{ "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": "Qwopus3.8-27B-Flash-V2 scores 61 (C) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 2 of 4 scored categories.", "b": { "slug": "qwopus3-8-27b-flash-v2", "name": "Qwopus3.8-27B-Flash-V2", "vendor": "Jackrong", "vendorUrl": "https://huggingface.co/Jackrong", "kind": "weights", "category": "open-weights", "summary": "Qwopus3.8-27B-Flash-V2 is Jackrong's open-weight 27B fine-tune of Qwen3.8-27B, by way of his Qwopus3.8-27B-Flash, trained for shorter reasoning in agent loops, published in BF16 and GGUF with a multi-token prediction head under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwopus3-8-27b-flash-v2", "markdownUrl": "https://www.anchorterminal.com/tools/qwopus3-8-27b-flash-v2.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwopus3-8-27b-flash-v2.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwopus3-8-27b-flash-v2.json", "repo": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2", "license": "Apache-2.0 (metadata only, no LICENSE file); built on Qwen3.8-27B under Apache 2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Jackrong/Qwopus3.8-27B-Flash-V2" }, { "registry": "huggingface", "name": "Jackrong/Qwopus3.8-27B-Flash-V2-GGUF" } ], "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 under Apache 2.0, the same licence as the Qwen3.8-27B base. Nothing is hosted. Running it yourself costs the hardware, 55.6 GB for the BF16 files and 15.4 GB for the smallest 4-bit GGUF (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/Jackrong/Qwopus3.8-27B-Flash-V2", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "reasoning", "tool-calling", "derivative", "fine-tune", "apache-2.0", "safetensors", "gguf", "mtp", "self-hosted" ], "lastRelease": "2026-09-21", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 39, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 60, "reach": 85, "risk": 50, "upkeep": 56 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A Qwen3.8-27B cousin tuned to think less per turn in long agent loops, with an MTP head for speculative decoding in llama.cpp. It fits 24 GB with Jackrong's 4-bit GGUF. The risks are one person's experimental release with no safety work, training traces taken from Claude and GPT, and a tool format that differs from Qwen's.", "bestFor": "Hobbyist and research agents that want a faster-finishing Qwen3.8-27B for local coding loops on a 24 GB GPU.", "strengths": [ "Jackrong's 4-bit GGUFs fit 24 GB, with many precisions up to BF16", "The base's architecture, so released vLLM, SGLang, Transformers and llama.cpp run it", "Apache 2.0 through the whole chain, with no gate or account", "Safetensors and GGUF only, with a checksum on every file", "Bundled MTP head for self-speculative decoding, and reasoning effort set per request", "A card that states its limitations and how its own tests were run" ], "weaknesses": [ "Published from a personal account, with no organisation, security contact or safety evaluation", "Training data includes traces derived from Claude and GPT, by the card's own account", "Tool calls switch to a Hermes-style JSON format that the card doesn't document", "Reasoning output location, context and cut-off aren't documented", "Reports of looping and a grammar failure wait without a reply" ], "agentNotes": [ "Quickest start is `llama-server -hf Jackrong/Qwopus3.8-27B-Flash-V2-GGUF:Q4_K_M --jinja`, about 17 GB, and add mmproj-F32.gguf for images", "The card's tests used Q5_K_M with the bundled MTP head and `--spec-type draft-mtp` in llama.cpp", "The template asks for tool calls as JSON inside tool_call tags, so use a Hermes-style parser (vLLM's hermes), not Qwen3.8's qwen3_coder", "For coding the card recommends enable_thinking true, reasoning_effort xhigh and temperature 0.3, which you must pass yourself, since generation_config.json says 1.0", "reasoning_effort takes xhigh (default), medium or low, and any other value makes the template raise an error", "Earlier reasoning stays in the context unless preserve_thinking is false", "Cap max_tokens, since a report says it loops more than V1" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 60, "reach": 85, "risk": 50, "upkeep": 