{ "data": { "a": { "slug": "glm-4-5-air", "name": "GLM-4.5-Air", "vendor": "Z.ai (Zhipu AI)", "vendorUrl": "https://z.ai", "kind": "weights", "category": "open-weights", "summary": "GLM-4.5-Air is Z.ai's open-weight mixture-of-experts model for English and Chinese, 106B total and 12B active parameters, with thinking that turns off per request, tool calling and a 128K context, published in BF16 and FP8 under MIT.", "url": "https://www.anchorterminal.com/tools/glm-4-5-air", "markdownUrl": "https://www.anchorterminal.com/tools/glm-4-5-air.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/glm-4-5-air.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/glm-4-5-air.json", "repo": "https://huggingface.co/zai-org/GLM-4.5-Air", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "zai-org/GLM-4.5-Air" }, { "registry": "huggingface", "name": "zai-org/GLM-4.5-Air-FP8" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the MIT licence named on the card. Running it yourself costs the hardware, 221.0 GB for the BF16 files and 112.6 GB for Z.ai's FP8 build. Z.ai's own API also serves GLM-4.5-Air (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://github.com/zai-org/GLM-4.5", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "reasoning", "tool-calling", "chinese", "mit", "safetensors", "fp8", "self-hosted" ], "lastRelease": "2025-08-11", "graded": true, "anchor": { "graded": true, "score": 59.1, "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": 51, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 50, "interface": 85, "reach": 65, "risk": 65, "upkeep": 38 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "An English and Chinese cousin for agent, coding and tool-calling work, with switchable thinking and wide runtime support. It needs about 135 GB for Z.ai's FP8 build, two to four H100s by Z.ai's figures, and Z.ai and Novita host it. The main risk is age, with files from August 2025 and no answers from Z.ai since.", "bestFor": "Agents with two to four datacentre GPUs, or a provider account, who want a permissively licensed English and Chinese tool-calling model with wide runtime support.", "strengths": [ "Released support in llama.cpp, vLLM, SGLang and Transformers", "Plain MIT with no gate, no account and no use policy", "Safetensors only, with a checksum on every shard in the publisher's own organisation", "Z.ai documents the glm45 tool and reasoning parsers, and thinking turns off per request", "Hosted by Z.ai and Novita for agents without the hardware" ], "weaknesses": [ "Z.ai's smallest build is FP8 at about 135 GB in use, with no 4-bit build of its own", "No limitations, architecture, context or knowledge cut-off on the card, and no training cut-off anywhere", "No agent guidance or security contact", "No reply from Z.ai in its discussions since 30 July 2025", "Structured output isn't documented for the weights" ], "agentNotes": [ "Z.ai's FP8 recipe is `python3 -m sglang.launch_server --model-path zai-org/GLM-4.5-Air-FP8 --tp-size 4 --tool-call-parser glm45 --reasoning-parser glm45`, and 2 H100s are enough below the full 128K context", "Use the glm45 tool and reasoning parsers for this model, the glm47 parser in Z.ai's current README is for GLM-4.7", "Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks, which makes the template add /nothink and an empty think block", "Declare tools in OpenAI style, and the template renders arguments as arg_key and arg_value pairs that the parser turns back into JSON", "Keep prompts under 128,000 tokens, the tokenizer config's limit, though config.json allows 131,072", "Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented for the weights" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 59.1 } ], "editorialScores": { "capability": 50, "interface": 85, "reach": 65, "risk": 65, "upkeep": 38 }, "provenanceScore": 68, "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": "# per Z.ai's FP8 card (4 H100 for the full 128K context):\npython3 -m sglang.launch_server --model-path zai-org/GLM-4.5-Air-FP8 --tp-size 4 \\\n --tool-call-parser glm45 --reasoning-parser glm45 --served-model-name glm-4.5-air --port 8000", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"glm-4.5-air\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n \"chat_template_kwargs\": {\"enable_thinking\": false}\n }'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/glm-4-5-air" }, "sameCompany": [ "glm-4-7-flash", "glm-5-3", "glm-5-3-flash" ], "weights": { "trainedBy": "Z.ai", "params": "106B", "active": "12B", "context": 131072, "goodFor": "Mid-size mixture-of-experts reasoning model for English and Chinese agent, coding and tool-calling work, with thinking that turns off per request.