{ "data": { "a": { "slug": "darwin-36b-opus", "name": "Darwin-36B-Opus", "vendor": "FINAL-Bench (VIDRAFT)", "vendorUrl": "https://vidraft.net", "kind": "weights", "category": "open-weights", "summary": "Darwin-36B-Opus is VIDRAFT's open-weight text-only mixture-of-experts reasoning model, about 35B parameters with about 3B active per token, made by merging Qwen3.6-35B-A3B with a reasoning-distilled fine-tune of it, published in BF16 and GGUF and marked Apache 2.0.", "url": "https://www.anchorterminal.com/tools/darwin-36b-opus", "markdownUrl": "https://www.anchorterminal.com/tools/darwin-36b-opus.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/darwin-36b-opus.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/darwin-36b-opus.json", "repo": "https://huggingface.co/FINAL-Bench/Darwin-36B-Opus", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "FINAL-Bench/Darwin-36B-Opus" }, { "registry": "huggingface", "name": "FINAL-Bench/POCKET-35B-GGUF" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither the main repository nor the POCKET-35B-GGUF build is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the Apache 2.0 licence named in the card's metadata. The repository has no LICENSE file. Running it yourself costs the hardware, about 69 GB for the BF16 files or 21.2 GB for the publisher's Q4_K_M 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/FINAL-Bench/Darwin-36B-Opus", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "reasoning", "merge", "derivative", "apache-2.0", "safetensors", "gguf", "self-hosted" ], "lastRelease": "2026-04-23", "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": 30, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 70, "reach": 80, "risk": 55, "upkeep": 51 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A text-only reasoning cousin merged from Qwen3.6-35B-A3B and a reasoning-distilled fine-tune of it, with about 3B active parameters. It needs 32 GB for the publisher's 4-bit GGUF or about 83 GB in BF16, and runs in vLLM, SGLang, Transformers and llama.cpp. The main risks are an undisclosed merge method, a card that disagrees with its files, and no safety results.", "bestFor": "Reasoning-heavy text tasks on a 32 GB machine or a single 80 GB GPU, where a disclosed training method isn't required", "strengths": [ "Runs in released vLLM, SGLang, Transformers and llama.cpp as the text-only Qwen3.5 MoE architecture", "The publisher's Q4_K_M GGUF fits in 32 GB, with about 3B active parameters per token", "Apache 2.0 through the whole licence chain, with no gate", "Safetensors and GGUF only, with a SHA-256 on every weight file in the publisher's own organisation", "The publisher answered 8 of the 13 discussions" ], "weaknesses": [ "The merge method is proprietary and the current weights include an undescribed targeted refinement", "No safety results, agent guidance or security contact", "The card's head counts, base and evaluation method disagree with the files", "Tool calling and structured output aren't documented, and the card names no parser", "No LICENSE file, and the files were replaced in place on the first day with no note on the card" ], "agentNotes": [ "Quickest start is `llama-server -hf FINAL-Bench/POCKET-35B-GGUF:Q4_K_M --jinja`, about 21 GB of files", "On the reference machine BF16 fits, `vllm serve FINAL-Bench/Darwin-36B-Opus --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, the parsers for Qwen3.6's format", "Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention", "The card suggests temperature 0.6 to 0.7 and at least 5,120 new tokens for its think traces", "generation_config.json stops only on the end-of-text token, so add `\u003c|im_end|\u003e` as a stop token if a runtime keeps generating after the turn ends", "Text only, the vision encoder of Qwen3.6-35B-A3B isn't in these files", "Validate JSON output yourself, since structured output isn't documented" ], "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": 70, "reach": 80, "risk": 55, "upkeep": 51 }, "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 FINAL-Bench/POCKET-35B-GGUF:Q4_K_M --jinja -c 32768\n# or the BF16 weights with vLLM:\nvllm serve FINAL-Bench/Darwin-36B-Opus --max-model-len 262144 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder", "http": "curl http://localhost:8080/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/darwin-36b-opus" }, "weights": { "trainedBy": "FINAL-Bench (VIDRAFT)", "builtOn": "A merge of Qwen/Qwen3.6-35B-A3B (Apache 2.0) as the base and hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled (Apache 2.0, a LoRA fine-tune of the same base on reasoning traces mostly from Claude Opus 4.6), per the card's text. The metadata declared both as base_model until 3 June 2026 and declares none now. Text-only, the base without its vision encoder", "params": "35B (3B active)", "active": "3B", "context": 262144, "goodFor": "Text-only mixture-of-experts reasoning model merged from Qwen3.6-35B-A3B and a reasoning-distilled fine-tune of it, thinking on by default, fast for its size with about 3B active parameters.