{ "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", "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": 56.1, "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": 1197, "categoryRank": 61, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 71, "interface": 75, "reach": 60, "risk": 30, "upkeep": 27 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "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", "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": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 56.1 } ], "editorialScores": { "capability": 71, "interface": 75, "reach": 60, "risk": 30, "upkeep": 27 }, "provenanceScore": 55, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "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-voice-cloning", "minimax-code", "minimax-m2-5" ], "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" ], "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": "2026-10-21", "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": 55 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/minimax-m2-7.json" }, "answer": "MiniMax-M2.7 scores 56.1 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in 1 of 4 scored categories. Swift 1.5 Qwen3.8-Flash-Next leads on risk and documentation and upkeep.", "b": { "slug": "swift-1-5-qwen3-8-flash-next", "name": "Swift 1.5 Qwen3.8-Flash-Next", "vendor": "UkisAI", "vendorUrl": "https://ukisai.com", "kind": "weights", "category": "open-weights", "summary": "Swift 1.5 Qwen3.8-Flash-Next is UkisAI's post-trained Qwen3.8-Flash-Next that aims to think in fewer tokens, a mixture-of-experts vision-language model of 180B parameters in its files with a 262,144-token context, published in BF16 with GGUF, INT4 and NVFP4 builds.", "url": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-flash-next", "markdownUrl": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-flash-next.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-flash-next.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-flash-next.json", "repo": "https://huggingface.co/ukisai/Swift1.5-Qwen3.8-Flash-Next", "license": "Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue, plus the Qwen Community License 1.0 on the base, which needs a separate licence from Qwen for a Model as a Service or AI Work Assistant business and the model name shown above 100M monthly users or US$20M monthly revenue", "transports": [], "packages": [ { "registry": "huggingface", "name": "ukisai/Swift1.5-Qwen3.8-Flash-Next" }, { "registry": "huggingface", "name": "ukisai/Swift-1.5-Qwen3.8-Flash-Next-GGUF" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports the repository as not gated, though the card's metadata says gated true (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download. Commercial use of UkisAI's contribution is free below US$1,000,000 gross annual revenue, affiliates included, and needs a Swift Enterprise License above it. The base's Qwen Community License 1.0 also applies, with a separate licence from Qwen for Model as a Service or AI Work Assistant businesses. UkisAI's card lists a free API for research at ukisai.com/api/flash-next/v1. Running it yourself costs the hardware, 360 GB in BF16 and 98.6 GB for the smallest 4-bit GGUF with its projector by the Hub's file lists (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/ukisai/Swift1.5-Qwen3.8-Flash-Next", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "fine-tune", "custom-licence", "safetensors", "gguf", "self-hosted" ], "lastRelease": "2026-09-24", "graded": true, "anchor": { "graded": true, "score": 52.3, "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": 1197, "categoryRank": 70, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 60, "reach": 60, "risk": 45, "upkeep": 40 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A large mixture-of-experts cousin, Qwen3.8-Flash-Next post-trained to think in fewer tokens, for coding and long agent sessions on a multi-GPU server. Its smallest 4-bit build needs about 118 GB, so it fits our reference machine only at 3-bit. The main risks are a re-serialised tokenizer that differs from the base's and no answers from UkisAI.", "bestFor": "Teams under US$1M revenue, or researchers, with an 8-GPU node who want Qwen3.8-Flash-Next with shorter thinking for coding and agent work.", "strengths": [ "Runs in released vLLM, SGLang, Transformers and llama.cpp as Qwen3.8-Flash-Next does, with the same template and 262,144-token context", "GGUF, INT4 and NVFP4 builds from UkisAI, including 3-bit GGUFs that fit about 102 GB", "Safetensors only, in the account UkisAI's site links to, with a checksum on every file", "Several tool calls a turn and post-training aimed at agent and coding work", "Ungated, with no account or terms