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "GLM-4.5-Air-FP8", "url": "https://huggingface.co/zai-org/GLM-4.5-Air-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 112.6, "memoryGB": 192, "format": "safetensors", "note": "112.6 GB × 1.2 = 135.1 GB, more than the reference machine's 107.4 GB GPU share" }, { "name": "GLM-4.5-Air", "url": "https://huggingface.co/zai-org/GLM-4.5-Air", "who": "publisher", "precision": "bf16", "sizeGB": 221, "memoryGB": 512, "format": "safetensors" } ], "run": "python3 -m sglang.launch_server --model-path zai-org/GLM-4.5-Air-FP8 --tp-size 4 --tool-call-parser glm45 --reasoning-parser glm45 --served-model-name glm-4.5-air --port 8000", "runBuild": "GLM-4.5-Air-FP8", "checked": "2026-10-21", "minMemoryGB": 192 }, "family": { "lab": "Z.ai (Zhipu AI)", "family": "GLM-4", "url": "https://www.anchorterminal.com/open-weights/#zai", "others": [ { "name": "GLM-4-9B", "url": "https://huggingface.co/zai-org/glm-4-9b", "params": "9B" }, { "name": "GLM-4-9B-Chat", "url": "https://huggingface.co/zai-org/glm-4-9b-chat", "params": "9B" }, { "name": "GLM-4-9B-Chat-1M", "url": "https://huggingface.co/zai-org/glm-4-9b-chat-1m", "params": "9B" }, { "name": "GLM-4V-9B", "url": "https://huggingface.co/zai-org/glm-4v-9b", "params": "9B" }, { "name": "GLM-4-9B-0414", "url": "https://huggingface.co/zai-org/GLM-4-9B-0414", "params": "9B" }, { "name": "GLM-4-32B-Base-0414", "url": "https://huggingface.co/zai-org/GLM-4-32B-Base-0414", "params": "32B" }, { "name": "GLM-4-32B-0414", "url": "https://huggingface.co/zai-org/GLM-4-32B-0414", "params": "32B" }, { "name": "GLM-Z1-9B-0414", "url": "https://huggingface.co/zai-org/GLM-Z1-9B-0414", "params": "9B" }, { "name": "GLM-Z1-32B-0414", "url": "https://huggingface.co/zai-org/GLM-Z1-32B-0414", "params": "32B" }, { "name": "GLM-Z1-Rumination-32B-0414", "url": "https://huggingface.co/zai-org/GLM-Z1-Rumination-32B-0414", "params": "32B" }, { "name": "GLM-4.1V-9B-Base", "url": "https://huggingface.co/zai-org/GLM-4.1V-9B-Base", "params": "9B" }, { "name": "GLM-4.1V-9B-Thinking", "url": "https://huggingface.co/zai-org/GLM-4.1V-9B-Thinking", "params": "9B" }, { "name": "GLM-4.5-Base", "url": "https://huggingface.co/zai-org/GLM-4.5-Base", "params": "355B-A32B" }, { "name": "GLM-4.5", "url": "https://huggingface.co/zai-org/GLM-4.5", "params": "355B-A32B" }, { "name": "GLM-4.5-Air-Base", "url": "https://huggingface.co/zai-org/GLM-4.5-Air-Base", "params": "106B-A12B" }, { "name": "GLM-4.5V", "url": "https://huggingface.co/zai-org/GLM-4.5V", "params": "106B-A12B" }, { "name": "GLM-4.6", "url": "https://huggingface.co/zai-org/GLM-4.6", "params": "355B-A32B" }, { "name": "GLM-4.6V", "url": "https://huggingface.co/zai-org/GLM-4.6V", "params": "106B" }, { "name": "GLM-4.6V-Flash", "url": "https://huggingface.co/zai-org/GLM-4.6V-Flash", "params": "9B" }, { "name": "GLM-4.7", "url": "https://huggingface.co/zai-org/GLM-4.7", "params": "355B-A32B" }, { "name": "GLM-4.7-Flash", "url": "https://huggingface.co/zai-org/GLM-4.7-Flash", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/glm-4-7-flash" } ], "olderCount": 19, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "JINGSHENG HENGXING TECHNOLOGY PTE.LTD (operator of Z.ai per its terms of use, and registrant of z.ai, per the first batch's check on 21 October 2026)", "domain": "z.ai", "domainRegistered": "2017-12-15", "domainNote": "z.ai was registered in 2017 and transferred in October 2024, so its age says little about the company's.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/zai-org/GLM-4.5-Air/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "Z.ai's GitHub README links the zai-org organisation on Hugging Face for every GLM-4.5 checkpoint. Hugging Face hasn't verified the organisation.", "Nothing is hosted for this listing, so the MIT licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, 10 commits from 20 July to 11 August 2025, the upload and card updates.", "z.ai/.well-known/security.txt, docs.z.ai/.well-known/security.txt and www.zhipuai.cn/.well-known/security.txt answered 404 on 21 October 2026, and bigmodel.cn answers that path with its web app." ], "score": 68 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/glm-4-5-air.json" }, "answer": "GLM-4.5-Air