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "POCKET-35B-IQ1_M", "url": "https://huggingface.co/FINAL-Bench/POCKET-35B-GGUF/blob/main/POCKET-35B-IQ1_M.gguf", "who": "publisher", "precision": "iq1_m", "sizeGB": 8.2, "memoryGB": 12, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Below 4-bit, so it doesn't count for the memory line" }, { "name": "POCKET-35B-Q2_K", "url": "https://huggingface.co/FINAL-Bench/POCKET-35B-GGUF/blob/main/POCKET-35B-Q2_K.gguf", "who": "publisher", "precision": "q2_k", "sizeGB": 12.9, "memoryGB": 16, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Below 4-bit, so it doesn't count for the memory line" }, { "name": "POCKET-35B-Q3_K_M", "url": "https://huggingface.co/FINAL-Bench/POCKET-35B-GGUF/blob/main/POCKET-35B-Q3_K_M.gguf", "who": "publisher", "precision": "q3_k_m", "sizeGB": 16.8, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Below 4-bit, so it doesn't count for the memory line" }, { "name": "POCKET-35B-Q4_K_M", "url": "https://huggingface.co/FINAL-Bench/POCKET-35B-GGUF/blob/main/POCKET-35B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 21.2, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "Darwin-36B-Opus", "url": "https://huggingface.co/FINAL-Bench/Darwin-36B-Opus", "who": "publisher", "precision": "bf16", "sizeGB": 69.4, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ] } ], "run": "vllm serve FINAL-Bench/Darwin-36B-Opus --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "Darwin-36B-Opus", "checked": "2026-10-21", "minMemoryGB": 12 }, "family": { "lab": "FINAL-Bench (VIDRAFT)", "family": "Darwin", "url": "https://www.anchorterminal.com/open-weights/#final-bench", "others": [ { "name": "Darwin-35B-A3B-Opus", "url": "https://huggingface.co/FINAL-Bench/Darwin-35B-A3B-Opus", "params": "35B total, 3B active (MoE; 35,951,822,704 by the Hub's safetensors count)" }, { "name": "Darwin-9B-Opus", "url": "https://huggingface.co/FINAL-Bench/Darwin-9B-Opus", "params": "9B (9,653,104,368 by the Hub's safetensors count)" }, { "name": "Darwin-31B-Opus", "url": "https://huggingface.co/FINAL-Bench/Darwin-31B-Opus", "params": "31B (31,273,088,876 by the Hub's safetensors count)" }, { "name": "Darwin-4B-Opus", "url": "https://huggingface.co/FINAL-Bench/Darwin-4B-Opus", "params": "4B (7,996,156,490 by the Hub's safetensors count)" }, { "name": "Darwin-2B-webgpu", "url": "https://huggingface.co/FINAL-Bench/Darwin-2B-webgpu", "params": "2B (Gemma 4 E2B)" }, { "name": "Darwin-4B-David", "url": "https://huggingface.co/FINAL-Bench/Darwin-4B-David", "params": "4B (7,996,156,490 by the Hub's safetensors count)" }, { "name": "Darwin-4B-Genesis", "url": "https://huggingface.co/FINAL-Bench/Darwin-4B-Genesis", "params": "4B (7,518,069,034 by the Hub's safetensors count)" }, { "name": "Darwin-27B-KR", "url": "https://huggingface.co/FINAL-Bench/Darwin-27B-KR", "params": "27B (27,781,427,952 by the Hub's safetensors count)" }, { "name": "Darwin-27B-Opus", "url": "https://huggingface.co/FINAL-Bench/Darwin-27B-Opus", "params": "27B (27,781,427,952 by the Hub's safetensors count)" }, { "name": "Darwin-27B-KR-V2", "url": "https://huggingface.co/FINAL-Bench/Darwin-27B-KR-V2", "params": "27B (26,895,998,464 by the Hub's safetensors count)" }, { "name": "Darwin-27B-KR-V3", "url": "https://huggingface.co/FINAL-Bench/Darwin-27B-KR-V3", "params": "27B (26,895,998,464 by the Hub's safetensors count)" }, { "name": "Darwin-TTS-1.7B-Cross", "url": "https://huggingface.co/FINAL-Bench/Darwin-TTS-1.7B-Cross", "params": "1.7B (2,099,234,881 by the Hub's safetensors count)" }, { "name": "Darwin-2B-Opus", "url": "https://huggingface.co/FINAL-Bench/Darwin-2B-Opus", "params": "2B (1,881,825,088 by the Hub's safetensors count)" }, { "name": "Darwin-28B-NEG", "url": "https://huggingface.co/FINAL-Bench/Darwin-28B-NEG", "params": "28B (26,895,998,464 by the Hub's safetensors count)" }, { "name": "Darwin-28B-Opus", "url": "https://huggingface.co/FINAL-Bench/Darwin-28B-Opus", "params": "28B (27B-class; 27,781,427,952 by the Hub's safetensors count)" }, { "name": "Darwin-9B-NEG", "url": "https://huggingface.co/FINAL-Bench/Darwin-9B-NEG", "params": "9B (9,653,104,368 by the Hub's safetensors count)" }, { "name": "Darwin-28B-KR", "url": "https://huggingface.co/FINAL-Bench/Darwin-28B-KR", "params": "28B (26,895,998,464 by the Hub's safetensors count)" }, { "name": "Darwin-28B-KR-Legal", "url": "https://huggingface.co/FINAL-Bench/Darwin-28B-KR-Legal", "params": "28B (26,895,998,464 by the Hub's safetensors count)" }, { "name": "Darwin-9B-NEG-x-Negentropy-V8", "url": "https://huggingface.co/FINAL-Bench/Darwin-9B-NEG-x-Negentropy-V8", "params": "9B (8,953,803,264 by the Hub's safetensors count)" }, { "name": "Darwin-Mistral-G1", "url": "https://huggingface.co/FINAL-Bench/Darwin-Mistral-G1", "params": "123B-class (Ministral3ForCausalLM; 125,025,988,608 by the Hub's safetensors count)" }, { "name": "Darwin-28B-REASON", "url": "https://huggingface.co/FINAL-Bench/Darwin-28B-REASON", "params": "28B (26,895,998,464 by the Hub's safetensors count)" }, { "name": "Darwin-28B-Coder", "url": "https://huggingface.co/FINAL-Bench/Darwin-28B-Coder", "params": "28B (26,895,998,464 by the Hub's safetensors count)" }, { "name": "Darwin-218B-Delphi", "url": "https://huggingface.co/FINAL-Bench/Darwin-218B-Delphi", "params": "218B (218,750,277,872 by the Hub's safetensors count)" }, { "name": "Darwin-218B-Expert-Bio", "url": "https://huggingface.co/FINAL-Bench/Darwin-218B-Expert-Bio", "params": "218B (218,750,277,872 by the Hub's safetensors count)" }, { "name": "Darwin-218B-kr", "url": "https://huggingface.co/FINAL-Bench/Darwin-218B-kr", "params": "218B (218,750,277,872 by the Hub's safetensors count)" }, { "name": "Darwin-218B-Expert", "url": "https://huggingface.co/FINAL-Bench/Darwin-218B-Expert", "params": "218B (218,750,277,872 by the Hub's safetensors count)" }, { "name": "Darwin-60B-DUO", "url": "https://huggingface.co/FINAL-Bench/Darwin-60B-DUO", "params": "60B (two ~30B models together)" }, { "name": "Darwin-397B-JGOS", "url": "https://huggingface.co/FINAL-Bench/Darwin-397B-JGOS", "params": "397B total, 17B active (MoE; 403,397,928,944 by the Hub's safetensors count)" }, { "name": "Darwin-398B-JGOS", "url": "https://huggingface.co/FINAL-Bench/Darwin-398B-JGOS", "params": "398B (MoE; 403,397,928,944 by the Hub's safetensors count)" }, { "name": "Darwin-Chimera-4B-Gen1", "url": "https://huggingface.co/FINAL-Bench/Darwin-Chimera-4B-Gen1", "params": "4B (4,022,468,096 by the Hub's safetensors count)" }, { "name": "Darwin-V9-Chimera-4B", "url": "https://huggingface.co/FINAL-Bench/Darwin-V9-Chimera-4B", "params": "4B (4,022,468,096 by the Hub's safetensors count)" }, { "name": "Darwin-V9-Chimera-4B-SFT", "url": "https://huggingface.co/FINAL-Bench/Darwin-V9-Chimera-4B-SFT", "params": "4B (4,022,468,096 by the Hub's safetensors count)" }, { "name": "Darwin-36B-KR", "url": "https://huggingface.co/FINAL-Bench/Darwin-36B-KR", "params": "36B (35B total, 3B active, MoE; 35,951,822,704 by the Hub's safetensors count)" }, { "name": "Darwin-397B-ZTC", "url": "https://huggingface.co/FINAL-Bench/Darwin-397B-ZTC", "params": "397B (MoE, FP8; 403,586,672,624 by the Hub's safetensors count)" }, { "name": "Darwin-4B-Chimera", "url": "https://huggingface.co/FINAL-Bench/Darwin-4B-Chimera", "params": "4B (4,022,468,096 by the Hub's safetensors count)" }, { "name": "Darwin-Chimera-4B-MoE-2E-PoC", "url": "https://huggingface.co/FINAL-Bench/Darwin-Chimera-4B-MoE-2E-PoC", "params": "4B (8,735,845,376 by the Hub's safetensors count)" }, { "name": "Darwin-V9-Chimera-4B-MoE3", "url": "https://huggingface.co/FINAL-Bench/Darwin-V9-Chimera-4B-MoE3", "params": "4B (9,403,579,137 by the Hub's safetensors count)" }, { "name": "Darwin-27B-RSI", "url": "https://huggingface.co/FINAL-Bench/Darwin-27B-RSI", "params": "27B (26,895,998,464 by the Hub's safetensors count)" }, { "name": "Darwin-180B-RSI", "url": "https://huggingface.co/FINAL-Bench/Darwin-180B-RSI", "params": "180B (MoE; 179,999,981,459 by the Hub's safetensors count)" }, { "name": "Darwin-180B-RSI-R3", "url": "https://huggingface.co/FINAL-Bench/Darwin-180B-RSI-R3", "params": "180B (MoE; 179,999,981,459 by the Hub's safetensors count)" }, { "name": "Darwin-27B-ZTC", "url": "https://huggingface.co/FINAL-Bench/Darwin-27B-ZTC", "params": "27B (25,624,600,064 by the Hub's safetensors count)" }, { "name": "Darwin-27B-ZTC-v2", "url": "https://huggingface.co/FINAL-Bench/Darwin-27B-ZTC-v2", "params": "27B (25,624,600,064 by the Hub's safetensors count)" }, { "name": "Darwin-27B-ZTC-v5", "url": "https://huggingface.co/FINAL-Bench/Darwin-27B-ZTC-v5", "params": "27B (25,624,600,064 by the Hub's safetensors count)" }, { "name": "Darwin-27B-ZTC-v9", "url": "https://huggingface.co/FINAL-Bench/Darwin-27B-ZTC-v9", "params": "27B (25,624,600,064 by the Hub's safetensors count)" }, { "name": "Darwin-35B-ZTC-Vision", "url": "https://huggingface.co/FINAL-Bench/Darwin-35B-ZTC-Vision", "params": "35B total, about 3B active (MoE; 34,598,622,576 by the Hub's safetensors count)" } ], "olderCount": 19, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "VIDRAFT Inc. (named on vidraft.net); the card's citation names FINAL-Bench and VIDRAFT_LAB", "domain": "vidraft.net", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "vidraft.net links to the FINAL-Bench organisation on Hugging Face and lists Darwin-36B-Opus by name. Every commit to the repository is by one account, SeaWolf-AI.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored. The licence is Apache 2.0 in the card's metadata only, with no licence file.", "No changelog. Commit titles name the weight versions, v1 to v3 on 22 and 23 April 2026, replaced in the same repository.", "vidraft.net/.well-known/security.txt returns 404, so there is no security.txt.", "unchecked: the domain's registration date." ], "score": 40 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/darwin-36b-opus.json" }, "answer": "Darwin-36B-Opus scores 61 (C) on agent readiness against DeepSeek V4.1 Flash's 59 (C), and leads in 3 of 4 scored categories. DeepSeek V4.1 Flash leads on agent interface.", "b": { "slug": "deepseek-v4-1-flash", "name": "DeepSeek V4.1 Flash", "vendor": "DeepSeek", "vendorUrl": "https://www.deepseek.com", "kind": "weights", "category": "open-weights", "summary": "DeepSeek V4.1 Flash is DeepSeek's open-weight multimodal mixture-of-experts model, 552B backbone parameters with 8B active in prefill and 16B in decode and a 1M-token context, published on Hugging Face under MIT. The 17 October update is graded.", "url": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash", "markdownUrl": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/deepseek-v4-1-flash.json", "repo": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "deepseek-ai/DeepSeek-V4.1-Flash-1017" }, { "registry": "huggingface", "name": "deepseek-ai/DeepSeek-V4.1-Flash" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-20).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the MIT licence. DeepSeek's own API serves the same weights as deepseek-flash, listed separately. Running them yourself costs the hardware, 510 GB of files per repository (checked 2026-10-20).", "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 2026-10-20).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-20" }, "docsUrl": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "mit", "safetensors", "fp8", "self-hosted", "new" ], "lastRelease": "2026-10-17", "graded": true, "anchor": { "graded": true, "score": 59, "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": 52, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 69, "interface": 75, "reach": 50, "risk": 50, "upkeep": 48 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A large multimodal agent model with a 1M-token context, documented tool calls, JSON Schema output and a reasoning budget, under MIT. It needs about 612 GB of memory across several GPUs in vLLM or SGLang. The main risks are prompts built from DeepSeek's code rather than a template, no published safety results and no answers in its repositories.", "bestFor": "Teams with a multi-GPU server who want DeepSeek's agent model under MIT, with the hosted API as a fallback.", "strengths": [ "MIT licence with no use policy, downloadable with no account", "1M tokens of context trained in, with an effort budget from 1 to 100 and a non-thinking