56 }, "provenanceScore": 40, "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": "llama-server -hf Jackrong/Qwopus3.8-27B-Flash-V2-GGUF:Q4_K_M --jinja -c 65536\n# or, with vLLM:\nvllm serve Jackrong/Qwopus3.8-27B-Flash-V2 --max-model-len 131072 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser hermes", "http": "curl http://localhost:8080/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"temperature\": 0.3, \"chat_template_kwargs\": {\"enable_thinking\": true, \"reasoning_effort\": \"xhigh\"}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwopus3-8-27b-flash-v2" }, "weights": { "trainedBy": "Jackrong", "builtOn": "Jackrong/Qwopus3.8-27B-Flash (fine-tune, Apache 2.0), itself a fine-tune of Qwen/Qwen3.8-27B (Apache 2.0); declared in the card's metadata", "params": "27.8B", "context": 262144, "goodFor": "A 27B vision-language fine-tune for long agent loops and coding, trained to reason less per turn, with an MTP head for speculative decoding.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Qwopus3.8-27B-Flash-V2-MTP-IQ4_XS.gguf", "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-IQ4_XS.gguf", "who": "publisher", "precision": "iq4_xs", "sizeGB": 15.42, "memoryGB": 24, "format": "gguf", "note": "Vision needs mmproj-F32.gguf as well (1.84 GB)" }, { "name": "Qwopus3.8-27B-Flash-V2-MTP-Q4_K_S.gguf", "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-Q4_K_S.gguf", "who": "publisher", "precision": "q4_k_s", "sizeGB": 15.83, "memoryGB": 24, "format": "gguf" }, { "name": "Qwopus3.8-27B-Flash-V2-MTP-Q4_K_M.gguf", "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 16.81, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp", "Ollama" ] }, { "name": "Qwopus3.8-27B-Flash-V2-MTP-Q5_K_M.gguf", "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-Q5_K_M.gguf", "who": "publisher", "precision": "q5_k_m", "sizeGB": 19.54, "memoryGB": 24, "format": "gguf", "note": "The quantisation the card's own tests used, with --spec-type draft-mtp" }, { "name": "Qwopus3.8-27B-Flash-V2-MTP-Q6_K.gguf", "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 22.43, "memoryGB": 32, "format": "gguf" }, { "name": "Qwopus3.8-27B-Flash-V2-MTP-Q8_0.gguf", "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 29.05, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "Qwopus3.8-27B-Flash-V2", "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2", "who": "publisher", "precision": "bf16", "sizeGB": 55.58, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ] }, { "name": "Qwopus3.8-27B-Flash-V2-MTP-BF16.gguf", "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-BF16.gguf", "who": "publisher", "precision": "bf16", "sizeGB": 54.66, "memoryGB": 96, "format": "gguf", "note": "Jackrong also publishes Q3_K_L, Q3_K_M, Q3_K_S and Q2_K files (10.9 to 14.6 GB), below 4-bit precision" } ], "run": "llama-server -hf Jackrong/Qwopus3.8-27B-Flash-V2-GGUF:Q8_0 --jinja -c 131072", "runBuild": "Qwopus3.8-27B-Flash-V2-MTP-Q8_0.gguf", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "Jackrong", "family": "Qwopus3", "url": "https://www.anchorterminal.com/open-weights/#jackrong", "others": [ { "name": "Qwopus3.5-9B-v3", "url": "https://huggingface.co/Jackrong/Qwopus3.5-9B-v3", "params": "9B (9,653,104,368 by the Hub's safetensors count)" }, { "name": "Qwopus3.5-27B-v3", "url": "https://huggingface.co/Jackrong/Qwopus3.5-27B-v3", "params": "27B (27,356,728,560 by the Hub's safetensors count)" }, { "name": "Qwopus3.5-4B-v3", "url": "https://huggingface.co/Jackrong/Qwopus3.5-4B-v3", "params": "4B (4,659,865,088 by the Hub's safetensors count)" }, { "name": "Qwopus3.5-0.8B-v3", "url": "https://huggingface.co/Jackrong/Qwopus3.5-0.8B-v3", "params": "0.8B (873,438,784 by the Hub's safetensors count)" }, { "name": "Qwopus3.5-27B-v3.5", "url": "https://huggingface.co/Jackrong/Qwopus3.5-27B-v3.5", "params": "27B (27,356,728,560 by the Hub's safetensors count)" }, { "name": "Qwopus3.5-2B-v3", "url": "https://huggingface.co/Jackrong/Qwopus3.5-2B-v3", "params": "2B (2,274,069,824 by the Hub's safetensors count)" }, { "name": "Qwopus3.5-9B-v3.5", "url": "https://huggingface.co/Jackrong/Qwopus3.5-9B-v3.5", "params": "9B (9,653,104,368 by the Hub's