{ "data": { "a": { "slug": "deepseek-v4-flash-0731", "name": "DeepSeek-V4-Flash-0731", "vendor": "DeepSeek", "vendorUrl": "https://www.deepseek.com", "kind": "weights", "category": "open-weights", "summary": "DeepSeek-V4-Flash-0731 is the 31 July 2026 release of DeepSeek's V4 Flash, a mixture-of-experts text model with 284B parameters, 13B active and a 1M-token context, under MIT. DeepSeek's API retired V4 Flash on 10 September 2026.", "url": "https://www.anchorterminal.com/tools/deepseek-v4-flash-0731", "markdownUrl": "https://www.anchorterminal.com/tools/deepseek-v4-flash-0731.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/deepseek-v4-flash-0731.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/deepseek-v4-flash-0731.json", "repo": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "deepseek-ai/DeepSeek-V4-Flash-0731" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository isn't gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the MIT licence. DeepSeek's API retired V4 Flash on 10 September 2026 and routes its old names to V4.1 Flash, so no hosted DeepSeek endpoint serves these weights. Running them yourself costs the hardware, 166.9 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-V4-Flash-0731", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "reasoning", "tool-calling", "long-context", "mit", "safetensors", "fp8", "fp4", "self-hosted", "new" ], "lastRelease": "2026-07-31", "graded": true, "anchor": { "graded": true, "score": 62.4, "grade": "B", "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": 23, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 66, "interface": 75, "reach": 70, "risk": 50, "upkeep": 43 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A long-context agent cousin under MIT with documented tool calls, three reasoning effort levels and support in every major runtime but Ollama. It needs about 190 to 200 GB of memory, though six inference providers serve it. The main risks are prompts built from DeepSeek's Python, no published safety results and no answers in its repository.", "bestFor": "Agents or teams with a multi-GPU server, or a provider account, who want DeepSeek's V4 agent model under MIT with a 1M context", "strengths": [ "MIT licence with nothing added, downloadable with no account", "1M tokens of context trained in", "Runs in vLLM, SGLang, llama.cpp, Transformers and LM Studio from released versions", "Parallel tool calls in a documented DSML format, with reasoning off or at three effort levels", "Six live inference providers on Hugging Face for agents without the hardware", "Safetensors only, with a SHA-256 on every shard" ], "weaknesses": [ "The smallest 4-bit build is 156.4 GB, so it needs about 188 GB and doesn't fit a 128 GB machine", "No Jinja template, so prompts come from running DeepSeek's encoding code or a runtime's own encoder", "No safety evaluation or agent-use guidance published, and only a company-wide security address", "DeepSeek hasn't replied to any of the 61 discussions, including reports of tool-call errors", "The card lacks intended use, limitations, languages and a knowledge cut-off" ], "agentNotes": [ "vllm serve deepseek-ai/DeepSeek-V4-Flash-0731 --trust-remote-code --kv-cache-dtype fp8 --block-size 256 --data-parallel-size 4 --enable-expert-parallel --moe-backend deep_gemm_mega_moe --attention-config '{\"use_fp4_indexer_cache\": true}' --speculative-config '{\"method\":\"dspark\",\"num_speculative_tokens\":7,\"draft_sample_method\":\"greedy\"}' --tokenizer-mode deepseek_v4 --tool-call-parser deepseek_v4 --enable-auto-tool-choice --reasoning-parser deepseek_v4 (the card's 4x GB300 example plus the parser flags from vLLM's recipe)", "Build prompts with encoding_dsv4.py, deepseek-recipe's V4 encoder or vLLM's --tokenizer-mode deepseek_v4, since the repository has no Jinja template", "Tool calls come back in a DSML tool_calls block with invoke elements, string parameters carry string=\"true\" and JSON values string=\"false\"", "Set temperature 1.0 and top_p 0.95 for agent work, and allow up to 384K output tokens at reasoning effort high or max", "Without the hardware, call it through a Hugging Face inference provider (Together, DeepInfra, Novita, Baseten, Scaleway, Featherless), not DeepSeek's API, where deepseek-v4-flash