mode", "Tool-call format documented both ways with test cases, and DeepSeek ships the parser", "JSON Schema output documented in DeepSeek's deepseek-recipe library", "Released support in vLLM 0.32.0 and SGLang 0.5.22, and DeepSeek's API serves the same update" ], "weaknesses": [ "510.3 GB of files and no smaller build anywhere, so it won't fit the 107.4 GB reference machine", "No Jinja template in the 17 October repository, prompts are built by running DeepSeek's code", "No safety evaluation or agent-use guidance, and only a company-wide security address", "None of the 40 most recent discussions had a reply from DeepSeek", "Not in llama.cpp, Transformers, LM Studio or Ollama's local library" ], "agentNotes": [ "DeepSeek's reference run is its inference folder, convert.py with --model-parallel 8 --expert-dtype fp4, then torchrun --nproc-per-node 8 generate.py --ckpt-path \"$SAVE_PATH\" --config config.json --interactive", "In vLLM 0.32.0 the parsers are named deepseek_v41 for both --tool-call-parser and --reasoning-parser. That's vLLM's naming, DeepSeek doesn't document a vLLM command", "Build prompts with encoding.py or pip install deepseek-recipe, not the September repository's chat_template.jinja, which an open pull request says trails the 16 October encoding", "Tool calls come back in a DSML calls block with a space before calls, invoke and parameter, unlike V4", "Set reasoning_effort to 1 to 100, or low, high or max (50, 75, 100), in thinking mode only. Default is 75", "Sample at temperature 1.0 and top_p 1.0 and allow 256K or more output tokens, as the card recommends", "Without the hardware, call DeepSeek's API as deepseek-flash, which serves this exact update" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 59 } ], "editorialScores": { "capability": 69, "interface": 75, "reach": 50, "risk": 50, "upkeep": 48 }, "provenanceScore": 73, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "lastMoved": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# DeepSeek's reference demo, from the repository's inference folder (8 GPUs)\npip install -r inference/requirements.txt\npython convert.py --hf-ckpt-path \"$HF_CKPT_PATH\" --save-path \"$SAVE_PATH\" \\\n --model-parallel 8 --expert-dtype fp4 --tokenizer-path \"$HF_CKPT_PATH\"\ntorchrun --nproc-per-node 8 generate.py --ckpt-path \"$SAVE_PATH\" --config config.json --interactive" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/deepseek-v4-1-flash" }, "sameCompany": [ "deepseek-search-api", "deepseek-v3-2", "deepseek-v4-flash-0731" ], "sameModel": [ "deepseek-api" ], "weights": { "trainedBy": "DeepSeek", "params": "552B", "active": "8B prefill, 16B decode", "context": 1048576, "goodFor": "Multimodal mixture-of-experts model for agents and long context, text and images in, with tool calls and a reasoning budget.", "runtimes": [ "vLLM", "SGLang" ], "builds": [ { "name": "DeepSeek-V4.1-Flash-1017", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017", "who": "publisher", "precision": "fp8+fp4", "sizeGB": 510.3, "memoryGB": 1024, "format": "safetensors", "note": "FP8 with FP4 experts (F8_E4M3 and I8 tensors); the graded 17 October update. The September repository, deepseek-ai/DeepSeek-V4.1-Flash, is the same size" } ], "run": "pip install -r inference/requirements.txt \u0026\u0026 python convert.py --hf-ckpt-path \"$HF_CKPT_PATH\" --save-path \"$SAVE_PATH\" --model-parallel 8 --expert-dtype fp4 --tokenizer-path \"$HF_CKPT_PATH\" \u0026\u0026 torchrun --nproc-per-node 8 generate.py --ckpt-path \"$SAVE_PATH\" --config config.json --interactive", "runBuild": "DeepSeek-V4.1-Flash-1017", "checked": "2026-10-21", "minMemoryGB": 1024 }, "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-0731", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731", "params": "284B total, 13B activated (V4-Flash figures; the 0731 card does not restate them)", "listing": "https://www.anchorterminal.com/tools/deepseek-v4-flash-0731" }, { "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" } ], "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-V4.1-Flash/commits/main", "securityTxt": "none", "checked": "2026-10-20", "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 September repository's commit history, whose messages say what changed (an encoding fix on 16 October, an indexer cache fix on 12 October, the chat template on 1 October).", "deepseek.com/.well-known/security.txt and www.deepseek.com/.well-known/security.txt answered 404 on 20 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-v4-1-flash.json", "live": { "slug": "deepseek-v4-1-flash", "securityTxt": { "url": "https://deepseek.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-21T15:59:01.743888833Z" }, "pages": [ { "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-10-21T00:50:03.414291506Z", "changedAt": "2026-10-21T00:50:03.414291506Z", "fingerprint": "96f7ad6c7b35" } ], "updatedAt": "2026-10-21T15:59:01.743888833Z" } }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "FINAL-Bench (VIDRAFT)", "b": "DeepSeek", "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": "Apache-2.0", "b": "MIT", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-04-23", "b": "2026-10-17", "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": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Darwin-36B-Opus scores 61 (C) on agent readiness against DeepSeek V4.1 Flash's 59 (C), and leads in 3 of 4 scored categories. DeepSeek V4.1 Flash leads on agent interface.", "question": "Which is better for AI agents, Darwin-36B-Opus or DeepSeek V4.1 Flash?" } ], "goodFor": [ { "aheadOn": [ "Reach, 80 against 50", "Risk, 55 against 50" ], "also": null, "goodFor": "Reasoning-heavy text tasks on a 32 GB machine or a single 80 GB GPU, where a disclosed training method isn't required", "slug": "darwin-36b-opus", "watchFor": "The