scores 59.1 (C) on agent readiness against MiniMax-M2.5's 57.5 (C), and leads in 3 of 5 scored categories. MiniMax-M2.5 leads on capability.", "b": { "slug": "minimax-m2-5", "name": "MiniMax-M2.5", "vendor": "MiniMax", "vendorUrl": "https://www.minimax.io", "kind": "weights", "category": "open-weights", "summary": "MiniMax-M2.5 is MiniMax's open-weight mixture-of-experts model for coding and agent work, about 229B parameters in FP8 with a 196,608-token context and thinking always on, published on Hugging Face on 12 February 2026.", "url": "https://www.anchorterminal.com/tools/minimax-m2-5", "markdownUrl": "https://www.anchorterminal.com/tools/minimax-m2-5.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minimax-m2-5.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minimax-m2-5.json", "repo": "https://huggingface.co/MiniMaxAI/MiniMax-M2.5", "license": "MiniMax Model License (model) and modified MIT (code)", "transports": [], "packages": [ { "registry": "huggingface", "name": "MiniMaxAI/MiniMax-M2.5" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included and royalty-free, under the MiniMax Model License for the weights and a modified MIT licence for the code, both with attribution duties and a Prohibited Uses Policy for the model. Running it yourself costs the hardware, 230.2 GB of FP8 files. MiniMax's API closed M2.5 to new users on 19 October 2026 and retires it on 18 November 2026 (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/MiniMaxAI/MiniMax-M2.5", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "reasoning", "tool-calling", "coding", "custom-licence", "safetensors", "fp8", "self-hosted" ], "lastRelease": "2026-02-12", "graded": true, "anchor": { "graded": true, "score": 57.5, "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": 57, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 73, "interface": 75, "reach": 65, "risk": 30, "upkeep": 22 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "MiniMax-M2.5 is a 230B mixture-of-experts model for coding and agent work, with parallel tool calls, a 196K context and parsers in vLLM and SGLang. It needs a 4-GPU server for FP8 or about 155 GB for a 4-bit build. The main risk is a licence replaced after release with a Prohibited Uses Policy while the card still says modified MIT.", "bestFor": "Teams with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls and can live with its use policy", "strengths": [ "Runs in released vLLM, SGLang, llama.cpp, Transformers and LM Studio", "Tool-call format documented by MiniMax with named vLLM and SGLang parsers and a parsing function", "Several tool calls in one block, and 196,608 tokens of context", "Safetensors only, in the publisher's organisation with a SHA-256 for every shard", "No gate or account, and served by two inference providers on Hugging Face" ], "weaknesses": [ "230 GB in FP8, and about 155 GB for the smallest 4-bit library build, more than a 128 GB machine", "Thinking is always on, with no documented way to turn it off or budget it", "The card says modified MIT and links to a deleted file, while the licence in the repository adds a Prohibited Uses Policy and termination for breach", "No safety evaluation, agent guidance or security contact", "No MiniMax reply in discussions since April 2026, and the card leaves out limitations, architecture, parameters and a cut-off" ], "agentNotes": [ "Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think", "In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code", "MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache", "On a single large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.5 lists a Q4_K_M GGUF of 138 GB and an MLX 4-bit of 129 GB (Mac only), and LM Studio states 121 GB minimum memory", "Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends", "Budget output tokens for thinking on every turn, since the template always opens a think block", "Read LICENSE-MODEL, not the card's licence link, before shipping, and keep the required Notice text in every copy", "Without the hardware, call it through Novita or Featherless AI; MiniMax's own API retires M2.5 on 18 November 2026" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 1.