to accept" ], "weaknesses": [ "Two licences to satisfy, UkisAI's US$1M revenue limit and Qwen's separate licence for model-as-a-service businesses", "The smallest 4-bit build needs about 118 GB, beyond a 128 GB unified-memory machine's GPU share", "tokenizer.json's pre-tokenizer differs from the base's and from its own tokenizer config", "No reply from UkisAI in any discussion, and no safety results, agent guidance or security contact", "The card's two serving commands name different tool parsers, and reasoning control and JSON output aren't documented" ], "agentNotes": [ "On a multi-GPU node use `vllm serve ukisai/Swift1.5-Qwen3.8-Flash-Next --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, the parser the base's recipes and the card's SGLang command use", "The card's vLLM command passes --trust-remote-code and the old repository name, neither is needed", "On our reference machine only UkisAI's GSQ-RCO IQ3_S GGUF (84.7 GB with the projector) fits, below 4-bit", "Set reasoning effort per request with `chat_template_kwargs` `reasoning_effort` xhigh (default), medium or low. UkisAI's own runs lose about 2.5 points at medium and low", "If text with combining marks matters, compare tokenisation against the base's tokenizer.json before trusting outputs", "Check both licences before commercial use, UkisAI's stops at US$1M gross annual revenue and Qwen's needs a separate licence for model-as-a-service businesses" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 1.8, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 52.3 } ], "editorialScores": { "interface": 60, "reach": 60, "risk": 45, "upkeep": 40 }, "provenanceScore": 13, "checked": { "categories": { "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "vllm serve ukisai/Swift1.5-Qwen3.8-Flash-Next --tensor-parallel-size 8 --max-model-len 262144 \\\n --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"ukisai/Swift1.5-Qwen3.8-Flash-Next\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello, Swift.\"}], \"chat_template_kwargs\": {\"reasoning_effort\": \"low\"}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/swift-1-5-qwen3-8-flash-next" }, "sameCompany": [ "swift-1-5-qwen3-8-27b" ], "weights": { "trainedBy": "UkisAI, post-training Alibaba's Qwen3.8-Flash-Next", "builtOn": "Qwen/Qwen3.8-Flash-Next (fine-tune, Qwen Community License 1.0)", "params": "125B", "context": 262144, "goodFor": "A Qwen3.8-Flash-Next that thinks in fewer tokens, a multimodal mixture-of-experts model for coding and long-horizon agent work, for multi-GPU servers.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Swift-1.5-Qwen3.8-Flash-Next-GGUF IQ4_XS", "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-GGUF", "who": "publisher", "precision": "iq4_xs", "sizeGB": 98.6, "memoryGB": 128, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "Three IQ4_XS shards (97.68 GB) plus mmproj-Swift-1.5-Qwen3.8-Flash-Next-BF16.gguf (0.91 GB), summed from the Hub's file list. The smallest build of 4-bit or better, 118.3 GB with a fifth, above the reference machine's 107.4 GB" }, { "name": "Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF IQ3_S", "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF", "who": "publisher", "precision": "iq3_s", "sizeGB": 84.7, "memoryGB": 128, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "Below 4-bit, so not counted for Reach memory. Two IQ3_S shards (83.74 GB) plus the 0.91 GB projector, 101.6 GB with a fifth, inside the reference machine's 107.4 GB. The repository also has IQ3_XXS, IQ2_XS and Q2_0 files from 66.5 GB" }, { "name": "Swift-1.5-Qwen3.8-Flash-Next-GGUF Q4_K_M", "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 120.5, "memoryGB": 192, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "Three Q4_K_M shards (119.60 GB) plus the 0.91 GB vision projector" }, { "name": "Swift-1.5-Qwen3.8-Flash-Next-W4A16-AWQ", "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-W4A16-AWQ", "who": "publisher", "precision": "int4", "sizeGB": 177.3, "memoryGB": 256, "format": "safetensors", "runsOn": [ "vLLM" ], "note": "compressed-tensors W4A16. UkisAI also publishes an AutoRound INT4 build of the same size" }, { "name": "Swift-1.5-Qwen3.8-Flash-Next-NVFP4", "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 186.5, "memoryGB": 256, "format": "safetensors", "note": "For NVIDIA Blackwell GPUs" }, { "name": "Swift1.5-Qwen3.8-Flash-Next", "url": "https://huggingface.co/ukisai/Swift1.5-Qwen3.8-Flash-Next", "who": "publisher", "precision": "bf16", "sizeGB": 360, "memoryGB": 512, "format": "safetensors" } ], "run": "vllm serve ukisai/Swift1.5-Qwen3.8-Flash-Next --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "Swift1.5-Qwen3.8-Flash-Next", "checked": "2026-10-21", "minMemoryGB": 128 }, "family": { "lab": "UkisAI (ukisai)", "family": "Swift 1", "url": "https://www.anchorterminal.com/open-weights/#ukisai", "others": [ { "name": "Swift-Qwen3.8-27b", "url": "https://huggingface.co/ukisai/Swift-Qwen3.8-27b", "params": "27B (27,781,427,952 by the Hub's safetensors count)" }, { "name": "Swift-1.5-Qwen3.8-27b", "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27b", "params": "27B (27,781,427,952 by the Hub's safetensors count)", "listing": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-27b" }, { "name": "Swift-1.5-Ornith-9B", "url": "https://huggingface.co/ukisai/Swift-1.5-Ornith-9B", "params": "9B (9,653,104,368 by the Hub's safetensors count)" }, { "name": "Swift-Bonsai-2-GGUF", "url": "https://huggingface.co/ukisai/Swift-Bonsai-2-GGUF", "params": "27B (26,895,998,464 by the Hub's GGUF count)" } ], "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "", "domain": "ukisai.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "https://huggingface.co/ukisai/Swift1.5-Qwen3.8-Flash-Next/blob/main/LICENSE", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The licence names UkisAI as licensor with no legal entity. ukisai.com describes a Europe-based applied AI lab in Belgrade, Eindhoven and Wilmington. RDAP wasn't queried.", "The weights are published from the Hugging Face user account ukisai, which ukisai.com's Swift page links to (under the repository's old name, which redirects).", "The two licences stand in for terms of service for the weights. The research API isn't graded, so no privacy policy is scored.", "No changelog. The repository's history was squashed to one commit.", "ukisai.com/.well-known/security.txt is 404, and ukisai/.github has no SECURITY.md." ], "score": 13 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-flash-next.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "MiniMax", "b": "UkisAI", "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": "Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue, plus the Qwen Community License 1.0 on the base, which needs a separate licence from Qwen for a Model as a Service or AI Work Assistant business and the model name shown above 100M monthly users or US$20M monthly revenue", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-04-20", "b": "2026-09-24", "name": "Last release" }, { "a": "no document linked", "b": "", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "1.8/5 (4)", "b": "1.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "MiniMax-M2.7 scores 56.1 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in 1 of 4 scored categories. Swift 1.5 Qwen3.8-Flash-Next leads on risk and documentation and upkeep.", "question": "Which is better for AI agents, MiniMax-M2.7 or Swift 1.5 Qwen3.8-Flash-Next?" } ], "goodFor": [ { "aheadOn": [ "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, 45 against 30", "Documentation and upkeep, 40 against 27" ], "also": null, "goodFor": "Teams under US$1M revenue, or researchers, with an 8-GPU node who want Qwen3.8-Flash-Next with shorter thinking for coding and agent work.", "slug": "swift-1-5-qwen3-8-flash-next", "watchFor": "Two licences to satisfy, UkisAI's US$1M revenue limit and Qwen's separate licence for model-as-a-service businesses" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-7.json", "title": "Agents-A1-4B vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Agents-A1-4B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-7.json", "title": "Apertus v1.5 8B vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Apertus v1.5 8B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-7.json", "title": "Apodex-1.1-mini vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Apodex-1.1-mini vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-7.json", "title": "BigBang-v1 vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-swift-1-5-qwen3-8-flash-next.json", "title": "BigBang-v1 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minimax-m2-7.json", "title": "Cosmos-Reason2-2B vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Cosmos-Reason2-2B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minimax-m2-7.json", "title": "Darwin-36B-Opus vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Darwin-36B-Opus vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minimax-m2-7.json", "title": "DeepSeek-V3.2 vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-swift-1-5-qwen3-8-flash-next.json", "title": "DeepSeek-V3.2 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minimax-m2-7.json", "title": "DeepSeek V4.1 Flash vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-swift-1-5-qwen3-8-flash-next.json", "title": "DeepSeek V4.1 Flash vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minimax-m2-7.json", "title": "DeepSeek-V4-Flash-0731 vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-swift-1-5-qwen3-8-flash-next.json", "title": "DeepSeek-V4-Flash-0731 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-minimax-m2-7.json", "title": "DiffusionGemma 26B A4B IT vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-swift-1-5-qwen3-8-flash-next.json", "title": 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A4B IT vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-7.json", "title": "Gemma 4 31B IT vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Gemma 4 31B IT vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minimax-m2-7.json", "title": "Gemma 4 E2B IT vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Gemma 4 E2B IT vs Swift 1.5 Qwen3.8-Flash-Next", "url": 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"https://www.anchorterminal.com/compare/glm-4-5-air-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minimax-m2-7.json", "title": "GLM-4.7-Flash vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-swift-1-5-qwen3-8-flash-next.json", "title": "GLM-4.7-Flash vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minimax-m2-7.json", "title": "GLM-5.3-Flash vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-swift-1-5-qwen3-8-flash-next.json", "title": "GLM-5.3-Flash vs Swift 1.5 Qwen3.8-Flash-Next", "url": 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"title": "Ornith-1.5-397B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Ornith-1.5-9B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3-30B-A3B-Instruct-2507 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3-4B-Instruct-2507 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.5-0.8B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.5-122B-A10B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.5-2B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.5-397B-A17B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.5-4B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.5-9B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.6-27B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.6-35B-A3B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.8-27B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.8-Flash-Next vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3-Coder-Next vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3-VL-2B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3-VL-32B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3-VL-4B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3-VL-8B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwopus3.8-27B-Flash-V2 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "SmolLM3-3B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Spark-X2.6-4B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Swift 1.5 Qwen3.8-27B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-thinkingcap-qwen3-8-27b.json", "title": "Swift 1.5 Qwen3.8-Flash-Next vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 0, "edge": "", "key": "reach", "minimax-m2-7": 60, "name": "Reach", "swift-1-5-qwen3-8-flash-next": 60, "weight": 25 }, { "by": 15, "edge": "swift-1-5-qwen3-8-flash-next", "key": "risk", "minimax-m2-7": 30, "name": "Risk", "swift-1-5-qwen3-8-flash-next": 45, "weight": 20 }, { "by": 15, "edge": "minimax-m2-7", "key": "interface", "minimax-m2-7": 75, "name": "Agent interface", "swift-1-5-qwen3-8-flash-next": 60, "weight": 10 }, { "by": 13, "edge": "swift-1-5-qwen3-8-flash-next", "key": "upkeep", "minimax-m2-7": 27, "name": "Documentation and upkeep", "swift-1-5-qwen3-8-flash-next": 40, "weight": 10 } ], "summary": "MiniMax-M2.7 scores 56.1 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in 1 of 4 scored categories. Swift 1.5 Qwen3.8-Flash-Next leads on risk and documentation and upkeep. Both do open-weight models you run yourself.", "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.", "swift-1-5-qwen3-8-flash-next": "A large mixture-of-experts cousin, Qwen3.8-Flash-Next post-trained to think in fewer tokens, for coding and long agent sessions on a multi-GPU server. Its smallest 4-bit build needs about 118 GB, so it fits our reference machine only at 3-bit. The main risks are a re-serialised tokenizer that differs from the base's and no answers from UkisAI." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-flash-next", "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-flash-next.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-flash-next.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-flash-next.min.md" }, "markdown": "MiniMax-M2.7 scores 56.1 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in 1 of 4 scored categories. Swift 1.5 Qwen3.8-Flash-Next leads on risk and documentation and upkeep. Both do open-weight models you run yourself.