now means V4.1 Flash", "LM Studio's library build is a 156.4 GB MXFP4 GGUF and LM Studio states 156 GB minimum system memory" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 62.4 } ], "editorialScores": { "capability": 66, "interface": 75, "reach": 70, "risk": 50, "upkeep": 43 }, "provenanceScore": 73, "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": "# From DeepSeek's card (one 4x GB300 node). Add vLLM's --tokenizer-mode deepseek_v4 --tool-call-parser deepseek_v4 --enable-auto-tool-choice --reasoning-parser deepseek_v4 for tool calls\nvllm serve deepseek-ai/DeepSeek-V4-Flash-0731 \\\n --trust-remote-code --kv-cache-dtype fp8 --block-size 256 \\\n --data-parallel-size 4 --enable-expert-parallel \\\n --moe-backend deep_gemm_mega_moe \\\n --attention-config '{\"use_fp4_indexer_cache\": true}' \\\n --speculative-config '{\"method\":\"dspark\",\"num_speculative_tokens\":7,\"draft_sample_method\":\"greedy\"}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/deepseek-v4-flash-0731" }, "sameCompany": [ "deepseek-api", "deepseek-search-api", "deepseek-v4-1-flash", "deepseek-v3-2" ], "weights": { "trainedBy": "DeepSeek", "params": "284B", "active": "13B", "context": 1048576, "goodFor": "Text model for agents and long documents, with thinking or chat mode, reasoning effort and tool calls in DSML tags.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "DeepSeek-V4-Flash-0731", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731", "who": "publisher", "precision": "fp8+fp4", "sizeGB": 166.9, "memoryGB": 256, "format": "safetensors", "note": "MoE experts in FP4, most other weights FP8; the only build DeepSeek publishes" } ], "run": "vllm serve deepseek-ai/DeepSeek-V4-Flash-0731 --trust-remote-code --kv-cache-dtype fp8 --block-size 256 --data-parallel-size 4 --enable-expert-parallel --moe-backend deep_gemm_mega_moe --attention-config '{\"use_fp4_indexer_cache\": true}' --speculative-config '{\"method\":\"dspark\",\"num_speculative_tokens\":7,\"draft_sample_method\":\"greedy\"}' --tokenizer-mode deepseek_v4 --tool-call-parser deepseek_v4 --enable-auto-tool-choice --reasoning-parser deepseek_v4", "runBuild": "DeepSeek-V4-Flash-0731", "checked": "2026-10-21", "minMemoryGB": 256 }, "family": { "lab": "DeepSeek", "family": "DeepSeek V4", "url": "https://www.anchorterminal.com/open-weights/#deepseek", "others": [ { "name": "DeepSeek-V4-Flash-Base", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Base", "params": "284B total, 13B activated" }, { "name": "DeepSeek-V4-Flash", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash", "params": "284B total, 13B activated" }, { "name": "DeepSeek-V4-Flash-DSpark", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-DSpark", "params": "284B total, 13B activated" }, { "name": "DeepSeek-V4-Flash-Vision-Exp", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp", "params": "not stated on card" }, { "name": "DeepSeek-V4-Pro-Base", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-Base", "params": "1.6T total, 49B activated" }, { "name": "DeepSeek-V4-Pro", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro", "params": "1.6T total, 49B activated" }, { "name": "DeepSeek-V4-Pro-DSpark", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-DSpark", "params": "1.6T total, 49B activated" }, { "name": "DeepSeek-V4-Pro-0813", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813", "params": "1.6T total, 49B activated (V4-Pro figures; the 0813 card does not restate them)" }, { "name": "DeepSeek-V4.1-Flash", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash", "params": "552B backbone, 8B activated in prefill / 16B in decode", "listing": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash" }, { "name": "DeepSeek-V4.1-Flash-1017", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017", "params": "552B backbone, 8B / 16B activated (V4.1-Flash figures; not restated)", "listing": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash" } ], "olderCount": 72, "unrestrictedCopies": 5 }, "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": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731/commits/main", "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.", "The changelog is the repository's commit history, four commits from 31 July to 1 August 