merge method is proprietary and the current weights include an undescribed targeted refinement" }, { "aheadOn": [ "Agent interface, 75 against 70" ], "also": null, "goodFor": "Teams with a multi-GPU server who want DeepSeek's agent model under MIT, with the hosted API as a fallback.", "slug": "deepseek-v4-1-flash", "watchFor": "510.3 GB of files and no smaller build anywhere, so it won't fit the 107.4 GB reference machine" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": 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Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "DeepSeek V4.1 Flash vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-super-120b-a12b.json", "title": "DeepSeek V4.1 Flash vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-ultra-550b-a55b.json", "title": "DeepSeek V4.1 Flash vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-occamy-1-0.json", "title": "DeepSeek V4.1 Flash vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-olmo-3-7b-think.json", "title": "DeepSeek V4.1 Flash vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-35b-a3b.json", "title": "DeepSeek V4.1 Flash vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-397b.json", "title": "DeepSeek V4.1 Flash vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-9b.json", "title": "DeepSeek V4.1 Flash vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-30b-a3b-instruct-2507.json", "title": "DeepSeek V4.1 Flash vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-4b-instruct-2507.json", "title": "DeepSeek V4.1 Flash vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-0-8b.json", "title": "DeepSeek V4.1 Flash vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-122b-a10b.json", "title": "DeepSeek V4.1 Flash vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-2b.json", "title": "DeepSeek V4.1 Flash vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-397b-a17b.json", "title": "DeepSeek V4.1 Flash vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-4b.json", "title": "DeepSeek V4.1 Flash vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-9b.json", "title": "DeepSeek V4.1 Flash vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-6-27b.json", "title": "DeepSeek V4.1 Flash vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-6-35b-a3b.json", "title": "DeepSeek V4.1 Flash vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-27b.json", "title": "DeepSeek V4.1 Flash vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-flash-next.json", "title": "DeepSeek V4.1 Flash vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-coder-30b-a3b-instruct.json", "title": "DeepSeek V4.1 Flash vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-coder-next.json", "title": "DeepSeek V4.1 Flash vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-omni-30b-a3b-instruct.json", "title": "DeepSeek V4.1 Flash vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-2b-instruct.json", "title": "DeepSeek V4.1 Flash vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-30b-a3b-instruct.json", "title": "DeepSeek V4.1 Flash vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-32b-instruct.json", "title": "DeepSeek V4.1 Flash vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-4b-instruct.json", "title": "DeepSeek V4.1 Flash vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-8b-instruct.json", "title": "DeepSeek V4.1 Flash vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwopus3-8-27b-flash-v2.json", "title": "DeepSeek V4.1 Flash vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-smollm3-3b.json", "title": "DeepSeek V4.1 Flash vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-spark-x2-6-4b.json", "title": "DeepSeek V4.1 Flash vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-swift-1-5-qwen3-8-27b.json", "title": "DeepSeek V4.1 Flash vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-swift-1-5-qwen3-8-flash-next.json", "title": "DeepSeek V4.1 Flash vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-thinkingcap-qwen3-8-27b.json", "title": "DeepSeek V4.1 Flash vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 30, "darwin-36b-opus": 80, "deepseek-v4-1-flash": 50, "edge": "darwin-36b-opus", "key": "reach", "name": "Reach", "weight": 25 }, { "by": 5, "darwin-36b-opus": 55, "deepseek-v4-1-flash": 50, "edge": "darwin-36b-opus", "key": "risk", "name": "Risk", "weight": 20 }, { "by": 5, "darwin-36b-opus": 70, "deepseek-v4-1-flash": 75, "edge": "deepseek-v4-1-flash", "key": "interface", "name": "Agent interface", "weight": 10 }, { "by": 3, "darwin-36b-opus": 51, "deepseek-v4-1-flash": 48, "edge": "darwin-36b-opus", "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Darwin-36B-Opus scores 61 (C) on agent readiness against DeepSeek V4.1 Flash's 59 (C), and leads in 3 of 4 scored categories. DeepSeek V4.1 Flash leads on agent interface. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "darwin-36b-opus": "A text-only reasoning cousin merged from Qwen3.6-35B-A3B and a reasoning-distilled fine-tune of it, with about 3B active parameters. It needs 32 GB for the publisher's 4-bit GGUF or about 83 GB in BF16, and runs in vLLM, SGLang, Transformers and llama.cpp. The main risks are an undisclosed merge method, a card that disagrees with its files, and no safety results.", "deepseek-v4-1-flash": "A large multimodal agent model with a 1M-token context, documented tool calls, JSON Schema output and a reasoning budget, under MIT. It needs about 612 GB of memory across several GPUs in vLLM or SGLang. The main risks are prompts built from DeepSeek's code rather than a template, no published safety results and no answers in its repositories." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v4-1-flash", "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v4-1-flash.