\n\n- MiniMax-M2.7: grade C, 56.1/100, rank #76 of 100 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- Swift 1.5 Qwen3.8-Flash-Next: grade D, 52.3/100, rank #87 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-flash-next.md · JSON https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-flash-next.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 2870 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- 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### Swift 1.5 Qwen3.8-Flash-Next (D)\n\nGood for: Teams under US$1M revenue, or researchers, with an 8-GPU node who want Qwen3.8-Flash-Next with shorter thinking for coding and agent work.\n\nAhead on:\n- Risk, 45 against 30\n- Documentation and upkeep, 40 against 27\n\nWatch for: Two licences to satisfy, UkisAI's US$1M revenue limit and Qwen's separate licence for model-as-a-service businesses\n\n\n## Score by category\n\n| Category | Weight | MiniMax-M2.7 | Swift 1.5 Qwen3.8-Flash-Next | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 60 | 60 | even |\n| Risk | 20% (20 this run) | 30 | 45 | Swift 1.5 Qwen3.8-Flash-Next +15 |\n| Agent interface | 10% (10 this run) | 75 | 60 | MiniMax-M2.7 +15 |\n| Documentation and upkeep | 10% (10 this run) | 27 | 40 | Swift 1.5 Qwen3.8-Flash-Next +13 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **56.1 · C** | **52.3 · D** | |\n\n## Facts side by side\n\n| Fact | MiniMax-M2.7 | Swift 1.5 Qwen3.8-Flash-Next |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | MiniMax | UkisAI |\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) | Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue, plus the Qwen Community License 1.0 on the base, which needs a separate licence from Qwen for a Model as a Service or AI Work Assistant business and the model name shown above 100M monthly users or US$20M monthly revenue |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-04-20 | 2026-09-24 |\n| Terms last updated | no document linked | |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 1.8/5 (4) | 1.8/5 (4) |\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**Swift 1.5 Qwen3.8-Flash-Next.** A large mixture-of-experts cousin, Qwen3.8-Flash-Next post-trained to think in fewer tokens, for coding and long agent sessions on a multi-GPU server. Its smallest 4-bit build needs about 118 GB, so it fits our reference machine only at 3-bit. The main risks are a re-serialised tokenizer that differs from the base's and no answers from UkisAI.\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### Swift 1.5 Qwen3.8-Flash-Next\n\n1. On a multi-GPU node use `vllm serve ukisai/Swift1.5-Qwen3.8-Flash-Next --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, the parser the base's recipes and the card's SGLang command use\n2. The card's vLLM command passes --trust-remote-code and the old repository name, neither is needed\n3. On our reference machine only UkisAI's GSQ-RCO IQ3_S GGUF (84.7 GB with the projector) fits, below 4-bit\n4. Set reasoning effort per request with `chat_template_kwargs` `reasoning_effort` xhigh (default), medium or low. UkisAI's own runs lose about 2.5 points at medium and low\n5. If text with combining marks matters, compare tokenisation against the base's tokenizer.json before trusting outputs\n6. Check both licences before commercial use, UkisAI's stops at US$1M gross annual revenue and Qwen's needs a separate licence for model-as-a-service businesses\n\n## Questions\n\n### Which is better for AI agents, MiniMax-M2.7 or Swift 1.5 Qwen3.8-Flash-Next?\n\nMiniMax-M2.7 scores 56.1 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in 1 of 4 scored categories. Swift 1.5 Qwen3.8-Flash-Next leads on risk and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-flash-next.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-flash-next.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"minimax-m2-7\", \"b\": \"swift-1-5-qwen3-8-flash-next\"}`. From a terminal: `anchor compare minimax-m2-7 swift-1-5-qwen3-8-flash-next`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/minimax-m2-7.json and https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-flash-next.json\n\n## Other comparisons with MiniMax-M2.7 or Swift 1.5 Qwen3.8-Flash-Next\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 Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/agents-a1-4b-vs-swift-1-5-qwen3-8-flash-next.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 Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-swift-1-5-qwen3-8-flash-next.