2026 (the release and a card edit adding the SGLang cookbook).", "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": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/deepseek-v4-flash-0731.json" }, "answer": "DeepSeek-V4-Flash-0731 scores 62.4 (B) on agent readiness against Qwen3.5-2B's 50.5 (D), and leads in 3 of 5 scored categories. Qwen3.5-2B leads on reach and risk.", "b": { "slug": "qwen3-5-2b", "name": "Qwen3.5-2B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3.5-2B is a small model in Alibaba's Qwen3.5 family, 2B language parameters with a vision encoder, thinking off by default and a 262,144-token context, published on Hugging Face in BF16 under Apache 2.0 for prototyping, fine-tuning and research.", "url": "https://www.anchorterminal.com/tools/qwen3-5-2b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-2b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-2b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-5-2b.json", "repo": "https://huggingface.co/Qwen/Qwen3.5-2B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3.5-2B" }, { "registry": "ollama", "name": "qwen3.5:2b" } ], "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 Apache 2.0. Running it yourself costs the hardware, 4.6 GB for the BF16 files and 1.9 to 2.7 GB for Ollama's 4-bit and 8-bit builds (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/Qwen/Qwen3.5-2B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "small", "multimodal", "tool-calling", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-03-02", "graded": true, "anchor": { "graded": true, "score": 50.5, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 73, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 28, "interface": 60, "reach": 80, "risk": 55, "upkeep": 37 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A small vision-language cousin for prototypes, fine-tuning and light local steps such as tool calls, tagging or reading an image, running in about 2 GB at 4 bits in every major runtime under plain Apache 2.0. Keep thinking off, since the card warns it loops. The main risk is neglect, with no safety results or answers from Alibaba since March.", "bestFor": "Builders who want a cheap local helper or fine-tuning base for narrow tasks and image reading on a laptop, small GPU or CPU", "strengths": [ "Runs in llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers, in about 2 GB at 4 bits", "Apache 2.0 with nothing added, no account or gate to download", "Safetensors only, no remote code, files hashed in the publisher's organisation", "262,144 tokens native and several tool calls per turn", "The card is honest about its size, naming prototyping and fine-tuning as the uses and warning about thinking loops" ], "weaknesses": [ "No quantised build from Alibaba, and only one inference provider", "No safety results, agent-use guidance or reporting route for flaws", "No reply from Qwen to any discussion, and a template fix waits since April", "Tool format only in the template, structured output undocumented, and no word on where reasoning comes back", "No knowledge cut-off or training data sources on the card" ], "agentNotes": [ "Smallest start is `ollama run qwen3.5:2b-q4_K_M` (1.9 GB); plain `qwen3.5:2b` pulls the 2.7 GB Q8_0 build", "Full precision `vllm serve Qwen/Qwen3.5-2B --port 8000 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder`", "Thinking is off by default; if you turn it on with `\"chat_template_kwargs\": {\"enable_thinking\": true}`, stream and cut off runaway output, since the card warns of thinking loops, and add `--reasoning-parser qwen3` in vLLM or SGLang to split the reasoning out", "There's no generation_config.json, so pass the card's sampling yourself, for example `temperature=1.0, top_p=1.0, top_k=20, presence_penalty=2.0` for text without thinking", "Use it for narrow steps or as a fine-tuning base, not as the main agent, as the card itself suggests", "Validate any JSON it returns, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "D", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 50.5 } ], "editorialScores": { "capability": 28, "interface": 60, "reach": 80, "risk": 55, "upkeep": 37 }, "provenanceScore": 49, "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": "ollama run qwen3.5:2b\n# or, with