{ "data": { "a": { "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": 62.8, "grade": "B", "agentReady": false, "rank": 472, "ranked": true, "rankOf": 1184, "categoryRank": 3, "methodology": "0.5", "run": "2026-10-01", "scores": { "ergonomics": 70, "maintenance": 70, "payments": 60, "reliability": 72, "schema": 69, "security": 35, "transparency": 69 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-20" }, "negative": 0, "verdict": "DeepSeek V4.1 Flash gives a 1M-token context, trained tool calls and an effort budget from 1 to 100 under the MIT licence, with every release kept in its own repository. It needs 510 GB of files and runs in vLLM or mlx-lm, prompts come from DeepSeek's code rather than a template, and no safety results or training sources are published.", "bestFor": "Teams with a multi-GPU server who want DeepSeek's agent model under MIT with the API as a fallback.", "strengths": [ "MIT licence for the weights with no acceptable 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 with test cases, and DeepSeek's deepseek-recipe library parses it", "Each release kept in its own repository, and the 17 October update dated and described", "Safetensors only, with model code in vLLM and mlx-lm" ], "weaknesses": [ "510.3 GB of files, with no memory needs stated by DeepSeek", "No Jinja template in the 17 October repository, so prompts are built by running DeepSeek's encoding code", "Not in Transformers 5.20.0 or llama.cpp, and Ollama serves it from its cloud only", "No safety evaluation, training sources or knowledge cut-off published", "None of the 34 discussions listed for the September repository had a reply from DeepSeek" ], "agentNotes": [ "Build prompts with encoding.py or deepseek-recipe; the September repository's Jinja template is behind the 16 October encoding changes per an open pull request", "Parse tool calls from the DSML `calls` block, which V4.1 writes with a space before `calls`, `invoke` and `parameter`, unlike V4", "Pass reasoning effort as a number from 1 to 100, or low, high or max (50, 75, 100), in thinking mode", "Allow 256K or more output tokens at high effort, as the card recommends", "Use DeepSeek's hosted API (the deepseek-api listing) if you can't hold 510 GB of weights" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.5", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 62.8 } ], "editorialScores": { "ergonomics": 70, "maintenance": 70, "payments": 60, "reliability": 72, "schema": 69, "security": 35, "transparency": 65 }, "provenanceScore": 73 }, "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" ], "sameModel": [ "deepseek-api" ], "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." ], "score": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash.json", "live": { "slug": "deepseek-v4-1-flash", "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": "2026-10-21T00:50:03.414291506Z" } }, "answer": "Qwen3.8-27B and DeepSeek V4.1 Flash score within a point of each other on agent readiness, 63.7 (B) and 62.8 (B). DeepSeek V4.1 Flash leads on schema \u0026 documentation, maintenance \u0026 community and transparency \u0026 trust.", "b": { "slug": "qwen3-8-27b", "name": "Qwen3.8-27B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3.8-27B is Alibaba's open-weight dense vision-language model of 27B parameters, with thinking on by default and a 262,144-token context, published on Hugging Face in BF16 and FP8 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-8-27b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-8-27b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-8-27b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-8-27b.json", "repo": "https://huggingface.co/Qwen/Qwen3.8-27B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3.8-27B" }, { "registry": "huggingface", "name": "Qwen/Qwen3.8-27B-FP8" }, { "registry": "ollama", "name": "qwen3.8" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-20).