safetensors count)" }, { "name": "Qwopus-GLM-18B-Healed", "url": "https://huggingface.co/Jackrong/Qwopus-GLM-18B-Healed", "params": "18B (16,572,666,096 by the Hub's safetensors count)" }, { "name": "Qwopus3.6-27B-v1-preview", "url": "https://huggingface.co/Jackrong/Qwopus3.6-27B-v1-preview", "params": "27B (27,781,427,952 by the Hub's safetensors count)" }, { "name": "Qwopus3.6-35B-A3B-v1", "url": "https://huggingface.co/Jackrong/Qwopus3.6-35B-A3B-v1", "params": "35B total, 3B active (MoE; 35,951,822,704 by the Hub's safetensors count)" }, { "name": "Qwopus3.6-27B-v2", "url": "https://huggingface.co/Jackrong/Qwopus3.6-27B-v2", "params": "27B (27,781,427,952 by the Hub's safetensors count)" }, { "name": "Qwopus3.5-9B-Coder", "url": "https://huggingface.co/Jackrong/Qwopus3.5-9B-Coder", "params": "9B (9,653,104,368 by the Hub's safetensors count)" }, { "name": "Qwopus3.5-4B-Coder", "url": "https://huggingface.co/Jackrong/Qwopus3.5-4B-Coder", "params": "4B (4,659,865,088 by the Hub's safetensors count)" }, { "name": "Qwopus3.6-27B-Coder", "url": "https://huggingface.co/Jackrong/Qwopus3.6-27B-Coder", "params": "27B (27,781,427,952 by the Hub's safetensors count)" }, { "name": "Qwopus3.6-35B-A3B-Coder", "url": "https://huggingface.co/Jackrong/Qwopus3.6-35B-A3B-Coder", "params": "35B total, 3B active (MoE; 35,951,822,704 by the Hub's safetensors count)" }, { "name": "Qwopus3.6-27B-Fusion-BF16", "url": "https://huggingface.co/Jackrong/Qwopus3.6-27B-Fusion-BF16", "params": "27B (27,320,697,856 by the Hub's GGUF count)" }, { "name": "Qwopus3.8-27B-Flash", "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash", "params": "27B (27,781,427,952 by the Hub's safetensors count)" }, { "name": "Qwopus3-5-9B-Coder-Flash", "url": "https://huggingface.co/Jackrong/Qwopus3-5-9B-Coder-Flash", "params": "9B (9,653,104,368 by the Hub's safetensors count)" } ], "olderCount": 53, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Jackrong, an individual Hugging Face account", "domain": "", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "Jackrong is a personal account (158 models, 5,852 followers), not an organisation, and no website or GitHub link is on the card. The card sends questions to a tester's X account.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.", "No changelog. The repository has one commit (21 September 2026); the card describes what changed from the first Flash release.", "No security contact or disclosure policy was found." ], "score": 40 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwopus3-8-27b-flash-v2.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "DeepSeek", "b": "Jackrong", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "MIT", "b": "Apache-2.0 (metadata only, no LICENSE file); built on Qwen3.8-27B under Apache 2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2025-12-01", "b": "2026-09-21", "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": "2.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Qwopus3.8-27B-Flash-V2 scores 61 (C) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 2 of 4 scored categories.", "question": "Which is better for AI agents, DeepSeek-V3.2 or Qwopus3.8-27B-Flash-V2?" } ], "goodFor": [ { "aheadOn": null, "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, 85 against 60", "Documentation and upkeep, 56 against 34" ], "also": null, "goodFor": "Hobbyist and research agents that want a faster-finishing Qwen3.8-27B for local coding loops on a 24 GB GPU.", "slug": "qwopus3-8-27b-flash-v2", "watchFor": "Published from a personal account, with no organisation, security contact or safety evaluation" } ], "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-qwopus3-8-27b-flash-v2.json", "title": "Agents-A1-4B vs Qwopus3.8-27B-Flash-V2", "url": 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"https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwopus3-8-27b-flash-v2.json", "title": "Kimi K3 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwopus3-8-27b-flash-v2.json", "title": "Kolibri-1 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwopus3-8-27b-flash-v2.json", "title": "Laguna M.1 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwopus3-8-27b-flash-v2.json", "title": "Laguna