GB of F32 weights stay at full size\n4. Pass enable_thinking=False to apply_chat_template for a direct answer\n5. Tool calls come back as tool_call blocks with function and parameter tags. Parse them yourself, since no runtime ships a parser\n6. Wait for the vLLM or SGLang pull request to land in a release before putting it behind an API\n\n## Questions\n\n### Which is better for AI agents, MiniMax-M2.7 or SenseNova-Loop-9B-Preview?\n\nMiniMax-M2.7 scores 57.6 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 2 of 4 scored categories. SenseNova-Loop-9B-Preview leads on risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/minimax-m2-7-vs-sensenova-loop-9b-preview.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minimax-m2-7-vs-sensenova-loop-9b-preview.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"minimax-m2-7\", \"b\": \"sensenova-loop-9b-preview\"}`. From a terminal: `anchor compare minimax-m2-7 sensenova-loop-9b-preview`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/minimax-m2-7.json and https://www.anchorterminal.com/api/v1/tools/sensenova-loop-9b-preview.json\n\n## Other comparisons with MiniMax-M2.7 or SenseNova-Loop-9B-Preview\n\n- [Agents-A1-4B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-7.md)\n- [Agents-A1-4B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/agents-a1-4b-vs-sensenova-loop-9b-preview.md)\n- [Apertus v1.5 8B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-7.md)\n- [Apertus v1.5 8B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-sensenova-loop-9b-preview.md)\n- [Apodex-1.1-mini vs MiniMax-M2.7](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-7.md)\n- [Apodex-1.1-mini vs 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[SenseNova-Loop-9B-Preview vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-unlimited-ocr-2.md)\n- [SenseNova-Loop-9B-Preview vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b.md)\n- [SenseNova-Loop-9B-Preview vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-4b.md)\n- [SenseNova-Loop-9B-Preview vs ZAYA1-8B](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-zaya1-8b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-11", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": 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