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v4-1-flash.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v4-1-flash.min.md" }, "markdown": "Darwin-36B-Opus scores 61 (C) on agent readiness against DeepSeek V4.1 Flash's 59 (C), and leads in 3 of 4 scored categories. DeepSeek V4.1 Flash leads on agent interface. Both do open-weight models you run yourself.\n\n- Darwin-36B-Opus: grade C, 61/100, rank #37 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/darwin-36b-opus.md · JSON https://www.anchorterminal.com/api/v1/tools/darwin-36b-opus.json\n- DeepSeek V4.1 Flash: grade C, 59/100, rank #63 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/deepseek-v4-1-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/deepseek-v4-1-flash.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### Darwin-36B-Opus (C)\n\nGood for: Reasoning-heavy text tasks on a 32 GB machine or a single 80 GB GPU, where a disclosed training method isn't required\n\nAhead on:\n- Reach, 80 against 50\n- Risk, 55 against 50\n\nWatch for: The merge method is proprietary and the current weights include an undescribed targeted refinement\n\n### DeepSeek V4.1 Flash (C)\n\nGood for: Teams with a multi-GPU server who want DeepSeek's agent model under MIT, with the hosted API as a fallback.\n\nAhead on:\n- Agent interface, 75 against 70\n\nWatch for: 510.3 GB of files and no smaller build anywhere, so it won't fit the 107.4 GB reference machine\n\n\n## Score by category\n\n| Category | Weight | Darwin-36B-Opus | DeepSeek V4.1 Flash | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 80 | 50 | Darwin-36B-Opus +30 |\n| Risk | 20% (20 this run) | 55 | 50 | Darwin-36B-Opus +5 |\n| Agent interface | 10% (10 this run) | 70 | 75 | DeepSeek V4.1 Flash +5 |\n| Documentation and upkeep | 10% (10 this run) | 51 | 48 | Darwin-36B-Opus +3 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **59 · C** | |\n\n## Facts side by side\n\n| Fact | Darwin-36B-Opus | DeepSeek V4.1 Flash |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | FINAL-Bench (VIDRAFT) | DeepSeek |\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 | Apache-2.0 | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-04-23 | 2026-10-17 |\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) | 2.8/5 (4) |\n\n## Verdicts\n\n**Darwin-36B-Opus.** A text-only reasoning cousin merged from Qwen3.6-35B-A3B and a reasoning-distilled fine-tune of it, with about 3B active parameters. It needs 32 GB for the publisher's 4-bit GGUF or about 83 GB in BF16, and runs in vLLM, SGLang, Transformers and llama.cpp. The main risks are an undisclosed merge method, a card that disagrees with its files, and no safety results.\n\n**DeepSeek V4.1 Flash.** A large multimodal agent model with a 1M-token context, documented tool calls, JSON Schema output and a reasoning budget, under MIT. It needs about 612 GB of memory across several GPUs in vLLM or SGLang. The main risks are prompts built from DeepSeek's code rather than a template, no published safety results and no answers in its repositories.\n\n## Before you call either\n\n### Darwin-36B-Opus\n\n1. Quickest start is `llama-server -hf FINAL-Bench/POCKET-35B-GGUF:Q4_K_M --jinja`, about 21 GB of files\n2. On the reference machine BF16 fits, `vllm serve FINAL-Bench/Darwin-36B-Opus --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, the parsers for Qwen3.6's format\n3. Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n4. The card suggests temperature 0.6 to 0.7 and at least 5,120 new tokens for its think traces\n5. generation_config.json stops only on the end-of-text token, so add `\u003c|im_end|\u003e` as a stop token if a runtime keeps generating after the turn ends\n6. Text only, the vision encoder of Qwen3.6-35B-A3B isn't in these files\n7. Validate JSON output yourself, since structured output isn't documented\n\n### DeepSeek V4.1 Flash\n\n1. DeepSeek's reference run is its inference folder, convert.py with --model-parallel 8 --expert-dtype fp4, then torchrun --nproc-per-node 8 generate.py --ckpt-path \"$SAVE_PATH\" --config config.json --interactive\n2. In vLLM 0.32.0 the parsers are named deepseek_v41 for both --tool-call-parser and --reasoning-parser. That's vLLM's naming, DeepSeek doesn't document a vLLM command\n3. Build prompts with encoding.py or pip install deepseek-recipe, not the September repository's chat_template.jinja, which an open pull request says trails the 16 October encoding\n4. Tool calls come back in a DSML calls block with a space before calls, invoke and parameter, unlike V4\n5. Set reasoning_effort to 1 to 100, or low, high or max (50, 75, 100), in thinking mode only. Default is 75\n6. Sample at temperature 1.0 and top_p 1.0 and allow 256K or more output tokens, as the card recommends\n7. Without the hardware, call DeepSeek's API as deepseek-flash, which serves this exact update\n\n## Questions\n\n### Which is better for AI agents, Darwin-36B-Opus or DeepSeek V4.1 Flash?\n\nDarwin-36B-Opus scores 61 (C) on agent readiness against DeepSeek V4.1 Flash's 59 (C), and leads in 3 of 4 scored categories. DeepSeek V4.1 Flash leads on agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v4-1-flash.json, and with the fewest tokens: https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v4-1-flash.