the card's vLLM command:\nvllm serve Qwen/Qwen3.5-2B --port 8000 --max-model-len 262144 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"qwen3.5:2b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen3-5-2b" }, "sameCompany": [ "qwen3-embedding-0-6b", "qwen3-embedding-4b", "qwen3-embedding-8b", "qwen3-vl-embedding-2b", "qwen3-vl-embedding-8b", "qwen3-asr-0-6b", "qwen3-asr-1-7b", "qwen3-tts-1-7b-customvoice", "qwen3-30b-a3b-instruct-2507", "qwen3-4b-instruct-2507", "qwen3-coder-30b-a3b-instruct", "qwen3-coder-next", "qwen3-omni-30b-a3b-instruct", "qwen3-vl-2b-instruct", "qwen3-vl-30b-a3b-instruct", "qwen3-vl-32b-instruct", "qwen3-vl-4b-instruct", "qwen3-vl-8b-instruct", "qwen3-5-0-8b", "qwen3-5-122b-a10b", "qwen3-5-397b-a17b", "qwen3-5-4b", "qwen3-5-9b", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "builtOn": "Qwen/Qwen3.5-2B-Base", "params": "2B", "context": 262144, "goodFor": "Small vision-language model for prototyping, fine-tuning and light local agent steps: text, images and video in, tool calling, thinking off by default.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "Qwen3.5-2B", "url": "https://huggingface.co/Qwen/Qwen3.5-2B", "who": "publisher", "precision": "bf16", "sizeGB": 4.6, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] }, { "name": "ollama qwen3.5:2b-q4_K_M", "url": "https://ollama.com/library/qwen3.5:2b-q4_K_M", "who": "ollama", "precision": "q4_k_m", "sizeGB": 1.9, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M language model (1.3 GB) with a BF16 vision projector (671 MB), digest 1e2d21a4f03a. The default qwen3.5:2b is Q8_0 at 2.7 GB (3.1 GB for the MLX variant). Size as Ollama's pages show it" }, { "name": "LM Studio qwen/qwen3.5-2b (Q4_K_M GGUF)", "url": "https://huggingface.co/lmstudio-community/Qwen3.5-2B-GGUF", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 1.9, "memoryGB": 8, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "Qwen3.5-2B-Q4_K_M.gguf (1.27 GB) plus mmproj-Qwen3.5-2B-BF16.gguf (0.67 GB), summed from the Hub's file list. LM Studio's catalogue entry also lists MLX 4-bit and 8-bit builds" } ], "run": "vllm serve Qwen/Qwen3.5-2B --port 8000 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "Qwen3.5-2B", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "Alibaba (Qwen)", "family": "Qwen3", "url": "https://www.anchorterminal.com/open-weights/#qwen", "others": [ { "name": "Qwen3-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-0.6B", "params": "0.6B" }, { "name": "Qwen3-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-1.7B", "params": "1.7B" }, { "name": "Qwen3-14B", "url": "https://huggingface.co/Qwen/Qwen3-14B", "params": "14B" }, { "name": "Qwen3-235B-A22B", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B", "params": "30B-A3B" }, { "name": "Qwen3-32B", "url": "https://huggingface.co/Qwen/Qwen3-32B", "params": "32B" }, { "name": "Qwen3-4B", "url": "https://huggingface.co/Qwen/Qwen3-4B", "params": "4B" }, { "name": "Qwen3-8B", "url": "https://huggingface.co/Qwen/Qwen3-8B", "params": "8B" }, { "name": "Qwen3-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-14B-Base", "url": "https://huggingface.co/Qwen/Qwen3-14B-Base", "params": "14B" }, { "name": "Qwen3-30B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Base", "params": "30B-A3B" }, { "name": "Qwen3-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3-4B-Base", "params": "4B" }, { "name": "Qwen3-8B-Base", "url": "https://huggingface.co/Qwen/Qwen3-8B-Base", "params": "8B" }, { "name": "Qwen3-Reranker-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-0.6B", "params": "0.6B" }, { "name": "Qwen3-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-8B", "params": "8B" }, { "name": "Qwen3-Embedding-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-0-6b" }, { "name": "Qwen3-Embedding-4B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-4b" }, { "name": "Qwen3-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-8b" }, { "name": "Qwen3-Reranker-4B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-4B", "params": "4B" }, { "name": "Qwen3-235B-A22B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507", "params": "235B-A22B" }, { "name": "Qwen3-Coder-480B-A35B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct", "params": "480B-A35B" }, { "name