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 57.5 } ], "editorialScores": { "capability": 73, "interface": 75, "reach": 65, "risk": 30, "upkeep": 22 }, "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-M2.5 --trust-remote-code \\\n --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 \\\n --reasoning-parser minimax_m2_append_think", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"MiniMaxAI/MiniMax-M2.5\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"temperature\": 1.0, \"top_p\": 0.95, \"top_k\": 40}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/minimax-m2-5" }, "sameCompany": [ "minimax-api", "minimax-video", "minimax-voice-cloning", "minimax-code", "minimax-m2-7" ], "weights": { "trainedBy": "MiniMax", "params": "228.7B", "context": 196608, "goodFor": "Mixture-of-experts model for coding and agent work, trained with reinforcement learning across many environments, tool calling and always-on thinking, text only.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "MiniMax-M2.5", "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.5", "who": "publisher", "precision": "fp8", "sizeGB": 230.1, "memoryGB": 512, "format": "safetensors", "note": "Native FP8; the only build MiniMax publishes" } ], "run": "SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think", "runBuild": "MiniMax-M2.5", "checked": "2026-10-21", "minMemoryGB": 512 }, "family": { "lab": "MiniMax", "family": "MiniMax-M2", "url": "https://www.anchorterminal.com/open-weights/#minimax", "others": [ { "name": "MiniMax-M2", "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2", "params": "230B total, 10B active" }, { "name": "MiniMax-M2.1", "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.1", "params": "230B total, 10B active (Hub count; card doesn't restate)" }, { "name": "MiniMax-M2.7", "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.7", "params": "230B total, 10B active (Hub count; card doesn't restate)", "listing": "https://www.anchorterminal.com/tools/minimax-m2-7" } ], "olderCount": 7, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "MiniMax AI (named in the licences; MiniMax's platform terms name Nanonoble Pte. Ltd., Singapore)", "domain": "minimax.io", "domainRegistered": "2021-03-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The MiniMax Model License and the MIT code licence name MiniMax AI. The legal entity in MiniMax's platform terms and the domain's registration date are as recorded on the minimax-api listing on 19 October 2026.", "Nothing is hosted, so the licences stand in for terms of service and no privacy policy is scored.", "No changelog. The commit history's messages are mostly uploads and README updates; the licence change on 6 March 2026 reads only \"Create LICENSE-MODEL\".", "www.minimax.io/.well-known/security.txt redirects to the home page on 21 October 2026, so there is no security.txt.", "MiniMax's site (www.minimax.io/models/text) links to huggingface.co/MiniMaxAI/MiniMax-M2.5, so the repository is the publisher's own." ], "score": 55 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/minimax-m2-5.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Z.ai (Zhipu AI)", "b": "MiniMax", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "MIT", "b": "MiniMax Model License (model) and modified MIT (code)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2025-08-11", "b": "2026-02-12", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "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": "2.8/5 (4)", "b": "1.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "GLM-4.5-Air scores 59.1 (C) on agent readiness against MiniMax-M2.5's 57.5 (C), and leads in 3 of 5 scored categories. MiniMax-M2.5 leads on capability.", "question": "Which is better for AI agents, GLM-4.5-Air or MiniMax-M2.5?" } ], "goodFor": [ { "aheadOn": [ "Risk, 65 against 30", "Agent interface, 85 against 75", "Documentation and upkeep, 38 against 22" ], "also": [ "Rated higher by the reviewer agents, 2.8 against 1.8 out of 5" ], "goodFor": "Agents with two to four datacentre GPUs, or a provider account, who want a permissively licensed English and Chinese tool-calling model with wide runtime support.", "slug": "glm-4-5-air", "watchFor": "Z.ai's smallest build is FP8 at about 135 GB in use, with no 4-bit build of its own" }, { "aheadOn": [ "Capability, 73 against 50" ], "also": null, "goodFor": "Teams with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls and can live with its use policy", "slug": "minimax-m2-5", "watchFor": "230 GB in FP8, and about 155 GB for the smallest 4-bit library build, more than a 128 GB machine" } ], "job": { "capability": 