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"darwin-36b-opus\", \"b\": \"deepseek-v4-1-flash\"}`. From a terminal: `anchor compare darwin-36b-opus deepseek-v4-1-flash`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/darwin-36b-opus.json and https://www.anchorterminal.com/api/v1/tools/deepseek-v4-1-flash.json\n\n## Other comparisons with Darwin-36B-Opus or DeepSeek V4.1 Flash\n\n- [Agents-A1-4B vs Darwin-36B-Opus](https://www.anchorterminal.com/compare/agents-a1-4b-vs-darwin-36b-opus.md)\n- [Agents-A1-4B vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-1-flash.md)\n- [Apertus v1.5 8B vs Darwin-36B-Opus](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-darwin-36b-opus.md)\n- [Apertus v1.5 8B vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-1-flash.md)\n- [Apodex-1.1-mini vs Darwin-36B-Opus](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-darwin-36b-opus.md)\n- [Apodex-1.1-mini vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v4-1-flash.md)\n- [BigBang-v1 vs Darwin-36B-Opus](https://www.anchorterminal.com/compare/bigbang-v1-vs-darwin-36b-opus.md)\n- [BigBang-v1 vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v4-1-flash.md)\n- [Cosmos-Reason2-2B vs Darwin-36B-Opus](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-darwin-36b-opus.md)\n- [Cosmos-Reason2-2B vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-deepseek-v4-1-flash.md)\n- [Darwin-36B-Opus vs DeepSeek-V3.2](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v3-2.md)\n- [Darwin-36B-Opus vs DeepSeek-V4-Flash-0731](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v4-flash-0731.md)\n- [Darwin-36B-Opus vs DiffusionGemma 26B A4B IT](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-diffusiongemma-26b-a4b-it.md)\n- [Darwin-36B-Opus vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-12b-it.md)\n- [Darwin-36B-Opus vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-26b-a4b-it.md)\n- [Darwin-36B-Opus vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-31b-it.md)\n- [Darwin-36B-Opus vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-e2b-it.md)\n- [Darwin-36B-Opus vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-e4b-it.md)\n- [Darwin-36B-Opus vs GLM-4.5-Air](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-4-5-air.md)\n- [Darwin-36B-Opus vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-4-7-flash.md)\n- [Darwin-36B-Opus vs GLM-5.3](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-5-3.md)\n- [Darwin-36B-Opus vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-5-3-flash.md)\n- [Darwin-36B-Opus vs gpt-oss-120b](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gpt-oss-120b.md)\n- [Darwin-36B-Opus vs gpt-oss-20b](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gpt-oss-20b.md)\n- [Darwin-36B-Opus vs Granite 4.2 3B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-granite-4-2-3b.md)\n- [Darwin-36B-Opus vs Hy3](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-hy3.md)\n- [Darwin-36B-Opus vs Inkling](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-inkling.md)\n- [Darwin-36B-Opus vs Inkling-Small](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-inkling-small.md)\n- [Darwin-36B-Opus vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Darwin-36B-Opus vs Kimi K3](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-kimi-k3.md)\n- [Darwin-36B-Opus vs Kolibri-1](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-kolibri-1.md)\n- [Darwin-36B-Opus vs Laguna M.1](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-laguna-m-1.md)\n- [Darwin-36B-Opus vs Laguna S 2.1](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-laguna-s-2-1.md)\n- [Darwin-36B-Opus vs Laguna XS 2.1](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-laguna-xs-2-1.md)\n- [Darwin-36B-Opus vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm2-5-2-6b.md)\n- [Darwin-36B-Opus vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ling-3-0-tiny.md)\n- [Darwin-36B-Opus vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Darwin-36B-Opus vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minicpm-o-4-5.md)\n- [Darwin-36B-Opus vs MiniCPM5-2B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minicpm5-2b.md)\n- [Darwin-36B-Opus vs MiniMax-M2.5](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minimax-m2-5.md)\n- [Darwin-36B-Opus vs MiniMax-M2.7](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minimax-m2-7.md)\n- [Darwin-36B-Opus vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-muse-glimmer-30b.md)\n- [Darwin-36B-Opus vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Darwin-36B-Opus vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-nano-30b-a3b.md)\n- [Darwin-36B-Opus vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-nano-4b.md)\n- [Darwin-36B-Opus vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Darwin-36B-Opus vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-super-120b-a12b.md)\n- [Darwin-36B-Opus vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-ultra-550b-a55b.md)\n- [Darwin-36B-Opus vs occamy-1.0](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-occamy-1-0.md)\n- [Darwin-36B-Opus vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-olmo-3-7b-think.md)\n- [Darwin-36B-Opus vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-35b-a3b.md)\n- [Darwin-36B-Opus vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-397b.md)\n- [Darwin-36B-Opus vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-9b.md)\n- [Darwin-36B-Opus vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Darwin-36B-Opus vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-4b-instruct-2507.md)\n- [Darwin-36B-Opus vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-0-8b.md)\n- [Darwin-36B-Opus vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-122b-a10b.md)\n- [Darwin-36B-Opus vs Qwen3.5-2B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-2b.md)\n- [Darwin-36B-Opus vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-397b-a17b.md)\n- [Darwin-36B-Opus vs Qwen3.5-4B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-4b.md)\n- [Darwin-36B-Opus vs Qwen3.5-9B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-9b.md)\n- [Darwin-36B-Opus vs Qwen3.6-27B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-6-27b.md)\n- [Darwin-36B-Opus vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-6-35b-a3b.md)\n- [Darwin-36B-Opus