": "Qwen3-235B-A22B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-30b-a3b-instruct-2507" }, { "name": "Qwen3-30B-A3B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507", "params": "30B-A3B" }, { "name": "Qwen3-Coder-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct" }, { "name": "Qwen3-4B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507" }, { "name": "Qwen3-4B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507", "params": "4B" }, { "name": "Qwen3-Next-80B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct", "params": "80B-A3B" }, { "name": "Qwen3-Next-80B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking", "params": "80B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Captioner", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Captioner", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-omni-30b-a3b-instruct" }, { "name": "Qwen3-VL-235B-A22B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct", "params": "235B-A22B" }, { "name": "Qwen3-VL-235B-A22B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Thinking", "params": "235B-A22B" }, { "name": "Qwen3Guard-Gen-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Gen-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-4B", "params": "4B" }, { "name": "Qwen3Guard-Gen-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-8B", "params": "8B" }, { "name": "Qwen3Guard-Stream-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Stream-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-4B", "params": "4B" }, { "name": "Qwen3Guard-Stream-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-8B", "params": "8B" }, { "name": "Qwen3-4B-SafeRL", "url": "https://huggingface.co/Qwen/Qwen3-4B-SafeRL", "params": "4B" }, { "name": "Qwen3-VL-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct" }, { "name": "Qwen3-VL-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-VL-4B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct" }, { "name": "Qwen3-VL-4B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Thinking", "params": "4B" }, { "name": "Qwen3-VL-8B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct" }, { "name": "Qwen3-VL-8B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking", "params": "8B" }, { "name": "Qwen3-VL-2B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-2b-instruct" }, { "name": "Qwen3-VL-2B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Thinking", "params": "2B" }, { "name": "Qwen3-VL-32B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct", "params": "32B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct" }, { "name": "Qwen3-VL-32B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Thinking", "params": "32B" }, { "name": "Qwen3-VL-Embedding-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-2b" }, { "name": "Qwen3-VL-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-8b" }, { "name": "Qwen3-VL-Reranker-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-2B", "params": "2B" }, { "name": "Qwen3-VL-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-8B", "params": "8B" }, { "name": "Qwen3-TTS-12Hz-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-0.6B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-TTS-12Hz-1.7B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-tts-1-7b-customvoice" }, { "name": "Qwen3-TTS-12Hz-1.7B-VoiceDesign", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign", "params": "1.7B" }, { "name": "Qwen3-TTS-Tokenizer-12Hz", "url": "https://huggingface.co/Qwen/Qwen3-TTS-Tokenizer-12Hz", "params": "not in name" }, { "name": "Qwen3-ASR-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-0-6b" }, { "name": "Qwen3-ASR-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-1.7B", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-1-7b" }, { "name": "Qwen3-ForcedAligner-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ForcedAligner-0.6B", "params": "0.6B" }, { "name": "Qwen3-Coder-Next", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next", "params": "not in name; Hub safetensors total 79.