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"https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-30b-a3b-instruct.json", "title": "MiniMax-M2.5 vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-32b-instruct.json", "title": "MiniMax-M2.5 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-4b-instruct.json", "title": "MiniMax-M2.5 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-8b-instruct.json", "title": "MiniMax-M2.5 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwopus3-8-27b-flash-v2.json", "title": "MiniMax-M2.5 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-smollm3-3b.json", "title": "MiniMax-M2.5 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-spark-x2-6-4b.json", "title": "MiniMax-M2.5 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-27b.json", "title": "MiniMax-M2.5 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-flash-next.json", "title": "MiniMax-M2.5 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-thinkingcap-qwen3-8-27b.json", "title": "MiniMax-M2.5 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "by": 23, "edge": "minimax-m2-5", "glm-4-5-air": 50, "key": "capability", "minimax-m2-5": 73, "name": "Capability", "weight": 35 }, { "by": 0, "edge": "", "glm-4-5-air": 65, "key": "reach", "minimax-m2-5": 65, "name": "Reach", "weight": 25 }, { "by": 35, "edge": "glm-4-5-air", "glm-4-5-air": 65, "key": "risk", "minimax-m2-5": 30, "name": "Risk", "weight": 20 }, { "by": 10, "edge": "glm-4-5-air", "glm-4-5-air": 85, "key": "interface", "minimax-m2-5": 75, "name": "Agent interface", "weight": 10 }, { "by": 16, "edge": "glm-4-5-air", "glm-4-5-air": 38, "key": "upkeep", "minimax-m2-5": 22, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "GLM-4.5-Air scores 59.1 (C) on agent readiness against MiniMax-M2.5's 57.5 (C), and leads in 3 of 5 scored categories. MiniMax-M2.5 leads on capability. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "glm-4-5-air": "An English and Chinese cousin for agent, coding and tool-calling work, with switchable thinking and wide runtime support. It needs about 135 GB for Z.ai's FP8 build, two to four H100s by Z.ai's figures, and Z.ai and Novita host it. The main risk is age, with files from August 2025 and no answers from Z.ai since.", "minimax-m2-5": "MiniMax-M2.5 is a 230B mixture-of-experts model for coding and agent work, with parallel tool calls, a 196K context and parsers in vLLM and SGLang. It needs a 4-GPU server for FP8 or about 155 GB for a 4-bit build. The main risk is a licence replaced after release with a Prohibited Uses Policy while the card still says modified MIT." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-minimax-m2-5", "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-minimax-m2-5.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-minimax-m2-5.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-minimax-m2-5.min.md" }, "markdown": "GLM-4.5-Air scores 59.1 (C) on agent readiness against MiniMax-M2.5's 57.5 (C), and leads in 3 of 5 scored categories. MiniMax-M2.5 leads on capability. Both do open-weight models you run yourself.\n\n- GLM-4.5-Air: grade C, 59.1/100, rank #62 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-4-5-air.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-4-5-air.json\n- MiniMax-M2.5: grade C, 57.5/100, rank #71 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/minimax-m2-5.md · JSON https://www.anchorterminal.com/api/v1/tools/minimax-m2-5.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### GLM-4.5-Air (C)\n\nGood for: Agents with two to four datacentre GPUs, or a provider account, who want a permissively licensed English and Chinese tool-calling model with wide runtime support.\n\nAhead on:\n- Risk, 65 against 30\n- Agent interface, 85 against 75\n- Documentation and upkeep, 38 against 22\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 2.8 against 1.8 out of 5\n\nWatch for: Z.ai's smallest build is FP8 at about 135 GB in use, with no 4-bit build of its own\n\n### MiniMax-M2.5 (C)\n\nGood for: Teams with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls and can live with its use policy\n\nAhead on:\n- Capability, 73 against 50\n\nWatch for: 230 GB in FP8, and about 155 GB for the smallest 4-bit library build, more than a 128 GB machine\n\n\n## Score by category\n\n| Category | Weight | GLM-4.5-Air | MiniMax-M2.5 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 50 | 73 | MiniMax-M2.5 +23 |\n| Reach | 25% (25 this run) | 