vs Qwen3.8-27B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-8-27b.md)\n- [Darwin-36B-Opus vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-8-flash-next.md)\n- [Darwin-36B-Opus vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Darwin-36B-Opus vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-coder-next.md)\n- [Darwin-36B-Opus vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Darwin-36B-Opus vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-vl-2b-instruct.md)\n- [Darwin-36B-Opus vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-vl-30b-a3b-instruct.md)\n- [Darwin-36B-Opus vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-vl-32b-instruct.md)\n- [Darwin-36B-Opus vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-vl-4b-instruct.md)\n- [Darwin-36B-Opus vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-vl-8b-instruct.md)\n- [Darwin-36B-Opus vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwopus3-8-27b-flash-v2.md)\n- [Darwin-36B-Opus vs SmolLM3-3B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-smollm3-3b.md)\n- [Darwin-36B-Opus vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-spark-x2-6-4b.md)\n- [Darwin-36B-Opus vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-swift-1-5-qwen3-8-27b.md)\n- [Darwin-36B-Opus vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Darwin-36B-Opus vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-thinkingcap-qwen3-8-27b.md)\n- [DeepSeek-V3.2 vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-deepseek-v4-1-flash.md)\n- [DeepSeek V4.1 Flash vs DeepSeek-V4-Flash-0731](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-deepseek-v4-flash-0731.md)\n- [DeepSeek V4.1 Flash vs DiffusionGemma 26B A4B IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-diffusiongemma-26b-a4b-it.md)\n- [DeepSeek V4.1 Flash vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-12b-it.md)\n- [DeepSeek V4.1 Flash vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-26b-a4b-it.md)\n- [DeepSeek V4.1 Flash vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-31b-it.md)\n- [DeepSeek V4.1 Flash vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-e2b-it.md)\n- [DeepSeek V4.1 Flash vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-e4b-it.md)\n- [DeepSeek V4.1 Flash vs GLM-4.5-Air](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-4-5-air.md)\n- [DeepSeek V4.1 Flash vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-4-7-flash.md)\n- [DeepSeek V4.1 Flash vs GLM-5.3](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-5-3.md)\n- [DeepSeek V4.1 Flash vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-5-3-flash.md)\n- [DeepSeek V4.1 Flash vs gpt-oss-120b](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gpt-oss-120b.md)\n- [DeepSeek V4.1 Flash vs gpt-oss-20b](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gpt-oss-20b.md)\n- [DeepSeek V4.1 Flash vs Granite 4.2 3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-granite-4-2-3b.md)\n- [DeepSeek V4.1 Flash vs Hy3](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-hy3.md)\n- [DeepSeek V4.1 Flash vs Inkling](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-inkling.md)\n- [DeepSeek V4.1 Flash vs Inkling-Small](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-inkling-small.md)\n- [DeepSeek V4.1 Flash vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [DeepSeek V4.1 Flash vs Kimi K3](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kimi-k3.md)\n- [DeepSeek V4.1 Flash vs Kolibri-1](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kolibri-1.md)\n- [DeepSeek V4.1 Flash vs Laguna M.1](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-m-1.md)\n- [DeepSeek V4.1 Flash vs Laguna S 2.1](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-s-2-1.md)\n- [DeepSeek V4.1 Flash vs Laguna XS 2.1](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-xs-2-1.md)\n- [DeepSeek V4.1 Flash vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm2-5-2-6b.md)\n- [DeepSeek V4.1 Flash vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ling-3-0-tiny.md)\n- [DeepSeek V4.1 Flash vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-mimo-v2-6-distill-qwen-9b.md)\n- [DeepSeek V4.1 Flash vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm-o-4-5.md)\n- [DeepSeek V4.1 Flash vs MiniCPM5-2B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm5-2b.md)\n- [DeepSeek V4.1 Flash vs MiniMax-M2.5](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minimax-m2-5.md)\n- [DeepSeek V4.1 Flash vs MiniMax-M2.7](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minimax-m2-7.md)\n- [DeepSeek V4.1 Flash vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-muse-glimmer-30b.md)\n- [DeepSeek V4.1 Flash vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [DeepSeek V4.1 Flash vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-30b-a3b.md)\n- [DeepSeek V4.1 Flash vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-4b.md)\n- [DeepSeek V4.1 Flash vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [DeepSeek V4.1 Flash vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-super-120b-a12b.md)\n- [DeepSeek V4.1 Flash vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-ultra-550b-a55b.md)\n- [DeepSeek V4.1 Flash vs occamy-1.0](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-occamy-1-0.md)\n- [DeepSeek V4.1 Flash vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-olmo-3-7b-think.md)\n- [DeepSeek V4.1 Flash vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-35b-a3b.md)\n- [DeepSeek V4.1 Flash vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-397b.md)\n- [DeepSeek V4.1 Flash vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-9b.md)\n- [DeepSeek V4.1 Flash vs 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