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-next" }, { "name": "Qwen3-Coder-Next-Base", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next-Base", "params": "not in name; Hub safetensors total 79.7B" }, { "name": "WebWorld-14B", "url": "https://huggingface.co/Qwen/WebWorld-14B", "params": "14B" }, { "name": "WebWorld-32B", "url": "https://huggingface.co/Qwen/WebWorld-32B", "params": "32B" }, { "name": "WebWorld-8B", "url": "https://huggingface.co/Qwen/WebWorld-8B", "params": "8B" }, { "name": "Qwen3.5-397B-A17B", "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B", "params": "397B-A17B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b" }, { "name": "Qwen3.5-122B-A10B", "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B", "params": "122B-A10B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b" }, { "name": "Qwen3.5-27B", "url": "https://huggingface.co/Qwen/Qwen3.5-27B", "params": "27B" }, { "name": "Qwen3.5-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.5-35B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B-Base", "params": "35B-A3B" }, { "name": "Qwen3.5-9B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-9B-Base", "params": "9B" }, { "name": "Qwen3.5-4B", "url": "https://huggingface.co/Qwen/Qwen3.5-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-4b" }, { "name": "Qwen3.5-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-4B-Base", "params": "4B" }, { "name": "Qwen3.5-9B", "url": "https://huggingface.co/Qwen/Qwen3.5-9B", "params": "9B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-9b" }, { "name": "Qwen3.5-0.8B", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B", "params": "0.8B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-0-8b" }, { "name": "Qwen3.5-0.8B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B-Base", "params": "0.8B" }, { "name": "Qwen3.5-2B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-2B-Base", "params": "2B" }, { "name": "Qwen3.6-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B", "params": "35B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b" }, { "name": "Qwen3.6-27B", "url": "https://huggingface.co/Qwen/Qwen3.6-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-27b" }, { "name": "Qwen-AgentWorld-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.8-27B", "url": "https://huggingface.co/Qwen/Qwen3.8-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-27b" }, { "name": "Qwen3.8-2.4T-A95B", "url": "https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B", "params": "2.4T-A95B" }, { "name": "Qwen3.8-Flash-Next", "url": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next", "params": "125B total, 6B active, plus a 51B n-gram embedding table (publisher card); Hub safetensors total 180.0B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-flash-next" }, { "name": "Qwen-Drive-1.0-4B", "url": "https://huggingface.co/Qwen/Qwen-Drive-1.0-4B", "params": "4B" } ], "olderCount": 109, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Alibaba Cloud (named in the licence's copyright line)", "domain": "qwen.ai", "domainRegistered": "2023-08-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The LICENSE file reads Copyright Alibaba Cloud. The domain's registration date is carried from the 20 October 2026 RDAP check for qwen3-8-27b.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog for the files. The commit history's 16 messages are uploads, README edits and one template and tokenizer config update on 1 March 2026. The News list in github.com/QwenLM/Qwen3.5 dates the release to 2 March 2026.", "qwen.ai answers every path with its web app, so there is no security.txt, and QwenLM/Qwen3.5 has no SECURITY.md." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-5-2b.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "DeepSeek", "b": "Alibaba (Qwen team)", "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", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-07-31", "b": "2026-03-02", "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": "2.5/5 (4)", "b": "3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "DeepSeek-V4-Flash-0731 scores 62.4 (B) on agent readiness against Qwen3.5-2B's 50.5 (D), and leads in 3 of 5 scored categories. 