S 2.1 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwopus3-8-27b-flash-v2.json", "title": "Laguna XS 2.1 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwopus3-8-27b-flash-v2.json", "title": "LFM2.5-2.6B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwopus3-8-27b-flash-v2.json", "title": "Ling-3.0-tiny vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwopus3-8-27b-flash-v2.json", "title": "MiMo-V2.6 Distill Qwen 9B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwopus3-8-27b-flash-v2.json", "title": "MiniCPM-o 4.5 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwopus3-8-27b-flash-v2.json", "title": "MiniCPM5-2B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwopus3-8-27b-flash-v2" }, { "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-m2-7-vs-qwopus3-8-27b-flash-v2.json", "title": "MiniMax-M2.7 vs Qwopus3.8-27B-Flash-V2", "url": 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"title": "Nemotron 3 Nano 4B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwopus3-8-27b-flash-v2.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwopus3-8-27b-flash-v2.json", "title": "Nemotron 3 Super 120B A12B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwopus3-8-27b-flash-v2.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwopus3-8-27b-flash-v2.json", "title": "occamy-1.0 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwopus3-8-27b-flash-v2.json", "title": "Olmo 3 7B Think vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwopus3-8-27b-flash-v2.json", "title": "Ornith-1.5-35B-A3B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwopus3-8-27b-flash-v2.json", "title": "Ornith-1.5-397B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwopus3-8-27b-flash-v2.json", "title": "Ornith-1.5-9B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-30B-A3B-Instruct-2507 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-4B-Instruct-2507 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.5-0.8B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.5-122B-A10B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.5-2B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.5-397B-A17B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.5-4B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.5-9B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.6-27B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.6-35B-A3B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.8-27B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.8-Flash-Next vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-Coder-Next vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-VL-2B-Instruct vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-VL-32B-Instruct vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-VL-4B-Instruct vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-VL-8B-Instruct vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-smollm3-3b.json", "title": "Qwopus3.8-27B-Flash-V2 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-spark-x2-6-4b.json", "title": "Qwopus3.8-27B-Flash-V2 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwopus3.8-27B-Flash-V2 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-27b" }, { "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/qwopus3-8-27b-flash-v2-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwopus3.8-27B-Flash-V2 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 25, "deepseek-v3-2": 60, "edge": "qwopus3-8-27b-flash-v2", "key": "reach", "name": "Reach", "qwopus3-8-27b-flash-v2": 85, "weight": 25 }, { "by": 0, "deepseek-v3-2": 50, "edge": "", "key": "risk", "name": "Risk", "qwopus3-8-27b-flash-v2": 50, "weight": 20 }, { "by": 0, "deepseek-v3-2": 60, "edge": "", "key": "interface", "name": "Agent interface", "qwopus3-8-27b-flash-v2": 60, "weight": 10 }, { "by": 22, "deepseek-v3-2": 34, "edge": "qwopus3-8-27b-flash-v2", "key": "upkeep", "name": "Documentation and upkeep", "qwopus3-8-27b-flash-v2": 56, "weight": 10 } ], "summary": "Qwopus3.8-27B-Flash-V2 scores 61 (C) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 2 of 4 scored categories. 