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. The card says a hosted version on Qwen Cloud is coming soon. Running it yourself costs the hardware, 55.6 GB for the BF16 files and 18 GB for Ollama's default build (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/Qwen/Qwen3.8-27B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "reasoning", "tool-calling", "apache-2.0", "safetensors", "fp8", "self-hosted" ], "lastRelease": "2026-08-14", "graded": true, "anchor": { "graded": true, "score": 63.7, "grade": "B", "agentReady": false, "rank": 428, "ranked": true, "rankOf": 1184, "categoryRank": 2, "methodology": "0.5", "run": "2026-10-01", "scores": { "ergonomics": 85, "maintenance": 25, "payments": 60, "reliability": 90, "schema": 63, "security": 45, "transparency": 47 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-20" }, "negative": 0, "verdict": "Qwen3.8-27B runs nearly anywhere, in vLLM, SGLang, llama.cpp, Ollama and MLX, with an 18 GB Ollama build and Apache 2.0 terms. The card leaves out tool calling, hardware needs, training data, limitations and safety results, the files haven't changed since 14 August, and Alibaba answered none of the 40 most recent discussions.", "bestFor": "People who want a capable open model on one 24 to 32 GB GPU or Mac with the widest runtime support and a plain licence.", "strengths": [ "Model code in vLLM, SGLang, mlx-lm and Transformers 5.20.0, the architecture in llama.cpp and a local entry in Ollama's library", "Apache 2.0 weights with no acceptable use policy, downloadable with no account", "Ollama's default build is 18 GB and Alibaba's FP8 repository 30.9 GB", "262,144 tokens of native context, thinking that can be turned off per request and three reasoning effort levels", "Safetensors only, with a chat template in the repository" ], "weaknesses": [ "The card doesn't describe tool calling, so the parser to use comes from vLLM's and SGLang's recipes", "No training data, limitations, languages, knowledge cut-off or safety results on the card", "No memory needs stated by Alibaba", "None of the 40 most recent discussions had a reply from a Qwen member, including reports about the chat template", "The files and card haven't changed since 14 August 2026" ], "agentNotes": [ "Serve it with `--tool-call-parser qwen3_coder` and `--reasoning-parser qwen3`; the Hermes parser leaves its tool calls unparsed, per SGLang's cookbook", "Set `enable_thinking: false` in `chat_template_kwargs` for short tasks, since thinking is on by default", "Use the sampling the card gives for each mode, `temperature=1.0, top_p=0.95` thinking and `temperature=0.7, top_p=0.8, presence_penalty=1.5` without", "Turn on YaRN only for contexts beyond 262,144 tokens; the card says static YaRN can cost quality on short texts", "Check JSON output yourself, since structured output isn't documented for the model" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.5", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 63.7 } ], "editorialScores": { "ergonomics": 85, "maintenance": 25, "payments": 60, "reliability": 90, "schema": 63, "security": 45, "transparency": 45 }, "provenanceScore": 49 }, "connect": { "install": "ollama run qwen3.8\n# or, with vLLM's recipe:\nvllm serve Qwen/Qwen3.8-27B-FP8 --max-model-len 262144 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"qwen3.8\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen3-8-27b" }, "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": "2026-10-20", "notes": [ "The LICENSE file reads Copyright 2026 Alibaba Cloud. RDAP names Alibaba Cloud (Singapore) Private Limited as the registrant of qwen.ai.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog. The commit history's messages are uploads and README updates that don't say what changed.", "qwen.ai answers every path, /.well-known/security.txt and /robots.txt included, with its web app, so there is no security.txt." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-8-27b.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": "2026-10-17", "b": "2026-08-14", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "2.8/5 (4)", "b": "3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Qwen3.8-27B and DeepSeek V4.1 Flash score within a point of each other on agent readiness, 63.7 (B) and 62.8 (B). DeepSeek V4.1 Flash leads on schema \u0026 documentation, maintenance \u0026 community and transparency \u0026 trust.", "question": "Which is better for AI agents, DeepSeek V4.1 Flash or Qwen3.8-27B?" } ], "goodFor": [ { "aheadOn": [ "Schema \u0026 documentation, 69 against 63", "Maintenance \u0026 community, 70 against 25", "Transparency \u0026 trust, 69 against 47" ], "also": null, "goodFor": "Teams with a multi-GPU server who want DeepSeek's agent model under MIT with the API as a fallback.", "slug": "deepseek-v4-1-flash", "watchFor": "510.3 GB of files, with no memory needs stated by DeepSeek" }, { "aheadOn": [ "Reliability, 90 against 72", "Agent ergonomics, 85 against 70", "Security \u0026 auth, 45 against 35" ], "also": null, "goodFor": "People who want a capable open model on one 24 to 32 GB GPU or Mac with the widest