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"https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-2b-instruct.json", "title": "Qwen3.5-2B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3.5-2B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3.5-2B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.5-2B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3.5-2B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-8b-instruct" }, { "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/qwen3-5-2b-vs-smollm3-3b.json", "title": "Qwen3.5-2B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-spark-x2-6-4b.json", "title": "Qwen3.5-2B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3.5-2B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.5-2B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.5-2B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "by": 38, "deepseek-v4-flash-0731": 66, "edge": "deepseek-v4-flash-0731", "key": "capability", "name": "Capability", "qwen3-5-2b": 28, "weight": 35 }, { "by": 10, "deepseek-v4-flash-0731": 70, "edge": "qwen3-5-2b", "key": "reach", "name": "Reach", "qwen3-5-2b": 80, "weight": 25 }, { "by": 5, "deepseek-v4-flash-0731": 50, "edge": "qwen3-5-2b", "key": "risk", "name": "Risk", "qwen3-5-2b": 55, "weight": 20 }, { "by": 15, "deepseek-v4-flash-0731": 75, "edge": "deepseek-v4-flash-0731", "key": "interface", "name": "Agent interface", "qwen3-5-2b": 60, "weight": 10 }, { "by": 6, "deepseek-v4-flash-0731": 43, "edge": "deepseek-v4-flash-0731", "key": "upkeep", "name": "Documentation and upkeep", "qwen3-5-2b": 37, "weight": 10 } ], "summary": "DeepSeek-V4-Flash-0731 scores 62.4 (B) on agent readiness against Qwen3.5-2B's 50.5 (D), and leads in 3 of 5 scored categories. Qwen3.5-2B leads on reach and risk. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "deepseek-v4-flash-0731": "A long-context agent cousin under MIT with documented tool calls, three reasoning effort levels and support in every major runtime but Ollama. It needs about 190 to 200 GB of memory, though six inference providers serve it. The main risks are prompts built from DeepSeek's Python, no published safety results and no answers in its repository.", "qwen3-5-2b": "A small vision-language cousin for prototypes, fine-tuning and light local steps such as tool calls, tagging or reading an image, running in about 2 GB at 4 bits in every major runtime under plain Apache 2.0. Keep thinking off, since the card warns it loops. The main risk is neglect, with no safety results or answers from Alibaba since March." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-2b", "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-2b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-2b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-2b.min.md" }, "markdown": "DeepSeek-V4-Flash-0731 scores 62.4 (B) on agent readiness against Qwen3.5-2B's 50.5 (D), and leads in 3 of 5 scored categories. Qwen3.5-2B leads on reach and risk. Both do open-weight models you run yourself.\n\n- DeepSeek-V4-Flash-0731: grade B, 62.4/100, rank #28 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/deepseek-v4-flash-0731.md · JSON https://www.anchorterminal.com/api/v1/tools/deepseek-v4-flash-0731.json\n- Qwen3.5-2B: grade D, 50.5/100, rank #92 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-2b.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-V4-Flash-0731 (B)\n\nGood for: Agents or teams with a multi-GPU server, or a provider account, who want DeepSeek's V4 agent model under MIT with a 1M context\n\nAhead on:\n- Capability, 66 against 28\n- Agent interface, 75 against 60\n- Documentation and upkeep, 43 against 37\n\nWatch for: The smallest 4-bit build is 156.4 GB, so it needs about 188 GB and doesn't fit a 128 GB machine\n\n### Qwen3.5-2B (D)\n\nGood for: Builders who want a cheap local helper or fine-tuning base for narrow tasks and image reading on a laptop, small GPU or CPU\n\nAhead on:\n- Reach, 80 against 70\n- Risk, 55 against 50\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.0 against 2.5 out of 5\n\nWatch for: No quantised build from Alibaba, and only one inference provider\n\n\n## Score by category\n\n| Category | Weight | DeepSeek-V4-Flash-0731 | Qwen3.5-2B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 66 | 28 | DeepSeek-V4-Flash-0731 +38 |\n| Reach | 25% (25 this run) | 70 | 80 | Qwen3.5-2B +10 |\n| Risk | 20% (20 this run) | 50 | 55 | Qwen3.5-2B +5 |\n| Agent interface | 10% (10 this run) | 75 | 60 | DeepSeek-V4-Flash-0731 +15 |\n| Documentation and upkeep | 10% (10 this run) | 43 | 37 | DeepSeek-V4-Flash-0731 +6 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **62.4 · B** | **50.5 · D** | |\n\n## Facts side by side\n\n| Fact | DeepSeek-V4-Flash-0731 | Qwen3.5-2B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | DeepSeek | Alibaba (Qwen team) |\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 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-31 | 2026-03-02 |\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 | 2.5/5 (4) | 3/5 (4) |\n\n## Verdicts\n\n**DeepSeek-V4-Flash-0731.