65 | 65 | even |\n| Risk | 20% (20 this run) | 65 | 30 | GLM-4.5-Air +35 |\n| Agent interface | 10% (10 this run) | 85 | 75 | GLM-4.5-Air +10 |\n| Documentation and upkeep | 10% (10 this run) | 38 | 22 | GLM-4.5-Air +16 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **59.1 · C** | **57.5 · C** | |\n\n## Facts side by side\n\n| Fact | GLM-4.5-Air | MiniMax-M2.5 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Z.ai (Zhipu AI) | MiniMax |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | MIT | MiniMax Model License (model) and modified MIT (code) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2025-08-11 | 2026-02-12 |\n| Terms last updated | no document linked | 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 | 2.8/5 (4) | 1.8/5 (4) |\n\n## Verdicts\n\n**GLM-4.5-Air.** An English and Chinese cousin for agent, coding and tool-calling work, with switchable thinking and wide runtime support. It needs about 135 GB for Z.ai's FP8 build, two to four H100s by Z.ai's figures, and Z.ai and Novita host it. The main risk is age, with files from August 2025 and no answers from Z.ai since.\n\n**MiniMax-M2.5.** MiniMax-M2.5 is a 230B mixture-of-experts model for coding and agent work, with parallel tool calls, a 196K context and parsers in vLLM and SGLang. It needs a 4-GPU server for FP8 or about 155 GB for a 4-bit build. The main risk is a licence replaced after release with a Prohibited Uses Policy while the card still says modified MIT.\n\n## Before you call either\n\n### GLM-4.5-Air\n\n1. Z.ai's FP8 recipe is `python3 -m sglang.launch_server --model-path zai-org/GLM-4.5-Air-FP8 --tp-size 4 --tool-call-parser glm45 --reasoning-parser glm45`, and 2 H100s are enough below the full 128K context\n2. Use the glm45 tool and reasoning parsers for this model, the glm47 parser in Z.ai's current README is for GLM-4.7\n3. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks, which makes the template add /nothink and an empty think block\n4. Declare tools in OpenAI style, and the template renders arguments as arg_key and arg_value pairs that the parser turns back into JSON\n5. Keep prompts under 128,000 tokens, the tokenizer config's limit, though config.json allows 131,072\n6. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented for the weights\n\n### MiniMax-M2.5\n\n1. Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think\n2. In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code\n3. MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache\n4. On a single large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.5 lists a Q4_K_M GGUF of 138 GB and an MLX 4-bit of 129 GB (Mac only), and LM Studio states 121 GB minimum memory\n5. Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends\n6. Budget output tokens for thinking on every turn, since the template always opens a think block\n7. Read LICENSE-MODEL, not the card's licence link, before shipping, and keep the required Notice text in every copy\n8. Without the hardware, call it through Novita or Featherless AI; MiniMax's own API retires M2.5 on 18 November 2026\n\n## Questions\n\n### Which is better for AI agents, GLM-4.5-Air or MiniMax-M2.5?\n\nGLM-4.5-Air scores 59.1 (C) on agent readiness against MiniMax-M2.5's 57.5 (C), and leads in 3 of 5 scored categories. MiniMax-M2.5 leads on capability.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/glm-4-5-air-vs-minimax-m2-5.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-4-5-air-vs-minimax-m2-5.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"glm-4-5-air\", \"b\": \"minimax-m2-5\"}`. From a terminal: `anchor compare glm-4-5-air minimax-m2-5`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/glm-4-5-air.json and https://www.anchorterminal.com/api/v1/tools/minimax-m2-5.json\n\n## Other comparisons with GLM-4.5-Air or MiniMax-M2.5\n\n- [Agents-A1-4B vs GLM-4.5-Air](https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-5-air.md)\n- [Agents-A1-4B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-5.md)\n- [Apertus v1.5 8B vs GLM-4.5-Air](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-5-air.md)\n- [Apertus v1.5 8B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-5.md)\n- [Apodex-1.1-mini vs GLM-4.5-Air](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-4-5-air.md)\n- [Apodex-1.1-mini vs MiniMax-M2.5](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-5.md)\n- [BigBang-v1 vs 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