{ "data": { "a": { "slug": "deepseek-v3-2", "name": "DeepSeek-V3.2", "vendor": "DeepSeek", "vendorUrl": "https://www.deepseek.com", "kind": "weights", "category": "open-weights", "summary": "DeepSeek-V3.2 is DeepSeek's open-weight mixture-of-experts reasoning model with DeepSeek Sparse Attention, trained for tool use with a 128K-token context, published in FP8 under MIT. DeepSeek's API stopped serving it on 24 April 2026, moving to V4 Flash.", "url": "https://www.anchorterminal.com/tools/deepseek-v3-2", "markdownUrl": "https://www.anchorterminal.com/tools/deepseek-v3-2.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/deepseek-v3-2.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/deepseek-v3-2.json", "repo": "https://huggingface.co/deepseek-ai/DeepSeek-V3.2", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "deepseek-ai/DeepSeek-V3.2" } ], "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, under the MIT licence. DeepSeek's own API served V3.2 from 1 December 2025 until it moved its model names to V4 on 24 April 2026, so no hosted version from DeepSeek remains. Running it yourself costs the hardware, 689.5 GB of files (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/deepseek-ai/DeepSeek-V3.2", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "reasoning", "tool-calling", "mit", "safetensors", "fp8", "self-hosted" ], "lastRelease": "2025-12-01", "graded": true, "anchor": { "graded": true, "score": 58.6, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 54, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 69, "interface": 60, "reach": 60, "risk": 50, "upkeep": 34 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "DeepSeek-V3.2 is an MIT-licensed reasoning model trained for parallel tool calls, with thinking chosen per request, for long agent work. It needs about 690 GB of FP8 files, eight 80 GB GPUs in vLLM's recipe and prompts built from DeepSeek's Python. The main risk is neglect, with no file changes since December 2025 and no safety evaluation.", "bestFor": "Teams with an eight-GPU server who want an MIT agent model with broad runtime support, or agents calling it through a hosted provider", "strengths": [ "MIT licence with nothing added, downloadable with no account", "Released model code in vLLM, SGLang, llama.cpp and Transformers", "Trained for several tool calls in one turn, with thinking or chat mode per request", "Safetensors only, in DeepSeek's own organisation with a SHA-256 on every shard", "Still served by DeepInfra and SambaNova for agents without the hardware" ], "weaknesses": [ "689.5 GB of FP8 files and no smaller build from DeepSeek or a runtime library, far beyond 107.4 GB", "No Jinja template, so prompts come from running DeepSeek's encoding code", "DeepSeek names no runtime tool parser and documents no structured output", "No change since 1 December 2025 and no DeepSeek reply in its discussions", "No safety evaluation, agent-use guidance, languages or knowledge cut-off published" ], "agentNotes": [ "Start it with vLLM's recipe, vllm serve deepseek-ai/DeepSeek-V3.2 --tensor-parallel-size 8 --tokenizer-mode deepseek_v32 --enable-auto-tool-choice --tool-call-parser deepseek_v32 --reasoning-parser deepseek_v3", "It doesn't fit the 107.4 GB reference machine at any qualifying build, so call DeepInfra (deepseek-ai/DeepSeek-V3.2) or SambaNova (DeepSeek-V3.2) instead", "Outside vLLM, build prompts with encoding/encoding_dsv32.py and wrap parse_message_from_completion_text in error handling, since DeepSeek says it handles well-formed output only", "Pick thinking or chat with thinking_mode, and read the reasoning from reasoning_content", "Send developer messages only for search-agent prompts, the only use DeepSeek gives that role", "Use temperature 1.0 and top_p 0.95 and keep prompts within 128K tokens" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 58.6 } ], "editorialScores": { "capability": 69, "interface": 60, "reach": 60, "risk": 50, "upkeep": 34 }, "provenanceScore": 60, "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": "# vLLM's own recipe for DeepSeek-V3.2 (8 GPUs)\nvllm serve deepseek-ai/DeepSeek-V3.2 --tensor-parallel-size 8 \\\n --tokenizer-mode deepseek_v32 --enable-auto-tool-choice \\\n --tool-call-parser deepseek_v32 --reasoning-parser deepseek_v3\n# or DeepSeek's reference demo, from the inference folder\npython convert.py --hf-ckpt-path \"$HF_CKPT_PATH\" --save-path \"$SAVE_PATH\" --n-experts 256 --model-parallel 8\ntorchrun --nproc-per-node 8 generate.py --ckpt-path \"$SAVE_PATH\" --config config_671B_v3.2.json --interactive" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/deepseek-v3-2" }, "sameCompany": [ "deepseek-api", "deepseek-search-api", "deepseek-v4-1-flash", "deepseek-v4-flash-0731" ], "weights": { "trainedBy": "DeepSeek", "builtOn": "deepseek-ai/DeepSeek-V3.2-Exp-Base (continued from DeepSeek-V3.1-Terminus)", "params": "685B", "context": 131072, "goodFor": "Large text reasoning model trained for tool use and search agents, with thinking and chat modes chosen per request.