** A long-context agent cousin under MIT with documented tool calls, three reasoning effort levels and support in every major runtime but Ollama. It needs about 190 to 200 GB of memory, though six inference providers serve it. The main risks are prompts built from DeepSeek's Python, no published safety results and no answers in its repository.\n\n**Qwen3.5-2B.** A small vision-language cousin for prototypes, fine-tuning and light local steps such as tool calls, tagging or reading an image, running in about 2 GB at 4 bits in every major runtime under plain Apache 2.0. Keep thinking off, since the card warns it loops. The main risk is neglect, with no safety results or answers from Alibaba since March.\n\n## Before you call either\n\n### DeepSeek-V4-Flash-0731\n\n1. vllm serve deepseek-ai/DeepSeek-V4-Flash-0731 --trust-remote-code --kv-cache-dtype fp8 --block-size 256 --data-parallel-size 4 --enable-expert-parallel --moe-backend deep_gemm_mega_moe --attention-config '{\"use_fp4_indexer_cache\": true}' --speculative-config '{\"method\":\"dspark\",\"num_speculative_tokens\":7,\"draft_sample_method\":\"greedy\"}' --tokenizer-mode deepseek_v4 --tool-call-parser deepseek_v4 --enable-auto-tool-choice --reasoning-parser deepseek_v4 (the card's 4x GB300 example plus the parser flags from vLLM's recipe)\n2. Build prompts with encoding_dsv4.py, deepseek-recipe's V4 encoder or vLLM's --tokenizer-mode deepseek_v4, since the repository has no Jinja template\n3. Tool calls come back in a DSML tool_calls block with invoke elements, string parameters carry string=\"true\" and JSON values string=\"false\"\n4. Set temperature 1.0 and top_p 0.95 for agent work, and allow up to 384K output tokens at reasoning effort high or max\n5. Without the hardware, call it through a Hugging Face inference provider (Together, DeepInfra, Novita, Baseten, Scaleway, Featherless), not DeepSeek's API, where deepseek-v4-flash now means V4.1 Flash\n6. LM Studio's library build is a 156.4 GB MXFP4 GGUF and LM Studio states 156 GB minimum system memory\n\n### Qwen3.5-2B\n\n1. Smallest start is `ollama run qwen3.5:2b-q4_K_M` (1.9 GB); plain `qwen3.5:2b` pulls the 2.7 GB Q8_0 build\n2. Full precision `vllm serve Qwen/Qwen3.5-2B --port 8000 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder`\n3. Thinking is off by default; if you turn it on with `\"chat_template_kwargs\": {\"enable_thinking\": true}`, stream and cut off runaway output, since the card warns of thinking loops, and add `--reasoning-parser qwen3` in vLLM or SGLang to split the reasoning out\n4. There's no generation_config.json, so pass the card's sampling yourself, for example `temperature=1.0, top_p=1.0, top_k=20, presence_penalty=2.0` for text without thinking\n5. Use it for narrow steps or as a fine-tuning base, not as the main agent, as the card itself suggests\n6. Validate any JSON it returns, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, DeepSeek-V4-Flash-0731 or Qwen3.5-2B?\n\nDeepSeek-V4-Flash-0731 scores 62.4 (B) on agent readiness against Qwen3.5-2B's 50.5 (D), and leads in 3 of 5 scored categories. Qwen3.5-2B leads on reach and risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-2b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"deepseek-v4-flash-0731\", \"b\": \"qwen3-5-2b\"}`. 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Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Qwen3.5-2B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-5-2b-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-V4-Flash-0731 vs Qwen3.5-2B", "url": "" } ], "description": "DeepSeek-V4-Flash-0731 scores 62.4 (B) to Qwen3.5-2B's 50.5 (D) for open-weight models you run yourself. 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