runtime support and a plain licence.", "slug": "qwen3-8-27b", "watchFor": "The card doesn't describe tool calling, so the parser to use comes from vLLM's and SGLang's recipes" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kolibri-1.json", "title": "DeepSeek V4.1 Flash vs Kolibri-1", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kolibri-1" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-8-27b.json", "title": "Kolibri-1 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-8-27b" } ], "scores": [ { "by": 18, "deepseek-v4-1-flash": 72, "edge": "qwen3-8-27b", "key": "reliability", "name": "Reliability", "qwen3-8-27b": 90, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 6, "deepseek-v4-1-flash": 69, "edge": "deepseek-v4-1-flash", "key": "schema", "name": "Schema \u0026 documentation", "qwen3-8-27b": 63, "weight": 13 }, { "by": 15, "deepseek-v4-1-flash": 70, "edge": "qwen3-8-27b", "key": "ergonomics", "name": "Agent ergonomics", "qwen3-8-27b": 85, "weight": 13 }, { "by": 10, "deepseek-v4-1-flash": 35, "edge": "qwen3-8-27b", "key": "security", "name": "Security \u0026 auth", "qwen3-8-27b": 45, "weight": 14 }, { "by": 0, "deepseek-v4-1-flash": 60, "edge": "", "key": "payments", "name": "Payments \u0026 pricing", "qwen3-8-27b": 60, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 45, "deepseek-v4-1-flash": 70, "edge": "deepseek-v4-1-flash", "key": "maintenance", "name": "Maintenance \u0026 community", "qwen3-8-27b": 25, "weight": 7 }, { "by": 22, "deepseek-v4-1-flash": 69, "edge": "deepseek-v4-1-flash", "key": "transparency", "name": "Transparency \u0026 trust", "qwen3-8-27b": 47, "weight": 7 } ], "summary": "Qwen3.8-27B and DeepSeek V4.1 Flash score within a point of each other on agent readiness, 63.7 (B) and 62.8 (B). DeepSeek V4.1 Flash leads on schema \u0026 documentation, maintenance \u0026 community and transparency \u0026 trust. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "deepseek-v4-1-flash": "DeepSeek V4.1 Flash gives a 1M-token context, trained tool calls and an effort budget from 1 to 100 under the MIT licence, with every release kept in its own repository. It needs 510 GB of files and runs in vLLM or mlx-lm, prompts come from DeepSeek's code rather than a template, and no safety results or training sources are published.", "qwen3-8-27b": "Qwen3.8-27B runs nearly anywhere, in vLLM, SGLang, llama.cpp, Ollama and MLX, with an 18 GB Ollama build and Apache 2.0 terms. The card leaves out tool calling, hardware needs, training data, limitations and safety results, the files haven't changed since 14 August, and Alibaba answered none of the 40 most recent discussions." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-27b", "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-27b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-27b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-27b.min.md" }, "markdown": "Qwen3.8-27B and DeepSeek V4.1 Flash score within a point of each other on agent readiness, 63.7 (B) and 62.8 (B). DeepSeek V4.1 Flash leads on schema \u0026 documentation, maintenance \u0026 community and transparency \u0026 trust. Both do open-weight models you run yourself.\n\n- DeepSeek V4.1 Flash: grade B, 62.8/100, rank #472 of 1184. 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- Qwen3.8-27B: grade B, 63.7/100, rank #428 of 1184. Markdown https://www.anchorterminal.com/tools/qwen3-8-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-8-27b.json\n- All 3 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### DeepSeek V4.1 Flash (B)\n\nGood for: Teams with a multi-GPU server who want DeepSeek's agent model under MIT with the API as a fallback.\n\nAhead on:\n- Schema \u0026 documentation, 69 against 63\n- Maintenance \u0026 community, 70 against 25\n- Transparency \u0026 trust, 69 against 47\n\nWatch for: 510.3 GB of files, with no memory needs stated by DeepSeek\n\n### Qwen3.8-27B (B)\n\nGood for: People who want a capable open model on one 24 to 32 GB GPU or Mac with the widest runtime support and a plain licence.