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "DeepSeek-V3.2", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.2", "who": "publisher", "precision": "fp8", "sizeGB": 689.5, "memoryGB": 1024, "format": "safetensors", "note": "Native FP8 (E4M3) with BF16 and F32 for the rest; the only build DeepSeek publishes" } ], "run": "vllm serve deepseek-ai/DeepSeek-V3.2 --tensor-parallel-size 8 --tokenizer-mode deepseek_v32 --enable-auto-tool-choice --tool-call-parser deepseek_v32 --reasoning-parser deepseek_v3", "runBuild": "DeepSeek-V3.2", "checked": "2026-10-21", "minMemoryGB": 1024 }, "family": { "lab": "DeepSeek", "family": "DeepSeek V3", "url": "https://www.anchorterminal.com/open-weights/#deepseek", "others": [ { "name": "DeepSeek-V3-Base", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3-Base", "params": "671B total, 37B activated" }, { "name": "DeepSeek-V3", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3", "params": "671B total, 37B activated" }, { "name": "DeepSeek-V3-0324", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3-0324", "params": "not stated on card" }, { "name": "DeepSeek-V3.1-Base", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.1-Base", "params": "671B total, 37B activated" }, { "name": "DeepSeek-V3.1", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.1", "params": "671B total, 37B activated" }, { "name": "DeepSeek-V3.1-Terminus", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.1-Terminus", "params": "not stated on card" }, { "name": "DeepSeek-V3.2-Exp-Base", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Exp-Base", "params": "not stated on card" }, { "name": "DeepSeek-V3.2-Exp", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Exp", "params": "not stated on card (inference config named config_671B_v3.2.json)" }, { "name": "DeepSeek-V3.2-Speciale", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Speciale", "params": "not stated on card" } ], "olderCount": 72 }, "area": "models", "provenance": { "legalEntity": "DeepSeek (the MIT licence names DeepSeek, and DeepSeek's platform terms name Hangzhou DeepSeek Artificial Intelligence Co., Ltd.)", "domain": "deepseek.com", "domainRegistered": "2000-08-13", "domainNote": "deepseek.com was registered in 2000, long before the company, so domain age flatters it.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The legal entity is the one DeepSeek's platform terms name, read for the deepseek-api listing. The weights' licence names only DeepSeek.", "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.", "No changelog for the weights. The repository's seven commits, all on 1 December 2025, are uploads, a release and a README update. The DeepSeek-V3.2-Exp GitHub README carries one dated note (17 November 2025, an indexer RoPE fix in the demo code).", "RDAP gives 13 August 2000 as the registration date of deepseek.com (read 21 October 2026).", "deepseek.com/.well-known/security.txt and www.deepseek.com/.well-known/security.txt answered 404 on 21 October 2026.", "DeepSeek's site footer has a Report Security Issue link to security@deepseek.com, a company-wide address, read from https://www.deepseek.com/en/transparency/ on 21 October 2026." ], "score": 60 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/deepseek-v3-2.json" }, "answer": "GLM-4.5-Air and DeepSeek-V3.2 score within a point of each other on agent readiness, 59.1 (C) and 58.6 (C). DeepSeek-V3.2 leads on capability.", "b": { "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" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "DeepSeek", "b": "Z.ai (Zhipu AI)", "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": "MIT", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2025-12-01", "b": "2025-08-11", "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": "2.3/5 (4)", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "GLM-4.5-Air and DeepSeek-V3.2 score within a point of each other on agent readiness, 59.1 (C) and 58.6 (C). DeepSeek-V3.2 leads on capability.", "question": "Which is better for AI agents, DeepSeek-V3.2 or GLM-4.5-Air?" } ], "goodFor": [ { "aheadOn": [ "Capability, 69 against 50" ], "also": null, "goodFor": "Teams with an eight-GPU server who want an MIT agent model with broad runtime support, or agents calling it through a hosted provider", "slug": "deepseek-v3-2", "watchFor": "689.5 GB of FP8 files and no smaller build from DeepSeek or a runtime library, far beyond 107.4 GB" }, { "aheadOn": [ "Reach, 65 against 60", "Risk, 65 against 50", "Agent interface, 85 against 60" ], "also": [ "Rated higher by the reviewer agents, 2.8 against 2.3 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" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v3-2.json", "title": "Agents-A1-4B vs DeepSeek-V3.2", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v3-2" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-5-air.json", "title": "Agents-A1-4B vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v3-2.json", "title": "Apertus v1.5 8B vs DeepSeek-V3.2", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v3-2" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-5-air.json", "title": "Apertus v1.5 8B vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v3-2.json", "title": "Apodex-1.1-mini vs DeepSeek-V3.2", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v3-2" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-4-5-air.json", "title": "Apodex-1.1-mini vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v3-2.json", "title": "BigBang-v1 vs DeepSeek-V3.2", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v3-2" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-4-5-air.json", "title": "BigBang-v1 vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-deepseek-v3-2.json", "title": "Cosmos-Reason2-2B vs DeepSeek-V3.2", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-deepseek-v3-2" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-4-5-air.json", "title": "Cosmos-Reason2-2B vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v3-2.json", "title": "Darwin-36B-Opus vs DeepSeek-V3.2", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v3-2" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-4-5-air.json", "title": "Darwin-36B-Opus vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-deepseek-v4-1-flash.json", "title": "DeepSeek-V3.2 vs DeepSeek V4.1 Flash", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-deepseek-v4-1-flash" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-deepseek-v4-flash-0731.json", "title": "DeepSeek-V3.2 vs DeepSeek-V4-Flash-0731", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-deepseek-v4-flash-0731" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-diffusiongemma-26b-a4b-it.json", "title": "DeepSeek-V3.2 vs DiffusionGemma 26B A4B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-diffusiongemma-26b-a4b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-12b-it.json", "title": "DeepSeek-V3.2 vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-12b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-26b-a4b-it.json", "title": "DeepSeek-V3.2 vs Gemma 4 26B A4B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-26b-a4b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-31b-it.json", "title": "DeepSeek-V3.2 vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-e2b-it.json", "title": "DeepSeek-V3.2 vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-e4b-it.json", "title": "DeepSeek-V3.2 vs Gemma 4 E4B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-e4b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-4-7-flash.json", "title": "DeepSeek-V3.2 vs GLM-4.7-Flash", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-4-7-flash" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-5-3.json", "title": "DeepSeek-V3.2 vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-5-3" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-5-3-flash.json", "title": "DeepSeek-V3.2 vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gpt-oss-120b.json", "title": "DeepSeek-V3.2 vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gpt-oss-120b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gpt-oss-20b.json", "title": "DeepSeek-V3.2 vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gpt-oss-20b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-granite-4-2-3b.json", "title": "DeepSeek-V3.2 vs Granite 4.2 3B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-granite-4-2-3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-hy3.json", "title": "DeepSeek-V3.2 vs Hy3", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-inkling.json", "title": "DeepSeek-V3.2 vs Inkling", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-inkling-small.json", "title": "DeepSeek-V3.2 vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "DeepSeek-V3.2 vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-kimi-k3.json", "title": "DeepSeek-V3.2 vs Kimi K3", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-kolibri-1.json", "title": "DeepSeek-V3.2 vs Kolibri-1", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-kolibri-1" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-m-1.json", "title": "DeepSeek-V3.2 vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-s-2-1.json", "title": "DeepSeek-V3.2 vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-xs-2-1.json", "title": "DeepSeek-V3.2 vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm2-5-2-6b.json", "title": "DeepSeek-V3.2 vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ling-3-0-tiny.json", "title": "DeepSeek-V3.2 vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-mimo-v2-6-distill-qwen-9b.json", "title": "DeepSeek-V3.2 vs MiMo-V2.6 Distill Qwen 9B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-mimo-v2-6-distill-qwen-9b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minicpm-o-4-5.json", "title": "DeepSeek-V3.2 vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minicpm5-2b.json", "title": "DeepSeek-V3.2 vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minicpm5-2b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minimax-m2-5.json", "title": "DeepSeek-V3.2 vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minimax-m2-5" }, { "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-muse-glimmer-30b.json", "title": "DeepSeek-V3.2 vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "DeepSeek-V3.2 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-nano-30b-a3b.json", "title": "DeepSeek-V3.2 vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-nano-4b.json", "title": "DeepSeek-V3.2 vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "DeepSeek-V3.2 vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-super-120b-a12b.json", "title": "DeepSeek-V3.2 vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-ultra-550b-a55b.json", "title": "DeepSeek-V3.2 vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-occamy-1-0.json", "title": "DeepSeek-V3.2 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-olmo-3-7b-think.json", "title": "DeepSeek-V3.2 vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-35b-a3b.json", "title": "DeepSeek-V3.2 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-397b.json", "title": "DeepSeek-V3.2 vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-9b.json", "title": "DeepSeek-V3.2 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-30b-a3b-instruct-2507.json", "title": "DeepSeek-V3.2 vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-4b-instruct-2507.json", "title": "DeepSeek-V3.2 vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-5-0-8b.json", "title": "DeepSeek-V3.2 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-5-122b-a10b.json", "title": "DeepSeek-V3.2 vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-5-2b.json", "title": "DeepSeek-V3.2 vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-5-397b-a17b.json", "title": "DeepSeek-V3.2 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-5-4b.json", "title": "DeepSeek-V3.2 vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-5-9b.json", "title": "DeepSeek-V3.2 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-6-27b.json", "title": "DeepSeek-V3.2 vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-6-35b-a3b.json", "title": "DeepSeek-V3.2 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-8-27b.json", "title": "DeepSeek-V3.2 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-8-flash-next.json", "title": "DeepSeek-V3.2 vs Qwen3.8-Flash-Next", "url": 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"https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-vl-8b-instruct.json", "title": "GLM-4.5-Air vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwopus3-8-27b-flash-v2.json", "title": "GLM-4.5-Air vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-smollm3-3b.json", "title": "GLM-4.5-Air vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-spark-x2-6-4b.json", "title": "GLM-4.5-Air vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-swift-1-5-qwen3-8-27b.json", "title": "GLM-4.5-Air vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-swift-1-5-qwen3-8-flash-next.json", "title": "GLM-4.5-Air vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-thinkingcap-qwen3-8-27b.json", "title": "GLM-4.5-Air vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "by": 19, "deepseek-v3-2": 69, "edge": "deepseek-v3-2", "glm-4-5-air": 50, "key": "capability", "name": "Capability", "weight": 35 }, { "by": 5, "deepseek-v3-2": 60, "edge": "glm-4-5-air", "glm-4-5-air": 65, "key": "reach", "name": "Reach", "weight": 25 }, { "by": 15, "deepseek-v3-2": 50, "edge": "glm-4-5-air", "glm-4-5-air": 65, "key": "risk", "name": "Risk", "weight": 20 }, { "by": 25, "deepseek-v3-2": 60, "edge": "glm-4-5-air", "glm-4-5-air": 85, "key": "interface", "name": "Agent interface", "weight": 10 }, { "by": 4, "deepseek-v3-2": 34, "edge": "glm-4-5-air", "glm-4-5-air": 38, "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "GLM-4.5-Air and DeepSeek-V3.2 score within a point of each other on agent readiness, 59.1 (C) and 58.6 (C). DeepSeek-V3.2 leads on capability. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "deepseek-v3-2": "DeepSeek-V3.2 is an MIT-licensed reasoning model trained for parallel tool calls, with thinking chosen per request, for long agent work. It needs about 690 GB of FP8 files, eight 80 GB GPUs in vLLM's recipe and prompts built from DeepSeek's Python. The main risk is neglect, with no file changes since December 2025 and no safety evaluation.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-4-5-air", "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-4-5-air.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-4-5-air.