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 Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-swift-1-5-qwen3-8-flash-next.md)\n- [BigBang-v1 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-7.md)\n- [BigBang-v1 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/bigbang-v1-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Cosmos-Reason2-2B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minimax-m2-7.md)\n- [Cosmos-Reason2-2B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Darwin-36B-Opus vs MiniMax-M2.7](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minimax-m2-7.md)\n- [Darwin-36B-Opus vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-swift-1-5-qwen3-8-flash-next.md)\n- [DeepSeek-V3.2 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minimax-m2-7.md)\n- [DeepSeek-V3.2 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-swift-1-5-qwen3-8-flash-next.md)\n- [DeepSeek V4.1 Flash vs MiniMax-M2.7](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minimax-m2-7.md)\n- [DeepSeek V4.1 Flash vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-swift-1-5-qwen3-8-flash-next.md)\n- [DeepSeek-V4-Flash-0731 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minimax-m2-7.md)\n- [DeepSeek-V4-Flash-0731 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-swift-1-5-qwen3-8-flash-next.md)\n- [DiffusionGemma 26B A4B IT vs MiniMax-M2.7](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-minimax-m2-7.md)\n- [DiffusionGemma 26B A4B IT vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Gemma 4 12B IT vs MiniMax-M2.7](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minimax-m2-7.md)\n- [Gemma 4 12B IT vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Gemma 4 26B A4B IT vs MiniMax-M2.7](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minimax-m2-7.md)\n- [Gemma 4 26B A4B IT vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Gemma 4 31B IT vs MiniMax-M2.7](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-7.md)\n- [Gemma 4 31B IT vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Gemma 4 E2B IT vs MiniMax-M2.7](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minimax-m2-7.md)\n- [Gemma 4 E2B IT vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Gemma 4 E4B IT vs MiniMax-M2.7](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minimax-m2-7.md)\n- [Gemma 4 E4B IT vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-swift-1-5-qwen3-8-flash-next.md)\n- [GLM-4.5-Air vs MiniMax-M2.7](https://www.anchorterminal.com/compare/glm-4-5-air-vs-minimax-m2-7.md)\n- [GLM-4.5-Air vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/glm-4-5-air-vs-swift-1-5-qwen3-8-flash-next.md)\n- [GLM-4.7-Flash vs MiniMax-M2.7](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minimax-m2-7.md)\n- [GLM-4.7-Flash vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-swift-1-5-qwen3-8-flash-next.md)\n- [GLM-5.3-Flash vs MiniMax-M2.7](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minimax-m2-7.md)\n- [GLM-5.3-Flash vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-swift-1-5-qwen3-8-flash-next.md)\n- [GLM-5.3 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/glm-5-3-vs-minimax-m2-7.md)\n- [GLM-5.3 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/glm-5-3-vs-swift-1-5-qwen3-8-flash-next.md)\n- [gpt-oss-120b vs MiniMax-M2.7](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-minimax-m2-7.md)\n- [gpt-oss-120b vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-swift-1-5-qwen3-8-flash-next.md)\n- [gpt-oss-20b vs MiniMax-M2.7](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minimax-m2-7.md)\n- [gpt-oss-20b vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Granite 4.2 3B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minimax-m2-7.md)\n- [Granite 4.2 3B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Hy3 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/hy3-vs-minimax-m2-7.md)\n- [Hy3 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/hy3-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Inkling-Small vs MiniMax-M2.7](https://www.anchorterminal.com/compare/inkling-small-vs-minimax-m2-7.md)\n- [Inkling-Small vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/inkling-small-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Inkling vs MiniMax-M2.7](https://www.anchorterminal.com/compare/inkling-vs-minimax-m2-7.md)\n- [Inkling vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/inkling-vs-swift-1-5-qwen3-8-flash-next.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniMax-M2.7](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minimax-m2-7.