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.", "qwopus3-8-27b-flash-v2": "A Qwen3.8-27B cousin tuned to think less per turn in long agent loops, with an MTP head for speculative decoding in llama.cpp. It fits 24 GB with Jackrong's 4-bit GGUF. The risks are one person's experimental release with no safety work, training traces taken from Claude and GPT, and a tool format that differs from Qwen's." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwopus3-8-27b-flash-v2", "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwopus3-8-27b-flash-v2.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwopus3-8-27b-flash-v2.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwopus3-8-27b-flash-v2.min.md" }, "markdown": "Qwopus3.8-27B-Flash-V2 scores 61 (C) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 2 of 4 scored categories. 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- Qwopus3.8-27B-Flash-V2: grade C, 61/100, rank #46 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwopus3-8-27b-flash-v2.md · JSON https://www.anchorterminal.com/api/v1/tools/qwopus3-8-27b-flash-v2.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\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### Qwopus3.8-27B-Flash-V2 (C)\n\nGood for: Hobbyist and research agents that want a faster-finishing Qwen3.8-27B for local coding loops on a 24 GB GPU.\n\nAhead on:\n- Reach, 85 against 60\n- Documentation and upkeep, 56 against 34\n\nWatch for: Published from a personal account, with no organisation, security contact or safety evaluation\n\n\n## Score by category\n\n| Category | Weight | DeepSeek-V3.2 | Qwopus3.8-27B-Flash-V2 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 60 | 85 | Qwopus3.8-27B-Flash-V2 +25 |\n| Risk | 20% (20 this run) | 50 | 50 | even |\n| Agent interface | 10% (10 this run) | 60 | 60 | even |\n| Documentation and upkeep | 10% (10 this run) | 34 | 56 | Qwopus3.8-27B-Flash-V2 +22 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **58.6 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | DeepSeek-V3.2 | Qwopus3.8-27B-Flash-V2 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | DeepSeek | Jackrong |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | MIT | Apache-2.0 (metadata only, no LICENSE file); built on Qwen3.8-27B under Apache 2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2025-12-01 | 2026-09-21 |\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) | 2.5/5 (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**Qwopus3.8-27B-Flash-V2.** A Qwen3.8-27B cousin tuned to think less per turn in long agent loops, with an MTP head for speculative decoding in llama.cpp. It fits 24 GB with Jackrong's 4-bit GGUF. The risks are one person's experimental release with no safety work, training traces taken from Claude and GPT, and a tool format that differs from Qwen's.\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### Qwopus3.8-27B-Flash-V2\n\n1. Quickest start is `llama-server -hf Jackrong/Qwopus3.8-27B-Flash-V2-GGUF:Q4_K_M --jinja`, about 17 GB, and add mmproj-F32.gguf for images\n2. The card's tests used Q5_K_M with the bundled MTP head and `--spec-type draft-mtp` in llama.cpp\n3. The template asks for tool calls as JSON inside tool_call tags, so use a Hermes-style parser (vLLM's hermes), not Qwen3.8's qwen3_coder\n4. For coding the card recommends enable_thinking true, reasoning_effort xhigh and temperature 0.3, which you must pass yourself, since generation_config.json says 1.0\n5. reasoning_effort takes xhigh (default), medium or low, and any other value makes the template raise an error\n6. Earlier reasoning stays in the context unless preserve_thinking is false\n7. Cap max_tokens, since a report says it loops more than V1\n\n## Questions\n\n### Which is better for AI agents, DeepSeek-V3.2 or Qwopus3.8-27B-Flash-V2?\n\nQwopus3.8-27B-Flash-V2 scores 61 (C) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 2 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwopus3-8-27b-flash-v2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwopus3-8-27b-flash-v2.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"deepseek-v3-2\", \"b\": \"qwopus3-8-27b-flash-v2\"}`. 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