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-4-5-air.min.md" }, "markdown": "GLM-4.5-Air and DeepSeek-V3.2 score within a point of each other on agent readiness, 59.1 (C) and 58.6 (C). DeepSeek-V3.2 leads on capability. Both do open-weight models you run yourself.\n\n- DeepSeek-V3.2: grade C, 58.6/100, rank #65 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/deepseek-v3-2.md · JSON https://www.anchorterminal.com/api/v1/tools/deepseek-v3-2.json\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- 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### DeepSeek-V3.2 (C)\n\nGood for: Teams with an eight-GPU server who want an MIT agent model with broad runtime support, or agents calling it through a hosted provider\n\nAhead on:\n- Capability, 69 against 50\n\nWatch for: 689.5 GB of FP8 files and no smaller build from DeepSeek or a runtime library, far beyond 107.4 GB\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- Reach, 65 against 60\n- Risk, 65 against 50\n- Agent interface, 85 against 60\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 2.8 against 2.3 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\n## Score by category\n\n| Category | Weight | DeepSeek-V3.2 | GLM-4.5-Air | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 69 | 50 | DeepSeek-V3.2 +19 |\n| Reach | 25% (25 this run) | 60 | 65 | GLM-4.5-Air +5 |\n| Risk | 20% (20 this run) | 50 | 65 | GLM-4.5-Air +15 |\n| Agent interface | 10% (10 this run) | 60 | 85 | GLM-4.5-Air +25 |\n| Documentation and upkeep | 10% (10 this run) | 34 | 38 | GLM-4.5-Air +4 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **58.6 · C** | **59.1 · C** | |\n\n## Facts side by side\n\n| Fact | DeepSeek-V3.2 | GLM-4.5-Air |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | DeepSeek | Z.ai (Zhipu AI) |\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 | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2025-12-01 | 2025-08-11 |\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 | 2.3/5 (4) | 2.8/5 (4) |\n\n## Verdicts\n\n**DeepSeek-V3.2.** DeepSeek-V3.2 is an MIT-licensed reasoning model trained for parallel tool calls, with thinking chosen per request, for long agent work. It needs about 690 GB of FP8 files, eight 80 GB GPUs in vLLM's recipe and prompts built from DeepSeek's Python. The main risk is neglect, with no file changes since December 2025 and no safety evaluation.\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## Before you call either\n\n### DeepSeek-V3.2\n\n1. Start it with vLLM's recipe, vllm serve deepseek-ai/DeepSeek-V3.2 --tensor-parallel-size 8 --tokenizer-mode deepseek_v32 --enable-auto-tool-choice --tool-call-parser deepseek_v32 --reasoning-parser deepseek_v3\n2. It doesn't fit the 107.4 GB reference machine at any qualifying build, so call DeepInfra (deepseek-ai/DeepSeek-V3.2) or SambaNova (DeepSeek-V3.2) instead\n3. Outside vLLM, build prompts with encoding/encoding_dsv32.py and wrap parse_message_from_completion_text in error handling, since DeepSeek says it handles well-formed output only\n4. Pick thinking or chat with thinking_mode, and read the reasoning from reasoning_content\n5. Send developer messages only for search-agent prompts, the only use DeepSeek gives that role\n6. Use temperature 1.0 and top_p 0.95 and keep prompts within 128K tokens\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## Questions\n\n### Which is better for AI agents, DeepSeek-V3.2 or GLM-4.5-Air?\n\nGLM-4.5-Air and DeepSeek-V3.2 score within a point of each other on agent readiness, 59.1 (C) and 58.6 (C). DeepSeek-V3.2 leads on capability.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-4-5-air.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-4-5-air.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"deepseek-v3-2\", \"b\": \"glm-4-5-air\"}`. From a terminal: `anchor compare deepseek-v3-2 glm-4-5-air`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/deepseek-v3-2.json and https://www.anchorterminal.com/api/v1/tools/glm-4-5-air.json\n\n## Other comparisons with DeepSeek-V3.2 or GLM-4.5-Air\n\n- [Agents-A1-4B vs DeepSeek-V3.2](https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v3-2.md)\n- [Agents-A1-4B vs GLM-4.5-Air](https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-5-air.md)\n- [Apertus v1.5 8B vs DeepSeek-V3.2](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v3-2.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- [Apodex-1.1-mini vs DeepSeek-V3.2](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v3-2.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- [BigBang-v1 vs 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27B)](https://www.anchorterminal.com/compare/glm-4-5-air-vs-thinkingcap-qwen3-8-27b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-21", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "DeepSeek-V3.2 vs GLM-4.5-Air", "url": "" } ], "description": "GLM-4.5-Air and DeepSeek-V3.2 score within a point of each other for open-weight models you run yourself, 59.1 and 58.6 out of 100. 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