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Kimi K3 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/kimi-k3-vs-minimax-m2-7.md)\n- [Kimi K3 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/kimi-k3-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Kolibri-1 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/kolibri-1-vs-minimax-m2-7.md)\n- [Kolibri-1 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/kolibri-1-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Laguna M.1 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-7.md)\n- [Laguna M.1 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/laguna-m-1-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Laguna S 2.1 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minimax-m2-7.md)\n- [Laguna S 2.1 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Laguna XS 2.1 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minimax-m2-7.md)\n- [Laguna XS 2.1 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-swift-1-5-qwen3-8-flash-next.md)\n- [LFM2.5-2.6B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m2-7.md)\n- [LFM2.5-2.6B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Ling-3.0-tiny vs MiniMax-M2.7](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-minimax-m2-7.md)\n- [Ling-3.0-tiny vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-swift-1-5-qwen3-8-flash-next.md)\n- [MiMo-V2.6 Distill Qwen 9B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-minimax-m2-7.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-swift-1-5-qwen3-8-flash-next.md)\n- [MiniCPM-o 4.5 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-minimax-m2-7.md)\n- [MiniCPM-o 4.5 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-flash-next.md)\n- [MiniCPM5-2B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/minicpm5-2b-vs-minimax-m2-7.md)\n- [MiniCPM5-2B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/minicpm5-2b-vs-swift-1-5-qwen3-8-flash-next.md)\n- [MiniMax-M2.5 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/minimax-m2-5-vs-minimax-m2-7.md)\n- [MiniMax-M2.5 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-flash-next.md)\n- [MiniMax-M2.7 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-muse-glimmer-30b.md)\n- [MiniMax-M2.7 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [MiniMax-M2.7 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-nano-30b-a3b.md)\n- [MiniMax-M2.7 vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-nano-4b.md)\n- [MiniMax-M2.7 vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [MiniMax-M2.7 vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-super-120b-a12b.md)\n- [MiniMax-M2.7 vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-ultra-550b-a55b.md)\n- [MiniMax-M2.7 vs occamy-1.0](https://www.anchorterminal.com/compare/minimax-m2-7-vs-occamy-1-0.md)\n- [MiniMax-M2.7 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/minimax-m2-7-vs-olmo-3-7b-think.md)\n- [MiniMax-M2.7 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-35b-a3b.md)\n- [MiniMax-M2.7 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-397b.md)\n- [MiniMax-M2.7 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-9b.md)\n- [MiniMax-M2.7 vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-30b-a3b-instruct-2507.md)\n- [MiniMax-M2.7 vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-4b-instruct-2507.md)\n- [MiniMax-M2.7 vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-0-8b.md)\n- [MiniMax-M2.7 vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-122b-a10b.md)\n- [MiniMax-M2.7 vs Qwen3.5-2B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-2b.md)\n- [MiniMax-M2.7 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-397b-a17b.md)\n- [MiniMax-M2.7 vs Qwen3.5-4B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-4b.md)\n- [MiniMax-M2.7 vs Qwen3.5-9B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-9b.md)\n- [MiniMax-M2.7 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-6-27b.md)\n- [MiniMax-M2.7 vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-6-35b-a3b.md)\n- [MiniMax-M2.7 vs Qwen3.8-27B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-8-27b.md)\n- [MiniMax-M2.7 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-8-flash-next.md)\n- [MiniMax-M2.7 vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-coder-30b-a3b-instruct.md)\n- [MiniMax-M2.7 vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-coder-next.md)\n- [MiniMax-M2.7 vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-omni-30b-a3b-instruct.md)\n- [MiniMax-M2.7 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-2b-instruct.md)\n- [MiniMax-M2.7 vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-30b-a3b-instruct.md)\n- [MiniMax-M2.7 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-32b-instruct.md)\n- [MiniMax-M2.7 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-4b-instruct.md)\n- [MiniMax-M2.7 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-8b-instruct.md)\n- [MiniMax-M2.7 vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwopus3-8-27b-flash-v2.md)\n- [MiniMax-M2.7 vs 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