\n\nAhead on:\n- Reliability, 90 against 72\n- Agent ergonomics, 85 against 70\n- Security \u0026 auth, 45 against 35\n\nWatch for: The card doesn't describe tool calling, so the parser to use comes from vLLM's and SGLang's recipes\n\n\n## Score by category\n\n| Category | Weight | DeepSeek V4.1 Flash | Qwen3.8-27B | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 72 | 90 | Qwen3.8-27B +18 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 69 | 63 | DeepSeek V4.1 Flash +6 |\n| Agent ergonomics | 13% (16.2 this run) | 70 | 85 | Qwen3.8-27B +15 |\n| Security \u0026 auth | 14% (17.5 this run) | 35 | 45 | Qwen3.8-27B +10 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 60 | 60 | even |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 70 | 25 | DeepSeek V4.1 Flash +45 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 69 | 47 | DeepSeek V4.1 Flash +22 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **62.8 · B** | **63.7 · B** | |\n\n## Facts side by side\n\n| Fact | DeepSeek V4.1 Flash | Qwen3.8-27B |\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 | 2026-10-17 | 2026-08-14 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 2.8/5 (4) | 3/5 (4) |\n\n## Verdicts\n\n**DeepSeek V4.1 Flash.** DeepSeek V4.1 Flash gives a 1M-token context, trained tool calls and an effort budget from 1 to 100 under the MIT licence, with every release kept in its own repository. It needs 510 GB of files and runs in vLLM or mlx-lm, prompts come from DeepSeek's code rather than a template, and no safety results or training sources are published.\n\n**Qwen3.8-27B.** Qwen3.8-27B runs nearly anywhere, in vLLM, SGLang, llama.cpp, Ollama and MLX, with an 18 GB Ollama build and Apache 2.0 terms. The card leaves out tool calling, hardware needs, training data, limitations and safety results, the files haven't changed since 14 August, and Alibaba answered none of the 40 most recent discussions.\n\n## Before you call either\n\n### DeepSeek V4.1 Flash\n\n1. Build prompts with encoding.py or deepseek-recipe; the September repository's Jinja template is behind the 16 October encoding changes per an open pull request\n2. Parse tool calls from the DSML `calls` block, which V4.1 writes with a space before `calls`, `invoke` and `parameter`, unlike V4\n3. Pass reasoning effort as a number from 1 to 100, or low, high or max (50, 75, 100), in thinking mode\n4. Allow 256K or more output tokens at high effort, as the card recommends\n5. Use DeepSeek's hosted API (the deepseek-api listing) if you can't hold 510 GB of weights\n\n### Qwen3.8-27B\n\n1. Serve it with `--tool-call-parser qwen3_coder` and `--reasoning-parser qwen3`; the Hermes parser leaves its tool calls unparsed, per SGLang's cookbook\n2. Set `enable_thinking: false` in `chat_template_kwargs` for short tasks, since thinking is on by default\n3. Use the sampling the card gives for each mode, `temperature=1.0, top_p=0.95` thinking and `temperature=0.7, top_p=0.8, presence_penalty=1.5` without\n4. Turn on YaRN only for contexts beyond 262,144 tokens; the card says static YaRN can cost quality on short texts\n5. Check JSON output yourself, since structured output isn't documented for the model\n\n## Questions\n\n### Which is better for AI agents, DeepSeek V4.1 Flash or Qwen3.8-27B?\n\nQwen3.8-27B and DeepSeek V4.1 Flash score within a point of each other on agent readiness, 63.7 (B) and 62.8 (B). DeepSeek V4.1 Flash leads on schema \u0026 documentation, maintenance \u0026 community and transparency \u0026 trust.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-27b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"deepseek-v4-1-flash\", \"b\": \"qwen3-8-27b\"}`. From a terminal: `anchor compare deepseek-v4-1-flash qwen3-8-27b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/deepseek-v4-1-flash.json and https://www.anchorterminal.com/api/v1/tools/qwen3-8-27b.json\n\n## Other comparisons with DeepSeek V4.1 Flash or Qwen3.8-27B\n\n- [DeepSeek V4.1 Flash vs Kolibri-1](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kolibri-1.md)\n- [Kolibri-1 vs Qwen3.8-27B](https://www.anchorterminal.com/compare/kolibri-1-vs-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": "0.5", "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 V4.1 Flash vs Qwen3.8-27B", "url": "" } ], "description": "Qwen3.8-27B and DeepSeek V4.1 Flash score within a point of each other for open-weight models you run yourself, 63.7 and 62.8 out of 100. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "DeepSeek V4.1 Flash B 62.8", "Qwen3.8-27B B 63.7", "scores" ], "h1": "DeepSeek V4.1 Flash vs Qwen3.8-27B", "image": "https://www.anchorterminal.com/assets/og/compare-deepseek-v4-1-flash-vs-qwen3-8-27b.png", "path": "/compare/deepseek-v4-1-flash-vs-qwen3-8-27b", "published": "2026-10-01", "section": "tools", "title": "DeepSeek V4.1 Flash vs Qwen3.8-27B for AI agents (2026)", "toc": null, "updated": "2026-10-21", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-27b" }, "tokens": { "markdown": 1750, "slim": 530 }, "version": 1 }